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Uber and Lyft Drivers Sue for New York Unemployment Benefits

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Uber and Lyft Drivers Sue for New York Unemployment Benefits

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It turns out as Uber workers retire or quit work, they do not receive any benefits. Since they are classified as gig workers, it was very difficult for them to receive many benefits. The specific man in the article, Mr. Ouattara, who moved Africa here, worked as an Uber driver, but was unable to anymore after the pandemic hit. He has no more savings left, and it is getting very difficult for him day-by-day.

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In the latest challenge over their labor status, gig workers say the state is illegally failing to pay them jobless benefits in a timely way.

A group of Uber and Lyft drivers in New York say they have not been able to collect state unemployment benefits in a timely way. Credit... Jeenah Moon for The New York Times

Doh Ouattara drove for Uber and Lyft from 2016 until mid-March of this year, when he became concerned about the pandemic. With three children under 6 to provide for, he decided to apply for unemployment benefits.

But despite hundreds of calls to the New York State Department of Labor and two prominent rulings that deemed gig workers like him to be employees eligible for the state's unemployment insurance, he has yet to receive any payment, and time is running short.

Mr. Ouattara, who was trained as an accountant in the Ivory Coast before moving to the United States, could afford to pay only half his rent in April and none of it this month. "My savings are almost gone - I've used them for food, basic necessities," he said in an interview. "It is getting very, very stressful."

On Monday, Mr. Ouattara and three other Uber and Lyft drivers, along with an advocacy group called the New York Taxi Workers Alliance, filed a complaint in federal court against Gov. Andrew M. Cuomo and the state's Department of Labor, saying drivers were not paid standard benefits to which they were entitled in a timely way.

The action comes at a time when drivers have been increasingly vocal in demanding the protections afforded to employees, which states like New York and California have granted them to varying degrees, even as the companies continue to maintain that drivers are contractors.

The lawsuit says drivers must wait months to receive standard unemployment benefits, if they receive them at all, compared with the two to three weeks that the state has said is typical for other workers. The plaintiffs are seeking an injunction requiring the state to immediately pay their benefits and the benefits of other drivers to whom they are owed.

"The issue of Uber driver employment status has been settled for over a year by the state's own decision," said Zubin Soleimany, a lawyer for the taxi workers group. "But it hasn't been able to provide people benefits when they need them. It's been a catastrophe for these guys."

Jack Sterne, a spokesman for the Cuomo administration, said, "During this pandemic emergency, we have been moving heaven and earth to get every single unemployed New Yorker their benefits as quickly as possible - including Uber and Lyft drivers, who are treated no different than any other worker."

According to the lawsuit, a key problem is that the state has not forced companies like Uber and Lyft to provide the data on workers' earnings that employers must typically supply. Lacking such data, the suit says, the state has required drivers to complete a lengthy application process that involves more steps and paperwork than other workers face to receive standard unemployment benefits.

Josh Gold, an Uber spokesman, said the company had provided the state with the earnings data it had requested, though he declined to elaborate on whether the data would be sufficient to calculate unemployment benefits promptly. The company said in April that it had agreed to comply with a state request for earnings data, but that it had not yet done so.

Lyft said the company was working with the state to provide access to earnings data. "The special interests behind this lawsuit aren't interested in what's best for drivers, since filing this lawsuit will do nothing to help them get assistance quickly," said Julie Wood, a Lyft spokeswoman.

Uber and Lyft have encouraged drivers to apply for benefits under the Pandemic Unemployment Assistance program, which Congress passed in March to replace income for out-of-work contractors and other workers who might fall through the gaps in the safety net.

Under federal rules, only those ineligible for traditional unemployment benefits are supposed to receive pandemic assistance. But even though New York and California consider many drivers to be employees eligible for traditional benefits, the states are helping drivers obtain the pandemic assistance, arguing that it is often the fastest way for them to receive financial support.

New York State introduced a single benefits application on April 20 that routes workers into traditional unemployment benefits or pandemic assistance, depending on which program the state considers them eligible for.

Lyft said that pandemic assistance was a better deal for many part-time drivers, because the minimum payment under that program was higher than the minimum unemployment benefit.

But for full-time drivers, pandemic assistance can be a weak substitute for traditional unemployment benefits. The complaint calculates that Mr. Ouattara's benefit under the pandemic assistance program would be less than $250 a week, compared with $504 that he would receive in unemployment benefits. (He and other drivers would receive a $600-a-week federal supplement on top of either program through the end of July.)

The difference arises because pandemic assistance is based on income net of expenses like gas and maintenance, whereas unemployment benefits are based on gross earnings - or about $26,000 versus about $55,500 for Mr. Ouattara in 2019. Other drivers face similar disparities, according to the complaint.

Mr. Ouattara said he received a notice from the state this month indicating it had no record of earnings for him from Uber or Lyft. After he sent the state his documentation, he said, it urged him to apply for pandemic assistance. He did so, but continues to pursue traditional unemployment benefits as well. A second plaintiff in the case received a notice last week saying he was approved for pandemic assistance, but he continues to press for conventional unemployment benefits.

Other drivers who believe they are employees have sought traditional unemployment benefits rather than pandemic assistance as well, but have encountered problems similar to those of Mr. Ouattara and his fellow plaintiffs.

Carole Vigne, a staff attorney for the nonprofit Legal Aid at Work, said she had represented Uber and Lyft drivers during the pandemic who received traditional unemployment benefits in California in about six weeks. But she said that some of her clients there were still waiting for benefits they applied for more than two months ago, and that some had been routed to the pandemic assistance program with no explanation, despite intending to apply for traditional benefits.

Crystal Page, a spokeswoman for California's labor agency, said in an email, "There are a number of different scenarios that apply to ALL benefit claims which could determine how quickly a claim can get processed and paid if the individual is eligible."

The New York lawsuit, brought with the help of the nonprofit group Legal Services NYC, cites two state decisions that found drivers eligible for unemployment benefits. The first was a 2018 ruling of the state's unemployment insurance appeal board, the final authority on eligibility questions in the executive branch, which found that three Uber drivers and all "similarly situated" drivers were eligible for benefits.

The second was a ruling involving Postmates, an app-based delivery service, which found the company's workers to be employees for purposes of unemployment benefits. The ruling did not apply to Uber and Lyft directly, but strongly suggested employee status for their drivers given the similarity of their business models to Postmates'.

The lawsuit by Mr. Soleimany's group argues that by failing to pay standard unemployment benefits to drivers in a timely way, the state is violating the "when due" provision of the federal Social Security Act, which requires states to ensure the full payment of benefits "with the greatest promptness that is administratively feasible."

The suit further argues that the state is violating the equal protection clause of the U.S. Constitution by typically paying employees who are not drivers their standard benefits on a two-to-three-week timetable, but taking months to pay app-based drivers.

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Uber and Lyft Drivers Sue for New York Unemployment Benefits

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In the latest challenge over their labor status, gig workers say the state is illegally failing to pay them jobless benefits in a timely way. Doh Ouattara drove for Uber and Lyft from 2016 until mid-March of this year, when he became concerned about the pandemic.

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California Sues Uber and Lyft, Claiming Workers Are Misclassified

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California Sues Uber and Lyft, Claiming Workers Are Misclassified

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Through the article I found out more about Uber's unhealthy business practices, and how it treats it's employees as gig workers. This is not good, and that is why California is suing them.

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The ride-hailing companies are accused of defying a new state law that says gig workers should be treated as employees.

At least one million gig workers in California are affected by the law that the state says Uber and Lyft are violating. Credit... Jason Henry for The New York Times

OAKLAND, Calif. - California's attorney general and a coalition of city attorneys in the state sued Uber and Lyft on Tuesday, claiming the companies wrongfully classified their drivers as independent contractors in violation of a state law that makes them employees.

The law, known as Assembly Bill 5, requires companies to treat their workers as employees instead of contractors if they control how workers perform tasks or if the work is a routine part of a company's business.

At least one million gig workers in the state are affected by the law, which is supposed to give them a path to benefits like a minimum wage and unemployment insurance that have been traditionally withheld from independent contractors.

Although A.B. 5 took effect on Jan. 1, Uber, Lyft and other gig economy companies that operate in California have resisted and are not taking steps to reclassify their drivers. Uber, Lyft and DoorDash have poured $90 million into a campaign for a ballot initiative that would exempt them from complying with the law. Uber has also argued that its core business is technology, not rides, and therefore drivers are not a key part of its business.

The lawsuit also claims the ride-hailing companies are engaging in an unfair business practice that harms other California companies that follow the law. By avoiding payroll taxes and not paying minimum wage, Uber and Lyft are able to provide rides at "an artificially low cost," the suit claims, giving them a competitive advantage over other businesses. The suit seeks civil penalties and back wages for workers that could add up to hundreds of millions of dollars.

"California has ground rules with rights and protections for workers and their employers. We intend to make sure that Uber or Lyft play by the rules," Xavier Becerra, California's attorney general, said in a statement. The city attorneys of San Francisco, Los Angeles and San Diego joined in the lawsuit.

California's move is a significant threat to the gig companies and could influence other states with similar laws to take action against them, labor experts said.

"Uber and Lyft have lived a kind of charmed life in terms of escaping law enforcement generally, and particularly with regard to employment law," said William B. Gould IV, a law professor at Stanford University and the former chairman of the National Labor Relations Board. "The attorney general's action can't help but have a positive influence on law enforcement generally against them."

Although Uber and Lyft have argued that their drivers have independence and decide when to work, the lawsuit claims that both ride-hailing companies exert enough control over drivers to make them employees. In remarks after the lawsuit was filed, Gov. Gavin Newsom of California said he would seek funding for enforcement of A.B. 5 in the state budget and that California had a responsibility to enforce the law.

"Uber and Lyft are traditional employers of these misclassified employees. They hire and fire them. They control which drivers have access to which possible assignments," the lawsuit says. "Uber and Lyft are transportation companies in the business of selling rides to customers, and their drivers are the employees who provide the rides they sell."

Because ride-hailing companies and app-based food delivery services do not employ drivers, they avoid the costs of insurance and vehicle maintenance, sick leave and unemployment. But the coronavirus pandemic has exposed gaps in the gig economy, as drivers have abruptly lost their income and struggled to get unemployment insurance, or fallen sick without access to paid sick leave.

"Uber and Lyft are breaking the law. We are going to put a stop to it," said Dennis Herrera, the city attorney of San Francisco. "This pandemic just highlights the danger of the work these essential workers are doing."

Lyft said in a statement that it was "looking forward" to working with the attorney general and mayors "to bring all the benefits of California's innovation economy to as many workers as possible, especially during this time when the creation of good jobs with access to affordable health care and other benefits is more important than ever."

Gig companies have responded to the outbreak by offering limited quarantine pay to drivers who receive a positive coronavirus diagnosis or a doctor's recommendation to isolate themselves. The companies have also distributed hand sanitizer and other cleaning supplies to drivers.

Uber has worried that providing those things could expose it to misclassification claims from workers, and the company has asked lawmakers to shield it from lawsuits over how its drivers are classified if it provides the drivers with medical supplies or compensation. Its chief executive, Dara Khosrowshahi, wrote a letter to President Trump recently asking for a new classification for drivers that would make them neither employees nor contractors.

Mr. Khosrowshahi has called for a so-called third way of classifying workers, which would provide some health benefits to drivers without making them employees who could receive full employment benefits.

The lawsuit criticized Uber and Lyft's lobbying efforts, including a ballot initiative that would exempt the companies from complying with A.B. 5. "Amid a once-in-a-century pandemic, they have gone to extraordinary lengths to convince the public that their unlawful misclassification scheme is in the public interest," the suit said.

Uber said on Tuesday it would press forward with its ballot initiative. "We will contest this action in court, while at the same time pushing to raise the standard of independent work for drivers in California," said Matt Wing, an Uber spokesman.

The lawsuit comes at a fraught moment for Uber and Lyft, as the businesses struggle to adapt to the sudden decline in demand caused by the pandemic. Consumer data suggests that spending on ride-hailing has dropped as much as 83 percent. Lyft is expected to report its first-quarter earnings on Wednesday, while Uber reports on Thursday.

Before the pandemic, Uber and Lyft were racing to become profitable after their 2019 initial public offerings stumbled.

But as demand has declined, both companies have tried to cut costs. Last week, Lyft laid off 17 percent of its work force, furloughed 5 percent, and cut pay for executives and remaining staff members. Uber is also considering layoffs and on Monday announced the closing of its food delivery business in Egypt, Honduras, Saudi Arabia and several other markets where it was deeply unprofitable.

The stock prices of both companies declined on news of the lawsuit. Uber's shares fell about 1 percent and Lyft's fell 4 percent.

Still, Uber and Lyft have reported that they have substantial cash reserves to weather the downturn caused by the pandemic. Uber said it had more than $8 billion, while Lyft said it had more than $2 billion.

"In what world can you not pay your fair share?" said Lorena Gonzalez, a California Assembly member who represents southern San Diego and drafted A.B. 5.

The city and state attorneys involved in the suit said they believed it would succeed despite Uber's ballot measure. "They are not going to succeed in any event. The voters are too smart for that," said Mike Feuer, the city attorney of Los Angeles.

Noam Scheiber contributed reporting from Evanston, Ill.

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California Sues Uber and Lyft, Claiming Workers Are Misclassified

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The ride-hailing companies are accused of defying a new state law that says gig workers should be treated as employees. OAKLAND, Calif. - California's attorney general and a coalition of city attorneys in the state sued Uber and Lyft on Tuesday, claiming the companies wrongfully classified their drivers as independent contractors in violation of a state law that makes them employees.

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Uber Settles Data Breach Investigation for $148 Million

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Uber Settles Data Breach Investigation for $148 Million

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I found this article on the NY Times, and although it sounds similar to The Independent's article on something similar, it focuses more on the business aspect, and how it affects Americans.

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SAN FRANCISCO - Uber will pay $148 million to settle a nationwide investigation into a 2016 data breach, in which a hacker managed to gain access to information belonging to 57 million riders and drivers. The breach included names and driver's license numbers for 600,000 drivers.

The investigation, led by state attorneys general across the United States, focused on whether Uber had violated data breach notification laws by not informing consumers that their information had been compromised.

Rather than disclosing the breach when it occurred, Uber paid the hacker $100,000 through its bug bounty program, which financially rewards hackers for discovering and disclosing software flaws. The ride-hailing company persuaded him to delete the data and stay quiet about it with a nondisclosure agreement.

The incident became public a year later when Uber's chief executive, Dara Khosrowshahi, announced it as a "failure" and fired the two employees who had signed off on the payment.

"Uber's decision to cover up this breach was a blatant violation of the public's trust," Xavier Becerra, California's attorney general, said in a statement. "The company failed to safeguard user data and notify authorities when it was exposed."

Tony West, Uber's chief legal officer, said the settlement was part of a larger effort inside Uber to remake the company's image. He said the company had recently hired a chief privacy officer and a chief trust and security officer.

"We know that earning the trust of our customers and the regulators we work with globally is no easy feat. After all, trust is hard to gain and easy to lose," Mr. West said.

He added that the breach was disclosed to the public during his first day on the job. "Rather than settling into my new work space and walking the floor to meet my new colleagues, I spent the day calling various state and federal regulators," Mr. West said.

The Federal Trade Commission settled its investigation into the data breach in April. The trade commission now requires Uber to submit to regular privacy audits as part of a 2017 settlement, which was revised this year to address the most recent breach.

The $148 million settlement announced Wednesday will be divided among all 50 states and the District of Columbia.

"Companies in California and throughout the nation are entrusted with customers' valuable private information," Mr. Becerra said. "This settlement broadcasts to all of them that we will hold them accountable to protect that data."

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Uber Settles Data Breach Investigation for $148 Million

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SAN FRANCISCO - Uber will pay $148 million to settle a nationwide investigation into a 2016 data breach, in which a hacker managed to gain access to information belonging to 57 million riders and drivers. The breach included names and driver's license numbers for 600,000 drivers.

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I found this article linked below, and it seems fairly relevant to what is happening today. I found out more about Uber's unhealthy business practices, and the multitude of workers it has had to fire due to this coronavirus pandemic

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Uber has announced it will be laying off an additional 3,000 employees as the coronavirus pandemic decreases demand for the company's services.

The company initially announced it would be laying off 3,700 staff members after Covid-19 started to make its toll on the ride-share industry. But more cuts would happen, Uber announced Monday.

Now the company has lost 25 per cent of its employees since the start of the pandemic.

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Sharing the full story, not just the headlines

Uber's Chief Executive Dara Khosrowshahi made the announcement in an email to employees. It also detailed how 45 offices would be closed in addition to the 3,000 jobs eliminated. Uber drivers are not considered employees under the ride-share app, so their employment was not impacted.

In April when a majority of Americans were under stay-at-home orders, the company found rides were down 80 per cent compared to the previous year.

"Given the dramatic impact of the pandemic, and the unpredictable nature of any eventual recovery, we are concentrating our efforts on our core mobility and delivery platforms and resizing our company to match the realities of our business," Mr Khosrowshahi said in a statement.

"That's led us to some painful decisions today: we are stopping some of our non-core investments and reducing the size of our workforce by around 3,000 people, each of whom I want to personally thank for their contributions to Uber. As I said to our teams today, we are making these hard choices now so that we can move forward and begin to build again with confidence."

The ride-share portion of Uber makes up about 75 per cent of the company profits, so that area taking a hit during the pandemic has greatly impacted the overall revenue.

Uber Eats, in comparison, was the bright spot amid the pandemic because of its increased demand, but "the business today doesn't come close to covering our expenses", Mr Khosrowshahi said.

Uber has reportedly been in talks to buy rival Grubhub, another food delivery service, according to The Wall Street Journal. The purchase of the delivery service could help boost revenue for Uber, but there was no mention of the potential deal in the memo emailed out to employees.

Other competitors in the ride-share industry have also seen a decline in revenue as more Americans are staying at home instead of travelling.

Lyft, Uber's top rival, has cut 17 per cent of its staff because of Covid-19.

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Uber has announced it will be laying off an additional 3,000 employees as the coronavirus pandemic decreases demand for the company's services. The company initially announced it would be laying off 3,700 staff members after Covid-19 started to make its toll on the ride-share industry. But more cuts would happen, Uber announced Monday.

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It is a very interesting article, and gives a lot of information about how the data leak from Uber hit different countries differently. I was able to find more information about the actual data leak through this website.

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Uber has revealed that 2.7m UK users of its app were affected by a mass data breach in 2016, which the company disclosed last week.

A ransom of $100,000 (£75,500) was paid to hackers so they would delete the data and keep the security lapse quiet.

Uber said user names, email addresses and mobile numbers of customers were hacked.

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Sharing the full story, not just the headlines

The app is used in towns and cities across the UK, with 3.5 million passengers and 40,000 drivers in London.

Sadiq Khan, London's Mayor, said: "This latest shocking development about Uber will alarm millions of Londoners whose personal data could have been stolen by criminals.

"Uber need to urgently confirm which of their customers are affected, what is being done to ensure these customers don't suffer adversely, and what action is being taken to prevent this happening again in the future.

"The public will want to know how there could be this catastrophic breach of personal data security."

Last week, Uber disclosed that 57 million people were hit by the breach, but did not say how many UK customers were affected.

Uber said that it did not believe any individual customers needed to take action.

"We have seen no evidence of fraud or misuse tied to the incident. We are monitoring the affected accounts and have flagged them for additional fraud protection," the company said in a statement.

The news comes after what has been a tough year for the company.

In October, Uber launched an appeal against Transport for London's (TfL) decision to deny it a new operating licence in the capital on the grounds of "public safety and security implications".

A spokesperson for the National Cyber Security Centre (NCSC) said: "We assess that the stolen information does not pose a direct threat to people or allow direct financial crime.

No hype, just the advice and analysis you need

"People who are concerned should continue to be vigilant and follow the advice on the NCSC website."

The Information Commissioner's Office has previously said that it is hugely concerned about Uber's data protection policies and ethics.

James Dipple-Johnstone, deputy commissioner of ICO, said: "On its own this information is unlikely to pose a direct threat to citizens. However, its use may make other scams, such as bogus emails or calls, appear more credible. People should continue to be vigilant and follow the advice from the NCSC.

"As part of our investigation we are still waiting for technical reports which should give full confirmation of the figures, and the type of personal data that has been compromised.

"We would expect Uber to alert all those affected in the UK as soon as possible.

"We are continuing to work with the NCSC plus other relevant authorities in the UK and overseas to ensure the data protection interests of UK citizens are upheld."

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Uber data leak hit 2.7m customers, admits ride-hailing company

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Uber has revealed that 2.7m UK users of its app were affected by a mass data breach in 2016, which the company disclosed last week. A ransom of $100,000 (£75,500) was paid to hackers so they would delete the data and keep the security lapse quiet.

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I found this tweet while attempting to find more information about Uber and its data leaks. It links to an article by The Independent. It looks interesting, and seems like its from a trusted source.

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48
— The Independent (@Independent) November 29, 2017

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48

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Uber data leak hit 2.7m customers, admits ride-hailing company https://t.co/4IsxA2jZ48

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Mobility networks and the spatial spreading of infectious diseases

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Mobility networks and the spatial spreading of infectious diseases

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I saw the author use this article as evidence and decided to dive in further. It is very relevant today with the coronavirus pandemic, and it highlighted many of the problems that led to the coronavirus. I found it very interesting, and it turns out Uber and other companies could have contributed to the spread of the virus.

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Computational approaches to the realistic modeling of spatial epidemic spread make use of a wide array of simulation schemes ( 1) ranging from very detailed agent-based approaches ( 2- 6) to structured metapopulation models based on data-driven mobility schemes at the interpopulation level ( 7- 10). All these approaches integrate a wealth of real-world data. However, it is not yet clear how to discriminate the effects of the inclusion/lack of real-world features in specific models. This limitation is mainly related to our incomplete knowledge of human interactions and mobility processes, which are fundamental aspects to describe a disease spread. Although recent efforts started to make available massive data on human mobility from different sources and at different levels of description ( 11- 20), the multiscale nature of human mobility is yet to be comprehensively explored. Human mobility can be generally described by defining a network of interacting communities where the connections and the corresponding intensity represent the flow of people among them ( 13, 14). Global mobility flows therefore form very complex multiscale networks ( 21) spanning several orders of magnitude in intensity and spatiotemporal scales ranging from the long-range intercontinental air traffic ( 13, 15) to the short range commuting flows ( 17- 19). A multitude of heuristic models for population structure and mobility patterns have been proposed, but they all depend on the specific mobility process under consideration ( 22, 23). The limited understanding of the interrelations among the multiple scales entailed in human mobility and their impact on the definition of epidemic patterns constitute a major road block in the development of predictive large-scale data driven epidemic models. In this context, two questions stand out: () Is there a most relevant mobility scale in the definition of the global epidemic pattern? and ( ii) At which level of resolution of the epidemic behavior does a given mobility scale become relevant, and to what extent?

To begin addressing these questions, we use high-resolution worldwide population data that allow for the definition of subpopulations according to a Voronoi decomposition of the world surface centered on the locations of International Air Transport Association (IATA)-indexed airports ( www.iata.org). We have then gathered data on the commuting patterns of 29 countries in five continents, constructing short-range commuting networks for the defined subpopulations. Extensive analysis of these networks allows us to draw a general gravity law for commuting flows that reproduces commuting patterns worldwide. This law, valid at the scale defined by the tessellation process, is statistically stable across the world because of the globally homogeneous procedure applied to build the subpopulations around transportation hubs. The multiscale networks we obtain are integrated into the global epidemic and mobility (GLEaM) model, a computational platform that uses a metapopulation stochastic model on a global scale to simulate the large-scale spreading of influenza-like illnesses (ILI). To fully consider the effect of multiscale mobility processes in the disease dynamics, we develop a timescale-separation technique for evaluating the force of infection due to different mobility couplings and simulate global pandemics with tunable reproductive ratios. The results obtained from the full multiscale mobility network are compared with the simulations in which only the large-scale coupling of the airline transportation network is included. Our analysis shows that although commuting flows are, on average, one order of magnitude larger than the long-range airline traffic, the global spatiotemporal patterns of disease-spreading are mainly determined by the airline network. Short-range commuting interactions have, on the other hand, a role in defining a larger degree of synchronization of nearby subpopulations and specific regions, which can be considered weakly connected by the airline transportation system. In particular, it is possible to show that short-range mobility has an impact in the definition of the subpopulation infection hierarchy. The techniques developed here allow for an initial understanding of the level of data integration required to obtain reliable results in large-scale modeling of infectious diseases.

Results and Discussion

Simulations of worldwide epidemic spread are generally based on structured metapopulation models that consider data-driven schemes for long-range mobility at the interpopulation level coupled with coarse-grained techniques within each subpopulation ( 7- 10, 24- 26). In this paper, we use the GLEaM computational scheme based on a georeferenced metapopulation approach. The model consists of three data layers. The population and mobility layers allows the partition of the world into geographical census regions coupled by population movements. This partition defines the subpopulation network where the connections between subpopulations represent the fluxes of individuals due to the transportation infrastructures and mobility patterns. Superimposed on this subpopulation network is the epidemic layer that defines inside each subpopulation the disease dynamics that depends on the specific etiology of the disease considered (see Material and Methods).

Multiscale Mobility Networks.

The basic structure of GLEaM is based on high-resolution population data that estimates the population with a resolution given by cells of 15 × 15 minutes of arc, covering the whole planet. This population data allows the construction of Voronoi tassels around transportation hubs in the world, defining the subpopulations structure of the metapopulation model (see SI Appendix). In particular, we identify 3,362 subpopulations centered around IATA airports in 220 different countries. The air-traffic network among the defined subpopulations is obtained from the IATA databases that contain the list of worldwide airport pairs connected by direct flights and the number of available seats on any given connection. The high level of geographical resolution of the subpopulation database enables us to integrate also the mobility flows due to commuting patterns between subpopulations (see Material and Methods) and construct the corresponding commuting network. The main difficulty in defining a commuting network worldwide is the lack of a global database as opposed to the case of the air-traffic flow. Data are scattered in different national and international databases that use different administrative and geographical granularities, and several definitions of commuting flows. We have collected commuting data from 29 countries (a full list of countries and the database properties are reported in the SI Appendix) in five different continents. Each dataset was mapped into the GLEaM Voronoi tessellation constructing the commuting networks at the subpopulation level.

In , we show the commuting network of the continental U.S. as obtained by mapping the county commuting data onto the subpopulations used by GLEaM. Commuting data do not consider airline flows that are accounted for by the IATA dataset. On the same scale, we also report the airline traffic network, readily highlighting the difference in scale and spatial structure of the two networks. The commuting network appears as an almost grid-like lattice connecting neighboring subpopulations, whereas the airline traffic network is dominated by long range connections. The wide range of scales is evident also in the intensities of the mobility flows, spanning several orders of magnitude, with the average commuting flow being one order of magnitude larger than the average airline traffic flow. Finally, it should be noted that, in general, commuting flows refer to round trip processes with a characteristic time of the order of 1/3 day (average duration of a work day) compared with much longer characteristic times for airline travel (average value around two weeks at the end.

To gain general insight on the commuting flow, we use the general gravity model from transportation theory ( 22, 23) as a starting point. This model assumes that the commuting flow between subpopulation i (with population ) and subpopulation j (with population ) takes on the form:

where C is a proportionality constant, α and γ tune the dependence with respect to each subpopulation size, and f() is a distance-dependent functional form. Gravity laws usually consider power or exponential laws for the behavior of f(). The results reported in the literature are variable and generally depend on the way the subpopulations are defined. In our case, we can take advantage of the statistical similarity of the subpopulations centered on major transportation hubs. We tested the gravity law by the statistical analysis of more than 10 ⁴ flows worldwide, and we found that the best fit is obtained by using an exponential function f() = exp(/ r), where r is the characteristic length that governs the decay of commuting flows. In , we report the estimated values for the exponents α, γ, and r.

Noticeably, we can validate the gravity law at the level of geographical area or country as shown in , where the commuting flows obtained from the data are compared with the synthetic ones predicted by the model, as functions of the various variables of the gravity law. In the SI Appendix, we report similar tests at the level of geographical areas and countries, that confirm the generality and goodness of the obtained law. It is worth remarking that although the present gravity law works well at the granularity defined by our Voronoi decomposition, these results cannot be extrapolated to different granularities. For instance, we notice that the exponents obtained by our approach are quite close to those obtained by Viboud et al. ( 17), although the spatial decay has a completely different form. We do not see any obvious reason for the observed scaling form, but it must be noted that we are working with subpopulations, which are by construction statistically more homogeneous and of larger size than the county level used in ref. 17. Administrative regions might indeed impose boundaries that define subpopulations not clearly associated to centers of gravity for mobility processes, as e.g., large urban areas cut in multiple counties. In general, both approaches are usually tested in transportation theory (as in ref. 19), and in some cases the exponential function was found to better fit migration phenomena ( 27).

The above gravity law allows us to work with two different worldwide commuting networks. An entirely synthetic one, generated by using the gravity law fitted to the empirical data, and one integrating the empirical data. The synthetic network considers only neighboring subpopulations. We have empirically observed that second-neighbor interactions are on average one order of magnitude smaller than the nearest-neighbor interactions and their contribution can be neglected in the first-order estimation of the effective force of infection. In the following, we will report the results obtained only with the synthetic network and we leave to the SI Appendix a demonstration that no significant differences are observed when we compare the results obtained by using the synthetic and the real data networks.

Epidemic Simulations.

To study the effect that commuting networks have on the overall spread of an emerging disease, we consider the simulation of an ILI and compare the results with a simulation in which we include only airline traffic as in previous works ( 10). For the sake of clarity, in the following we compare the results for the simulations of a hypothetical pandemic influenza with R ₀ = 1.9 starting in Hanoi on April 1 (the SI Appendix also reports results for October 1). The model includes a seasonal dependence of the transmission. In the SI Appendix file, we report simulations of the 2001-2002 seasonal influenza timeline and compare those with the real surveillance data. The simulations of the realistic model and comparison with real data confirm the analysis presented here for the synthetic pandemic model.

GLEaM is fully stochastic and takes into account the discrete nature of individuals both in the travel coupling and in the compartmental transitions. The transmission model within each urban area follows a compartmentalization specific to the disease under study. Here we use the classic ILI compartmentalization in which each individual is classified by one of the discrete states such as susceptible (S), latent (L), infectious (I), and permanently recovered (R). Infectious persons are further subdivided into asymptomatic, symptomatic traveling, and symptomatic nontraveling as detailed in Material and Methods. The discrete nature of individuals is implemented by introducing binomial and multinomial processes inside each urban area for the stochastic evolution of the infection. A detailed exposition of the stochastic approach to disease evolution is provided in the SI Appendix.

In GLEaM, the airline mobility is integrated explicitly as individuals are allowed to travel from one subpopulation to another by means of the airline transportation network ( 28) similarly to the models in refs. 7 and 8 and the stochastic generalizations of ref. 9. In each subpopulation j, the number of individuals is (t) and X(t) is the number of individuals in compartment [ m] of the disease evolution at time t; therefore (t) = Σ(t). The dynamics of individuals traveling between cities is described by the stochastic transport operator Ω ({ X}) representing the net balance of individuals in a given state [ m] that entered or left each city j. This operator is a function of the traffic flows per unit time ω with the neighboring cities and of the city population . In particular, the number of passengers of each category traveling from city j to city ℓ is an integer random variable, in that each of the potential travelers has a probability p = ω δt/ to go from j to ℓ in the time interval δt. In city j, the number of passengers traveling on each connection ( j,ℓ) at time t defines a set of stochastic variables that follow a multinomial distribution ( 10). The calculation can be extended to include transit traffic up to one connecting flight ( 29).

The introduction of commuting flows represents a numerical challenge as it acts at a very different time scale with individuals that have very short visit duration in the neighboring subpopulation. Although the airline traffic finds a natural time scale of one day, an equivalent mechanistic simulation of the commuting mobility would require working on a much smaller time scale hardly compatible with the airline traffic. This problem can be solved by relying on the results of refs. 30 and 31. Commuting flows govern subpopulation interactions by defining the visiting rate of an individual in subpopulation i to subpopulation j as σ = /. Visiting individuals have a very short visit time associated to high return rates, τ, to their original subpopulations (τ ≥ 3 day −1), corresponding to σ ≪ τ for all of the populations. It can be shown that the system is then described by stationary quantities in each compartment [ m] for time scales larger than τ −1 (the time scale governing the relaxation time to equilibrium across subpopulations) in which is defined as the number of visitors currently in j coming from subpopulation i in compartment [ m]. When the disease duration is significantly larger than τ −1, as is the case for the ILI with characteristic time μ −1 ≃ 3 days, we can use a time scale separation approximation in which at each time step, the force of infection experienced by susceptible individuals in each subpopulation is a function of the stationary values and . The full force of infection used in the model is reported in the Material and Methods section, and a full derivation of the time separation approximation is reported in the SI Appendix.

shows that the global and regional timing and size of the epidemic is weakly affected by also considering the commuting network in GLEaM. Both the probability of having a global outbreak and the overall profiles are very similar in the two cases, with the global epidemic size at the end of the first year almost unaffected by the inclusion of commuting. We perform a sensitivity analysis by testing the outset variation of the intensity of the commuting flows and varying the return time rate τ of more than one order of magnitude. In the SI Appendix, we show that the profiles do not show significant variations, the results being very robust against strong fluctuations in the commuting mobility process. The effect of commuting flows is, however, noticeable during the tail of the epidemic event. As presented in , many regions of the world show a broader tail in the absence of commuting, showing that the commuting coupling enhances the synchronization of the local epidemic profiles. The observed broadening of an epidemic profile that includes multiple subpopulations is due to the different timing of the outbreak that reaches the various subpopulations. The effect is more pronounced in the lack of short range coupling, as highlighted in the example reported in D and E of an air transportation hub loosely connected by air travel flow to the surrounding subpopulations. As expected, no significant change is observed in the hub profile, whereas the time delay in neighboring locations with limited airline connections is dramatically reduced by the coupling due to local commuting flows. After infecting the hub, the epidemic radiates out to the neighboring geographical census areas in a pattern reminiscent of the physical process of diffusion. This effect naturally leads to a much stronger correlation and synchrony in the evolution of the pandemic at the local level. In the SI Appendix, we present the model informed with the realistic parameters and initial conditions of 2001-2002 seasonal influenza season and compare the obtained epidemic pattern with the real data. The results confirm the synchronization effect and the better agreement with the actual data at the regional level when commuting flows are introduced in the model.

Commuting flows therefore alter the hierarchy of epidemic transmission from region to region. This hierarchical organization can be inferred by constructing the epidemic invasion tree that represents the transmission of the infection from one subpopulation to the other during the history of the epidemic. The stochastic nature of the epidemic process implies that each realization will produce a different tree. An overall epidemic invasion network can be constructed by defining weighted, directed links, , that denote the probability that the epidemic in subpopulation j is seeded by individuals belonging to the subpopulation i. This probability is defined by the ratio between the number of realizations in which we have a seeding i → j and the total number of realizations. When constructing the epidemic invasion tree we use averages over 10 ³ realizations. Finally, to highlight only the most likely infection tree, we construct the minimum spanning tree from the world-seeding subpopulation where we minimize the distance defined on each link as . In , we show the infection arrival time hierarchy in the two considered scenarios for the continental U.S. In the absence of commuting ( A), airline hubs have a predominant role and are completely responsible for spreading the disease to every other location through direct air connections. This feature leads to the counterintuitive effect that locations near a large airport, but with no frequent direct flight to that airport, can be infected only much later through a convoluted sequence of flights. On the other hand, when we superimpose the commuting network we obtain the expected effect of reducing the importance of large airports and increasing the locality of the epidemic spread ( B). The inclusion of commuting patterns is therefore relevant in the evaluation of the epidemic invasion path and timing.

Materials and Methods

Voronoi Tessellation Around Main Transportation Hubs.

We define the geographical census areas centered around IATA airports by assigning the population of each cell of 15 × 15 minutes of arc to the closest airport within the same country. Such a procedure defines a Voronoi-like tessellation ( 32) for the populated cells of the world with a cut-off scale for the tassels size of 200 kms (see also the SI Appendix for further details).

Disease Structure, Seasonality and R ₀.

In each urban area, the evolution of the disease is governed by the compartmental scheme of the baseline scenario of ref. 10. A susceptible individual S in contact with a symptomatic (I t,I nt, traveling or nontraveling, respectively) and asymptomatic (I) infectious individual contracts the infection at rate β or r β β, respectively, and enters the latent (L) compartment, where he/she is infected but not yet infectious. At the end of the latency period, individuals in the latent class enter one of the symptomatic infectious compartments (I t, I nt) with probability 1−p or become asymptomatic (I) with probability p. Symptomatic individuals are further divided between those who are allowed to travel (I t) with probability p t and those who are prevented from doing so (I nt) with probability 1−p t, depending on the severity of symptoms. All infectious individuals enter the permanently recovered/removed compartment (R) at a rate of μ per day. The latent period has an average duration of ε −1 = 1.9 days and is assumed to be followed by an infectious period with a mean duration of μ −1 = 3 days ( 3, 10, 33). Given that infection has occurred, we assume that individuals become asymptomatic with probability p = 0.33 ( 3, 10, 33). The relative infectiousness of asymptomatic individuals is r β = 0.5 ( 10) and symptomatic individuals are allowed to travel with probability p t = 0.5. The contagion process (i.e., the generation of new infections through the transmission of the disease from infectious to susceptible individuals) and the spontaneous transitions (e.g., from latent to infectious or from infectious to recovered) are modeled with binomial and multinomial distributions (see the SI Appendix for a detailed description of the processes). The threshold parameter of the disease that determines the spreading rate of infection is called basic reproduction number ( R ₀) and is defined as the average number of infected cases generated by a typical infectious individual when introduced into a fully susceptible population ( 34). For our compartmental model we have R ₀ = βμ −1 [1−p + r β p]. The R ₀ values indicated in the figures and discussed in the paper do not consider the effect of seasonality and the commuting in the force of infection. We take into account the seasonal behavior of influenza by adopting the scheme from ref. 10. The transmission rate β in each geographical census area is adjusted by a scaling factor that varies monthly according to the city's climatic zone. For example, cities in the tropical zone have a scaling factor that is always 1, independent of the season. See ref. 10 and its supporting information for details.

Effective Force of Infection Generated by Commuting Flows.

The effect of commuting in the spread of infection can be considered implicitly by evaluating the force of infection between subpopulations coupled by commuting flows. In the case of τ ≪ σ, the relaxation time to equilibrium values in the populations is dominated by the return rate τ to the origin subpopulation, as shown in the SI Appendix file. We can therefore use the equilibrium values of population sizes in our calculations of force of infection, because the τ −1 is much smaller than the time scales of disease evolution (i.e., ε −1 and μ −1). New infections in a subpopulation are due to the transmission between susceptibles and infectious individuals occurring in the subpopulation or during a visit to a neighboring subpopulation. Taking this into account, it is possible to derive the force of infection λ in j as

A detailed derivation is provided in the SI Appendix. In the above expression, N* = + Σ is the actual number of individuals in j due to commuting. The first terms of the right-hand side of Eq. 2 takes into account the transmission of the infection from the local infectious individuals in j. The second term considers the transmission due to the infectious individuals during their visits to j with local susceptible persons. The third and fourth terms consider the interactions of susceptible individuals during their visits to neighboring subpopulations i with the local infectious persons and the infectious visitors of i, respectively. Here we have also considered that the transmission rate β may be different in each population. The last expression includes second-order commuting terms (e.g., σ σ/τ ²), which are neglected in the actual computation. The probability of new infections to be generated in city j is finally given by λ δt in the time interval δt, acting on a pool of susceptible individuals .

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From the Cover: Multiscale mobility networks and the spatial spreading of infectious diseases

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Among the realistic ingredients to be considered in the computational modeling of infectious diseases, human mobility represents a crucial challenge both on the theoretical side and in view of the limited availability of empirical data. To study the interplay ...

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In this article, the author lays out specific examples of how our every move is tracked with apps like Uber. They use your data of where you are going to specifically target ads at you about those places. This invasive type of marketing should be frowned upon.

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Abstract

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

Derived from the Latin Privatus, meaning "withdraw from public life," the notion of privacy has been foundational to the development of our diverse societies, forming the basis for individuals' rights such as free speech and religious freedom. Despite its importance, privacy has mainly relied on informal protection mechanisms. For instance, tracking individuals' movements has been historically difficult, making them de-facto private. For centuries, information technologies have challenged these informal protection mechanisms. In 1086, William I of England commissioned the creation of the Doomsday book, a written record of major property holdings in England containing individual information collected for tax and draft purposes. In the late 19th century, de-facto privacy was similarly threatened by photographs and yellow journalism. This resulted in one of the first publications advocating privacy in the U.S. in which Samuel Warren and Louis Brandeis argued that privacy law must evolve in response to technological changes.

Modern information technologies such as the Internet and mobile phones, however, magnify the uniqueness of individuals, further enhancing the traditional challenges to privacy. Mobility data is among the most sensitive data currently being collected. Mobility data contains the approximate whereabouts of individuals and can be used to reconstruct individuals' movements across space and time. Individual mobility traces T [] have been used in the past for research purposes and to provide personalized services to users. A list of potentially sensitive professional and personal information that could be inferred about an individual knowing only his mobility trace was published recently by the Electronic Frontier Foundation. These include the movements of a competitor sales force, attendance of a particular church or an individual's presence in a motel or at an abortion clinic.

While in the past, mobility traces were only available to mobile phone carriers, the advent of smartphones and other means of data collection has made these broadly available. For example, Apple® recently updated its privacy policy to allow sharing the spatio-temporal location of their users with "partners and licensees". 65.5B geo-tagged payments are made per year in the US while Skyhook wireless is resolving 400 M user's WiFi location every day. Furthermore, it is estimated that a third of the 25B copies of applications available on Apple's App Store SM access a user's geographic location, and that the geo-location of ~50% of all iOS and Android traffic is available to ad networks. All these are fuelling the ubiquity of simply anonymized mobility datasets and are giving room to privacy concerns.

A simply anonymized dataset does not contain name, home address, phone number or other obvious identifier. Yet, if individual's patterns are unique enough, outside information can be used to link the data back to an individual. For instance, in one study, a medical database was successfully combined with a voters list to extract the health record of the governor of Massachusetts. In another, mobile phone data have been re-identified using users' top locations. Finally, part of the Netflix challenge dataset was re-identified using outside information from The Internet Movie Database.

All together, the ubiquity of mobility datasets, the uniqueness of human traces, and the information that can be inferred from them highlight the importance of understanding the privacy bounds of human mobility. We show that the uniqueness of human mobility traces is high and that mobility datasets are likely to be re-identifiable using information only on a few outside locations. Finally, we show that one formula determines the uniqueness of mobility traces providing mathematical bounds to the privacy of mobility data. The uniqueness of traces is found to decrease according to a power function with an exponent that scales linearly with the number of known spatio-temporal points. This implies that even coarse datasets provide little anonymity.

Results

Uniqueness of human mobility

In 1930, Edmond Locard showed that 12 points are needed to uniquely identify a fingerprint. Our unicity test estimates the number of points p needed to uniquely identify the mobility trace of an individual. The fewer points needed, the more unique the traces are and the easier they would be to re-identify using outside information. For re-identification purposes, outside observations could come from any publicly available information, such as an individual's home address, workplace address, or geo-localized tweets or pictures. To the best of our knowledge, this is the first quantification of the uniqueness of human mobility traces with random points in a sparse, simply anonymized mobility dataset of the scale of a small country.

Given , a set of spatio-temporal points, and D, a simply anonymized mobility dataset, we evaluate ε, the uniqueness of traces, by extracting from D the subset of trajectories S() that match the p points composing [See Methods]. A trace is unique if |S()| = 1, containing only one trace. For example, in , we evaluate the uniqueness of traces given = 2. The two spatio-temporal points contained in = 2 are zone I from 9am to 10am and zone II from 12pm to 1pm. The red and the green traces both satisfy = 2, making them not unique. However, we can also evaluate the uniqueness of traces knowing = 3, adding as a third point zone III between 3pm and 4pm. In this case |S( = 3)| = 1, uniquely characterize the green trace. A lower bound on the risk of deductive disclosure of a user's identity is given by the uniqueness of his mobility trace, the likelihood of this brute force characterization to succeed.

Our dataset contains 15 months of mobility data for 1.5 M people, a significant and representative part of the population of a small European country, and roughly the same number of users as the location-based service Foursquare®. Just as with smartphone applications or electronic payments, the mobile phone operator records the interactions of the user with his phone. This creates a comparable longitudinally sparse and discrete database []. On average, 114 interactions per user per month for the nearly 6500 antennas are recorded. Antennas in our database are distributed throughout the country and serve, on average, ~ 2000 inhabitants each, covering areas ranging from 0.15 km ² in cities to 15 km ² in rural areas. The number of antennas is strongly correlated with population density (R ² = .6426) []. The same is expected from businesses, places in location-based social networks, or WiFi hotspots.

shows the fraction of unique traces ( ε) as a function of the number of available points p. Four randomly chosen points are enough to uniquely characterize 95% of the users ( ε > .95), whereas two randomly chosen points still uniquely characterize more than 50% of the users ( ε > .5). This shows that mobility traces are highly unique, and can therefore be re-identified using little outside information.

Scaling properties

Nonetheless, ε depends on the spatial and temporal resolution of the dataset. Here, we determine this dependence by lowering the resolution of our dataset through spatial and temporal aggregation []. We do this by increasing the size of a region, aggregating neighbouring cells into clusters of v cells, or by reducing the dataset's temporal resolution, increasing the length of the observation time window to h hours [see Methods]. Both of these aggregations are bound to decrease ε, and therefore, make re-identification harder.

shows how the uniqueness of mobility traces ε depends on the spatial and temporal resolution of the data. This reduction, however, is quite gradual. Given four points (p = 4), we find that ε > .5 when using a resolution of h = 5 hours and v = 5 antennas.

Statistically, we find that traces are more unique when coarse on one dimension and fine along another than when they are medium-grained along both dimensions. Indeed, given four points, ε > .6 in a dataset with a temporal resolution of h = 15 hours or a spatial resolution of v = 15 antennas while ε > .4 in a dataset with a temporal resolution of h = 7 hours and a spatial resolution of v = 7 antennas [].

Next, we show that it is possible to find one formula to estimate the uniqueness of traces given both, the spatial and temporal resolution of the data, and the number of points available to an outside observer. Fig. 4B and 4C show that the uniqueness of a trace decreases as the power function ε = α − , for decreases in both the spatial and temporal resolution ( x), and for all considered p = 4, 6, 8 and 10 (see Table S1). The uniqueness of human mobility can thus be expressed using the single formula: ε = α − ( ν h). We find that this power function fits the data better than other two-parameters functions such as α − exp ( λx), a stretched exponential α − exp , or a standard linear function α − βx (see Table S1). Both estimators for α and β are highly significant (p < 0.001), and the mean pseudo-R ² is 0.98 for the = 4 case and the = 10 case. The fit is good at all levels of spatial and temporal aggregation [ Fig. S3A-B].

The power-law dependency of ε means that, on average, each time the spatial or temporal resolution of the traces is divided by two, their uniqueness decreases by a constant factor ~ (2). This implies that privacy is increasingly hard to gain by lowering the resolution of a dataset.

shows that, as expected, ε increases with p. The mitigating effect of p on ε is mediated by the exponent β which decays linearly with p: β = 0.157 − 0.007 p []. The dependence of β on p implies that a few additional points might be all that is needed to identify an individual in a dataset with a lower resolution. In fact, given four points, a two-fold decrease in spatial or temporal resolution makes it 9.3% less likely to identify an individual, while given ten points, the same two-fold decrease results in a reduction of only 6.2% (see Table S1).

Because of the functional dependency of ε on p through the exponent β, mobility datasets are likely to be re-identifiable using information on only a few outside locations.

Discussion

Our ability to generalize these results to other mobility datasets depends on the sensitivity of our analysis to extensions of the data to larger populations, or geographies. An increase in population density will tend to decrease ε. Yet, it will also be accompanied by an increase in the number of antennas, businesses or WiFi hotspots used for localizations. These effects run opposite to each other, and therefore, suggest that our results should generalize to higher population densities.

Extensions of the geographical range of observation are also unlikely to affect the results as human mobility is known to be highly circumscribed. In fact, 94% of the individuals move within an average radius of less than 100 km. This implies that geographical extensions of the dataset will stay locally equivalent to our observations, making the results robust to changes in geographical range.

From an inference perspective, it is worth noticing that the spatio-temporal points do not equally increase the likelihood of uniquely identifying a trace. Furthermore, the information added by a point is highly dependent from the points already known. The amount of information gained by knowing one more point can be defined as the reduction of the cardinality of S() associated with this extra point. The larger the decrease, the more useful the piece of information is. Intuitively, a point on the MIT campus at 3AM is more likely to make a trace unique than a point in downtown Boston on a Friday evening.

This study is likely to underestimate ε, and therefore the ease of re-identification, as the spatio-temporal points are drawn at random from users' mobility traces. Our are thus subject to the user's spatial and temporal distributions. Spatially, it has been shown that the uncertainty of a typical user's whereabouts measured by its entropy is 1.74, less than two locations. This makes our random choices of points likely to pick the user's top locations (typically "home" and "office"). Temporally, the distribution of calls during the week is far from uniform [ Fig. S1] which makes our random choice more likely to pick a point at 4PM than at 3AM. However, even in this case, the traces we considered that are most difficult to identify can be uniquely identified knowing only 11 locations [].

For the purpose of re-identification, more sophisticated approaches could collect points that are more likely to reduce the uncertainty, exploit irregularities in an individual's behaviour, or implicitly take into account information such as home and workplace or travels abroad. Such approaches are likely to reduce the number of locations required to identify an individual, vis-à-vis the average uniqueness of traces.

We showed that the uniqueness of human mobility traces is high, thereby emphasizing the importance of the idiosyncrasy of human movements for individual privacy. Indeed, this uniqueness means that little outside information is needed to re-identify the trace of a targeted individual even in a sparse, large-scale, and coarse mobility dataset. Given the amount of information that can be inferred from mobility data, as well as the potentially large number of simply anonymized mobility datasets available, this is a growing concern. We further showed that while , β ~ − p/100. Together, these determine the uniqueness of human mobility traces given the traces' resolution and the available outside information. These results should inform future thinking in the collection, use, and protection of mobility data. Going forward, the importance of location data will only increase34 and knowing the bounds of individual's privacy will be crucial in the design of both future policies and information technologies.

Methods

The dataset

This work was performed using an anonymized mobile phone dataset that contains call information for ~1.5 M users of a mobile phone operator. The data collection took place from April 2006 to June 2007 in a western country. Each time a user interacts with the mobile phone operator network by initiating or receiving a call or a text message, the location of the connecting antenna is recorded []. The dataset's intrinsic spatial resolution is thus the maximal half-distance between antennas. The dataset's intrinsic temporal resolution is one hour [].

Unicity test and the likelihood of deductive disclosure

The considered dataset contains one trace T for each user. The traces spatio-temporal points contain the region in which the user was and the time of the interaction. We evaluate the uniqueness of a trace given a set Ip of p randomly chosen spatio-temporal points. A trace is said to be to be compatible with Ip if [ Fig. 2A ]. Note that this notion of compatibility can easily be extended to noisier or richer data. A brute force characterization is performed by extracting from the entire dataset of 1.5 M users S( Ip), the set of users whose mobility traces T are compatible with Ip. All mobility traces in the dataset T are successively tested for compatibility with Ip. A trace is characterized "out of x", if the set of traces that are compatible with the points contains at most x users: |S( Ip)| ≤ x. A trace is uniquely characterized if the set contains exactly one trace: |S( Ip)| = 1. The uniqueness of traces is estimated as the percentage of 2500 random traces that are unique given p spatio-temporal points. The p points composing Ip are taken at random among all the interactions the user had with the service. As discussed, we do not apply any constraints regarding the choice of Ip.

Minimum number of spatio-temporal location needed to uniquely characterize every trace

shows that .95 < ε < 1 given = 4. evaluates the minimum p needed to uniquely characterize every trace in a given set. This set contains a random sample of 1000 heavy-users, i.e. users that used their phone at least 75 times per month as their randomly chosen points might make their trace less unique.

Spatial aggregation

Spatial aggregation is achieved by increasing the size of the regions in which the user is known to be during his interactions with the service. In the case of discrete data, a bijective relation exists between antennas (known in this case as centroids) and the region defined by the Voronoi tessellation. The tessellation is defined so that every point in a region is closer to the region's antenna than to any other antenna. In order to increase the region's area, one should group antennas into clusters of a given size v. While the problem of optimally grouping places in a 2D space into groups of given sizes v is non trivial, it can be approximated through clustering methods. The canonical clustering methods focus on minimizing the within-cluster sum of squares rather than producing balanced clusters. This drawback can be controlled by the use of a Frequency Sensitive Competitive Learning scheme. Fig. S2 shows the resulting group size histogram optimized for clusters of size 4. Once antennas are aggregated into groups, their associated regions are merged.

Author Contributions

Y.-A. de M. designed and performed experiments, analyzed data and wrote the paper; C.A.H. designed experiments, developed analytic tools and wrote the paper; M.V. and V.D.B. designed experiments and wrote the paper.

Supplementary Material
Supplementary Information:
Online Support Material for: Unique in the Crowd: The privacy bounds of human mobility

Acknowledgments

We thank Damien François, Janos Kertesz, Renaud Lambiotte, Vincent Traag and Paul Van Dooren for discussions and comments on the manuscript as well as Maxime Melchior for sharing computer code and Susie Fu for help with the figures. This work was supported by a grant 09/14-017 "Action de Recherche Concerté" of the "Communauté française de Belgique" on Information Retrieval in Time Evolving Networks.

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Unique in the Crowd: The privacy bounds of human mobility

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We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal ...

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The wiki laid out specific laws in the US and other nations which prohibit the abuse of consumer's privacy. It was interesting and made me begin to question how mobility and your own personal information(home addresses, phone number, etc) play into this exactly.

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Privacy is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively.

When something is private to a person, it usually means that something is inherently special or sensitive to them. The domain of privacy partially overlaps with security, which can include the concepts of appropriate use, as well as protection of information. Privacy may also take the form of bodily integrity. The right not to be subjected to unsanctioned invasions of privacy by the government, corporations or individuals is part of many countries' privacy laws, and in some cases, constitutions.

In the business world, a person may volunteer personal details, including for advertising, in order to receive some sort of benefit. Public figures may be subject to rules on the public interest. Personal information which is voluntarily shared but subsequently stolen or misused can lead to identity theft.

The concept of universal individual privacy is a modern concept primarily associated with Western culture, British and North American in particular, and remained virtually unknown in some cultures until recent times. Most cultures, however, recognize the ability of individuals to withhold certain parts of their personal information from wider society, such as closing the door to one's home.

In 1890 the United States jurists Samuel D. Warren and Louis Brandeis wrote " The Right to Privacy", an article in which they argued for the "right to be let alone", using that phrase as a definition of privacy. There is extensive commentary over the meaning of being "let alone", and among other ways, it has been interpreted to mean the right of a person to choose seclusion from the attention of others if they wish to do so, and the right to be immune from scrutiny or being observed in private settings, such as one's own home. Although this early vague legal concept did not describe privacy in a way that made it easy to design broad legal protections of privacy, it strengthened the notion of privacy rights for individuals and began a legacy of discussion on those rights.

Limited access refers to a person's ability to participate in society without having other individuals and organizations collect information about them.

Various theorists have imagined privacy as a system for limiting access to one's personal information. Edwin Lawrence Godkin wrote in the late 19th century that "nothing is better worthy of legal protection than private life, or, in other words, the right of every man to keep his affairs to himself, and to decide for himself to what extent they shall be the subject of public observation and discussion." Adopting an approach similar to the one presented by Ruth Gavison Nine years earlier, Sissela Bok said that privacy is "the condition of being protected from unwanted access by others-either physical access, personal information, or attention."

Control over one's personal information is the concept that "privacy is the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others." Generally, a person who has consensually formed an interpersonal relationship with another person is not considered "protected" by privacy rights with respect to the person they are in the relationship with. Charles Fried said that "Privacy is not simply an absence of information about us in the minds of others; rather it is the control we have over information about ourselves. Nevertheless, in the era of big data, control over information is under pressure.

In a way analogous to how the personhood theory imagines privacy as some essential part of being an individual, the intimacy theory imagines privacy to be an essential part of the way that humans have strengthened or intimate relationships with other humans. Because part of human relationships includes individuals volunteering to self-disclose most if not all personal information, this is one area in which privacy does not apply.

James Rachels advanced this notion by writing that privacy matters because "there is a close connection between our ability to control who has access to us and to information about us, and our ability to create and maintain different sorts of social relationships with different people." Protecting intimacy is at the core of the concept of sexual privacy, which law professor Danielle Citron argues should be protected as a unique form of privacy.

Physical privacy could be defined as preventing "intrusions into one's physical space or solitude." An example of the legal basis for the right to physical privacy is the U.S. Fourth Amendment, which guarantees "the right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures".

Government agencies, corporations, groups/societies and other organizations may desire to keep their activities or secrets from being revealed to other organizations or individuals, adopting various security practices and controls in order to keep private information confidential. Organizations may seek legal protection for their secrets. For example, a government administration may be able to invoke executive privilege or declare certain information to be classified, or a corporation might attempt to protect valuable proprietary information as trade secrets.

Privacy has historical roots in philosophical discussions, the most well-known being Aristotle's distinction between two spheres of life: the public sphere of the polis, associated with political life, and the private sphere of the oikos, associated with domestic life. More systematic treatises of privacy in the United States did not appear until the 1890s, with the development of privacy law in America.

As technology has advanced, the way in which privacy is protected and violated has changed with it. In the case of some technologies, such as the printing press or the Internet, the increased ability to share information can lead to new ways in which privacy can be breached. It is generally agreed that the first publication advocating privacy in the United States was the article by Samuel Warren and Louis Brandeis, " The Right to Privacy", that was written largely in response to the increase in newspapers and photographs made possible by printing technologies.

New technologies can also create new ways to gather private information. For example, in the United States it was thought that heat sensors intended to be used to find marijuana-growing operations would be acceptable. However, in 2001 in Kyllo v. United States (533 U.S. 27) it was decided that the use of thermal imaging devices that can reveal previously unknown information without a warrant does indeed constitute a violation of privacy.

This currently has an effect on employment. Microsoft reports that 75 percent of U.S. recruiters and human-resource professionals now do online research about candidates, often using information provided by search engines, social-networking sites, photo/video-sharing sites, personal web sites and blogs, and Twitter. They also report that 70 percent of U.S. recruiters have rejected candidates based on internet information. This has created a need by many to control various online privacy settings in addition to controlling their online reputations, both of which have led to legal suits against various sites and employers.

The ability to do online inquiries about individuals has expanded dramatically over the last decade. Facebook for example, as of August 2015, was the largest social-networking site, with nearly 1,490 million members, who upload over 4.75 billion pieces of content daily. Over 83.09 million accounts were fake. Twitter has more than 316 million registered users and over 20 million are fake users. The Library of Congress recently announced that it will be acquiring-and permanently storing-the entire archive of public Twitter posts since 2006, reports Rosen.

Importantly, directly observed behaviour, such as browsing logs, search queries, or contents of the Facebook profile can be automatically processed to infer secondary information about an individual, such as sexual orientation, political and religious views, race, substance use, intelligence, and personality.

According to some experts, many commonly used communication devices may be mapping every move of their users. Senator Al Franken has noted the seriousness of iPhones and iPads having the ability to record and store users' locations in unencrypted files, although Apple denied doing so.

Andrew Grove, co-founder and former CEO of Intel Corporation, offered his thoughts on internet privacy in an interview published in May 2000:

Privacy is one of the biggest problems in this new electronic age. At the heart of the Internet culture is a force that wants to find out everything about you. And once it has found out everything about you and two hundred million others, that's a very valuable asset, and people will be tempted to trade and do commerce with that asset. This wasn't the information that people were thinking of when they called this the information age.

1. Intrusion into a person's private space, own affairs, or wish for solitude

2. Public disclosure of personal information about a person which could be embarrassing for them to have revealed

3. Promoting access to information about a person which could lead the public to have incorrect beliefs about them

4. Encroaching someone's personality rights, and using their likeness to advance interests which are not their own

data aggregation, which is connecting many related but unconnected pieces of information

identification, which can mean breaking the de-identification of items of data by putting it through a de-anonymization process, thus making facts which were intended to not name particular people to become associated with those people

insecurity, such as lack of data security, which includes when an organization is supposed to be responsible for protecting data instead suffers a data breach which harms the people whose data it held

secondary use, which is when people agree to share their data for a certain purpose, but then the data is used in ways without the data donors' informed consent

exclusion is the use of a person's data without any attempt to give the person an opportunity to manage the data or participate in its usage

Count not him among your friends who will retail your privacies to the world.

Information dissemination is an attack on privacy when information which was shared in confidence is shared or threatened to be shared in a way that harms the subject of the information.

There are various examples of this. Breach of confidentiality is when one entity promises to keep a person's information private, then breaks that promise. Disclosure is making information about a person more accessible in a way that harms the subject of the information, regardless of how the information was collected or the intent of making it available. Exposure is a special type of disclosure in which the information disclosed is emotional to the subject or taboo to share, such as revealing their private life experiences, their nudity, or perhaps private body functions. Increased accessibility means advertising the availability of information without actually distributing it, as in the case of doxxing. Blackmail is making a threat to share information, perhaps as part of an effort to coerce someone. Appropriation is an attack on the personhood of someone, and can include using the value of someone's reputation or likeness to advance interests which are not those of the person being appropriated. Distortion is the creation of misleading information or lies about a person.

Privacy uses the theory of natural rights, and generally responds to new information and communication technologies. In North America, Samuel D. Warren and Louis D. Brandeis wrote that privacy is the "right to be let alone" (Warren & Brandeis, 1890) focuses on protecting individuals. This citation was a response to recent technological developments, such as photography, and sensationalist journalism, also known as yellow journalism.

In recent years there have been only few attempts to clearly and precisely define a "right to privacy." Some experts assert that in fact the right to privacy "should not be defined as a separate legal right" at all. By their reasoning, existing laws relating to privacy in general should be sufficient. It has therefore proposed a working definition for a "right to privacy":

The right to privacy is our right to keep a domain around us, which includes all those things that are part of us, such as our body, home, property, thoughts, feelings, secrets and identity. The right to privacy gives us the ability to choose which parts in this domain can be accessed by others, and to control the extent, manner and timing of the use of those parts we choose to disclose.

David Flaherty believes networked computer databases pose threats to privacy. He develops 'data protection' as an aspect of privacy, which involves "the collection, use, and dissemination of personal information". This concept forms the foundation for fair information practices used by governments globally. Flaherty forwards an idea of privacy as information control, "[i]ndividuals want to be left alone and to exercise some control over how information about them is used".

There have been attempts to establish privacy as one of the fundamental human rights, whose social value is an essential component in the functioning of democratic societies. Amitai Etzioni suggests a communitarian approach to privacy. This requires a shared moral culture for establishing social order. Etzioni believes that "[p]rivacy is merely one good among many others", and that technological effects depend on community accountability and oversight (ibid). He claims that privacy laws only increase government surveillance by weakening informal social controls. Furthermore, the government is no longer the only or even principle threat to people's privacy. Etzioni notes that corporate data miners, or " Privacy Merchants," stand to profit by selling massive dossiers personal information, including purchasing decisions and Internet traffic, to the highest bidder. And while some might not find collection of private information objectionable when it is only used commercially by the private sector, the information these corporations amass and process is also available to the government, so that it is no longer possible to protect privacy by only curbing the State.

Most countries give citizen rights to privacy in their constitutions. Representative examples of this include the Constitution of Brazil, which says "the privacy, private life, honor and image of people are inviolable"; the Constitution of South Africa says that "everyone has a right to privacy"; and the Constitution of the Republic of Korea says "the privacy of no citizen shall be infringed." Among most countries whose constitutions do not explicitly describe privacy rights, court decisions have interpreted their constitutions to intend to give privacy rights.

Many countries have broad privacy laws outside their constitutions, including Australia's Privacy Act 1988, Argentina's Law for the Protection of Personal Data of 2000, Canada's 2000 Personal Information Protection and Electronic Documents Act, and Japan's 2003 Personal Information Protection Law.

Beyond national privacy laws, there are international privacy agreements. The United Nations Universal Declaration of Human Rights says "No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his honor and reputation." The Organisation for Economic Co-operation and Development published its Privacy Guidelines in 1980. The European Union's 1995 Data Protection Directive guides privacy protection in Europe. The 2004 Privacy Framework by the Asia-Pacific Economic Cooperation is a privacy protection agreement for the members of that organization.

In the 1960s people began to consider how changes in technology were bringing changes in the concept of privacy. Vance Packard's The Naked Society was a popular book on privacy from that era and led discourse on privacy at that time.

Approaches to privacy can, broadly, be divided into two categories: free market or consumer protection.

One example of the free market approach is to be found in the voluntary OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data. The principles reflected in the guidelines are analysed in an article putting them into perspective with concepts of the GDPR put into law later in the European Union.

The Privacy Act 1988 is administered by the Office of the Australian Information Commissioner. Privacy law has been evolving in Australia for a number of years. The initial introduction of privacy law in 1998 extended to the public sector, specifically to Federal government departments, under the Information Privacy Principles. State government agencies can also be subject to state based privacy legislation. This built upon the already existing privacy requirements that applied to telecommunications providers (under Part 13 of the Telecommunications Act 1997), and confidentiality requirements that already applied to banking, legal and patient / doctor relationships.

In Italy the right to privacy is enshrined in Article 15 of the Constitution, which states:

In the United Kingdom, it is not possible to bring an action for invasion of privacy. An action may be brought under another tort (usually breach of confidence) and privacy must then be considered under EC law. In the UK, it is sometimes a defence that disclosure of private information was in the public interest. There is, however, the Information Commissioner's Office (ICO), an independent public body set up to promote access to official information and protect personal information. They do this by promoting good practice, ruling on eligible complaints, giving information to individuals and organisations, and taking action when the law is broken. The relevant UK laws include: Data Protection Act 1998; Freedom of Information Act 2000; Environmental Information Regulations 2004; Privacy and Electronic Communications Regulations 2003. The ICO has also provided a "Personal Information Toolkit" online which explains in more detail the various ways of protecting privacy online.

Although the US Constitution does not explicitly include the right to privacy, individual as well as locational privacy are implicitly granted by the Constitution under the 4th Amendment. The Supreme Court of the United States has found that other guarantees have "penumbras" that implicitly grant a right to privacy against government intrusion, for example in Griswold v. Connecticut (1965). In the United States, the right of freedom of speech granted in the First Amendment has limited the effects of lawsuits for breach of privacy. Privacy is regulated in the US by the Privacy Act of 1974, and various state laws. The Privacy Act of 1974 only applies to Federal agencies in the executive branch of the Federal government. Certain privacy rights have been established in the United States via legislation such as the Children's Online Privacy Protection Act (COPPA), the Gramm-Leach-Bliley Act (GLB), and the Health Insurance Portability and Accountability Act (HIPAA).

Unlike the EU and most EU-member states the US does not recognize the right to privacy to others than US citizens.

There are many means to protect one's privacy on the internet. For example, e-mails can be encrypted (via S/MIME or PGP) and anonymizing proxies or anonymizing networks like I2P and Tor can be used to prevent the internet service providers from knowing which sites one visits and with whom one communicates. Covert collection of personally identifiable information has been identified as a primary concern by the U.S. Federal Trade Commission. Although some privacy advocates recommend the deletion of original and third-party HTTP cookies, Anthony Miyazaki, marketing professor at Florida International University and privacy scholar, warns that the "elimination of third-party cookie use by Web sites can be circumvented by cooperative strategies with third parties in which information is transferred after the Web site's use of original domain cookies." As of December 2010, the Federal Trade Commission is reviewing policy regarding this issue as it relates to behavioral advertising. Another aspect of privacy on the Internet relates to online social networking. Several online social network sites (OSNs) are among the top 10 most visited websites globally. A review and evaluation of scholarly work regarding the current state of the value of individuals' privacy of online social networking show the following results: "first, adults seem to be more concerned about potential privacy threats than younger users; second, policy makers should be alarmed by a large part of users who underestimate risks of their information privacy on OSNs; third, in the case of using OSNs and its services, traditional one-dimensional privacy approaches fall short". This is exacerbated by the research indicating that personal traits such as sexual orientation, race, religious and political views, personality, or intelligence can be inferred based on the wide variety of digital footprint, such as samples of text, browsing logs, or Facebook Likes.

In recent years, seen with the increasing importance of mobile devices and paired with the National Do Not Call Registry, telemarketers have turned attention to mobiles.

Privacy self-synchronization is the mode by which the stakeholders of an enterprise privacy program spontaneously contribute collaboratively to the program's maximum success. The stakeholders may be customers, employees, managers, executives, suppliers, partners or investors. When self-synchronization is reached, the model states that the personal interests of individuals toward their privacy is in balance with the business interests of enterprises who collect and use the personal information of those individuals.

Susan B. Barnes similarly used the term "privacy paradox" to refer to the ambiguous boundary between private and public space on social media. When compared to adults, young people tend to disclose more information on social media. However, this does not mean that they are not concerned about their privacy. Susan B. Barnes gave a case in her article: in a television interview about Facebook, a student addressed her concerns about disclosing personal information online. However, when the reporter asked to see her Facebook page, she put her home address, phone numbers, and pictures of her young son on the page.

The willingness to incur a privacy risk is driven by a complex array of factors including risk attitudes, self-reported value for private information, and general attitudes to privacy (derived from surveys). Experiments aiming to determine the monetary value of several types of personal information indicate low evaluations of personal information. On the other hand, it appears that consumers are willing to pay a premium for privacy, albeit a small one. Users do not always act in accordance with their professed privacy concerns and they are sometimes willing to trade private information for convenience, functionality, or financial gain, even when the gains are very small. One of the studies suggest that people think their browser history is worth the equivalent of a cheap meal. Attitudes to privacy risk do not appear to depend on whether it is already under threat or not. People do not either get discouraged in protecting their information, or come to value it more if it is under threat.

Concrete solutions on how to solve paradoxical behavior still do not exist. Many efforts are focused on processes of decision making like restricting data access permissions during the applications installation. However, nothing that would solve the gap between user intention and behavior. Susanne Barth and Menno D.T. de Jong believe that for users to make more conscious decisions on privacy matters the design needs to be more user oriented. Meaning, the ownership of data related risks will be better perceived if psychological ownership of data is being considered as 'mine' rather than 'not mine'.

There are many opinions related to privacy paradox. It is also suggested that it should not be considered a paradox anymore. It's maybe more of a privacy dilemma, because people would like to do more but they also want to use services that would not exist without sharing their data. It is suggested to be, that people do understand that they pay with personal data, but believe they get a fair deal.

Articles, interviews and talks

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Privacy is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively. When something is private to a person, it usually means that something is inherently special or sensitive to them.