"Today, people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their home location, which insurance companies do, with an accuracy that constitutes privacy intrusion."
"The increasing availability of personal activity monitors, tracking devices, wearable recording devices, and associated smartphone apps has given rise to a wave of Quantified Self individuals
and applications. The data from these apps and sensors are usually collected by associated apps and uploaded to the software developers for feedback to the individual and selected partners. In this
article we highlight the privacy risks associated with this practice, demonstrating the ease with which an app provider can infer an individual’s co-location and joint activities without having access to
specific location data. We also highlight a number of potential solutions to the challenges that arise, with a view to minimising the privacy leakage from these applications."
"Ton Siedsma is nervous. He made the decision weeks ago, but keeps postponing it. It’s the 11th of November, a cold autumn evening. At ten past eight (20:10:48 to be exact), while passing Elst
station on the way home, he activates the app. It will track all of his phone’s metadata over the coming week. […] After exactly a week, on Monday, 18 November, he concludes the experiment, saying
afterwards that he felt liberated when doing so. There’s an easy explanation for his nervousness: what he’ll be doing, where he’ll be and who he has contact with will be seen by tens of thousands of
people. Today, by you and me, and all the other readers of this article."
"In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary
valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls
made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e.
meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal
information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of
mobile services and future markets of personal data."
"Fourteen mobile industry companies and 10 in-car navigation providers that GAO examined in its 2012 and 2013 reports—including mobile carriers and auto manufacturers with the largest market share and popular application developers—collect location data and use or share them to provide consumers with location-based services and improve consumer services. For example, mobile carriers and application developers use location data to provide social networking services that are linked to consumers’ locations. In-car navigation services use location data to provide services such as turn-by-turn directions or roadside assistance. Location data can also be used and shared to enhance the functionality of other services, such as search engines, to make search results more relevant by, for example, returning results of nearby businesses. While consumers can benefit from location-based services, their privacy may be at risk when companies collect and share location data. For example, in both reports, GAO found that when consumers are unaware their location data are shared and for what purpose data might be shared, they may be unable to judge whether location data are shared with trustworthy third parties. Furthermore, when location data are amassed over time, they can create a detailed profile of individual behavior, including habits, preferences, and routes traveled—private information that could be exploited. Additionally, consumers could be at higher risk of identity theft or threats to personal safety when companies retain location data for long periods or in a way that links the data to individual consumers. Companies can anonymize location data that they use or share, in part, by removing personally identifying information; however, in its 2013 report, GAO found that in-car navigation providers that GAO examined use different de-identification methods that may lead to varying levels of protection for consumers."
"Since 1967, when it decided Katz v. United States, the Supreme Court has tied the right to be free of unwanted govern-ment scrutiny to the concept of reasonable expectations of privacy. An
evaluation of reasonable expectations depends, among other factors, upon an assessment of the intrusiveness of government ac-tion. When making such assessment historically the Court consid-ered police
conduct with clear temporal, geographic, or substantive limits. However, in an era where new technologies permit the storage and compilation of vast amounts of personal data, things are becoming more
complicated. A school of thought known as ‘mosaic theory’ has stepped into the void, ringing the alarm that our old tools for assessing the intrusiveness of government conduct potentially undervalue
privacy rights. Mosaic theorists advocate a cumulative approach to the evaluation of data collection. Under the theory, searches are ‘analyzed as a collective sequence of steps rather than as individual
steps.’ The approach is based on the observation that comprehensive aggregation of even seemingly innocuous data reveals greater insight than consideration of each piece of information in isolation. Over
time, discrete units of surveillance data can be processed to create a mosaic of habits, relationships, and much more. Consequently, a Fourth Amendment analysis that focuses only on the government’s
collection of discrete units of data fails to appreciate the true harm of long-term surveillance—the composite."
"In the wake of the NSA revelations, there has been an avalanche of state bills requiring law enforcement to obtain a probable cause warrant before tracking an individual’s location in an
investigation. Most state legislators know they can’t control the NSA—but they can control their state and local law enforcement, which are engaging in some of the same invasive practices. […] Working
closely with our lobbyists in state capitols around the country, we’ve been tracking this activity and working hard to make sure these privacy-protective bills become law. The chart below shows the
current status of state legislation as we understand it. We will keep this chart up-to-date as we receive new information."
"We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users’ tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users."
"In this paper, we examine the application of Privacy by Design to the design and architecture of MLA systems through the work of Toronto-based MLA company Aislelabs. […] This paper has in
total four sections. It begins with a background discussion of MLA and how it works technologically (section 2). Next the paper discusses the unique privacy risks associated with MLA (section 3). Finally,
it introduces Privacy by Design, discusses Aislelabs’ MLA implementation, and shows how it designs in privacy from the outset (section 4)."
"Limited privacy protections for metadata may have made sense decades ago when technology to collect and analyze data was virtually nonexistent. But in today’s ‘big data’ world, non-content
does not mean non-sensitive. In fact, new technology is demonstrating just how sensitive metadata can be: how friend lists can reveal a person’s sexual orientation, purchase histories can identify a
pregnancy before any visible signs appear, and location information can expose individuals to harassment for unpopular political views or even theft and physical harm. Two separate committees assembled by
the executive branch — the President’s Review Group on Intelligence and Communications Technology and the Privacy and Civil Liberties Oversight Board —have joined lawmakers, academics, and judges in
calling for a reevaluation of the distinction between content and metadata. This paper examines how new technologies and outdated laws have combined to make metadata more important and more vulnerable
than ever, and proposes a way forward to ensure that all of our sensitive information gets the privacy protection it deserves."
"We now know that the NSA is collecting location information en masse. As we’ve long said, location data is an extremely powerful set of information about people. To flesh out why that is
true, here is the kind of future memo that we fear may someday soon be uncovered: […]"
"Mr. Zhang is a client of Turnstyle Solutions Inc., a year-old local company that has placed sensors in about 200 businesses within a 0.7 mile radius in downtown Toronto to track shoppers as
they move in the city. The sensors, each about the size of a deck of cards, follow signals emitted from Wi-Fi-enabled smartphones. That allows them to create portraits of roughly 2 million people’s habits
as they have gone about their daily lives, traveling from yoga studios to restaurants, to coffee shops, sports stadiums, hotels, and nightclubs."
"There are actually a surprising number of different ways law enforcement agencies can track and get information about phones, each of which exposes different information in different ways. And it’s all steeped in arcane surveillance jargon that’s evolved over decades of changes in the law and the technology. So now seems like a good time to summarize what the various phone tapping methods actually are, how they work, and how they differ from one another."
"This report addresses (1) what selected companies that provide in-car location-based services use location data for and if they share the data, and (2) how these companies’ policies and reported practices align with industry-recommended privacy practices. GAO selected a non-generalizable sample of 10 companies. The companies were selected because they represent the largest U.S. market share or because their services are widely used."
"A YouGov survey commissioned by the ICO in December has highlighted that concerns around how apps are using people’s personal information is hitting developer’s sales and usage figures. The survey found that 62% of people who have downloaded an app are concerned about the way apps use personal information, with almost half (49%) of app users having chosen not to download an app due to privacy concerns. […] It’s clear then, that as well as fulfilling a legal requirement, it is in developers’ interests to make sure they are looking after people’s information correctly by complying with the Data Protection Act. To help them achieve this we have published detailed guidance today that was developed in consultation with key figures within the industry, including academics and other regulators. The guidance explains the key requirements that developers must meet when processing personal information through an app, covering issues such as security and data retention."