Third-Party Web Tracking: Policy and Technology

Mayer, Jonathan; Mitchell, John C.

doi:10.1109/sp.2012.47

Cited by 372 publications

(300 citation statements)

References 32 publications

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“…‡ ‡ Prior work has demonstrated that the policy distinction between PII and non-PII is not based on sound science. Researchers have demonstrated "reidentification" risks in a number of applications, including health records (21,22), location histories (23)(24)(25), web search queries (26), web browsing activity (27)(28)(29), movie reviews (30), and social network graphs (31,32).…”

Section: Resultsmentioning

confidence: 99%

Evaluating the privacy properties of telephone metadata

Mayer

Mutchler

Mitchell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Since 2013, a stream of disclosures has prompted reconsideration of surveillance law and policy. One of the most controversial principles, both in the United States and abroad, is that communications metadata receives substantially less protection than communications content. Several nations currently collect telephone metadata in bulk, including on their own citizens. In this paper, we attempt to shed light on the privacy properties of telephone metadata. Using a crowdsourcing methodology, we demonstrate that telephone metadata is densely interconnected, can trivially be reidentified, and can be used to draw sensitive inferences.C ommunications privacy law, in the United States and many other nations, draws a distinction between "content" and "metadata" (1). The former category reflects the substance of an electronic communication; the latter includes all other information about the communication, such as parties, time, and duration (2).* When a government agency compels disclosure of content, the agency must usually comply with extensive substantive and procedural safeguards. Demands for metadata, by contrast, are often left to the near-total discretion of authorities. In the United States, for instance, a law enforcement officer can request telephone calling records with merely a subpoena-essentially a formal letter from the investigating agency (3). An intelligence program by the National Security Agency (NSA) has drawn particular criticism; under the business records provision of the USA PATRIOT Act (4), the agency acquired a substantial share of all domestic telephone metadata (5). † In this paper, we empirically investigate factual assumptions that undergird policies of differential treatment for content and metadata. Using crowdsourced telephone logs and social networking information, we find that telephone metadata is densely interconnected, susceptible to reidentification, and enables highly sensitive inferences. ‡ The balance of the paper is organized into three parts. First, we discuss our data collection methodology and properties of our participant population. We next present our results. Finally, we discuss implications for policy and future quantitative social science research. Additional methodological detail and figures are available in the Supporting Information. MethodsWe collected the data in this study through an Android smartphone application ( Fig. 1). § Potential participants could discover the project through academic websites, the Google Play store, and references in media coverage. The application automatically retrieved historical call and text message [Short Message Service (SMS)] metadata from device logs. { In addition, the application retrieved information from a participant's Facebook account, to be used as ground truth for potential inferences. # Participants were provided an opportunity to view individualized features of their phone metadata, and then they were invited to uninstall the application. In total, 823 participants volunteered their metadata, which included 251,7...

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Section: Resultsmentioning

confidence: 99%

Evaluating the privacy properties of telephone metadata

Mayer

Mutchler

Mitchell

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…For example, due to privacy concerns, much interest exists in determining how thirdparty data collection agencies use the information they collect. (See [51] for a survey.) Despite being a question of information flow, program analyses are inapplicable since the programs are unavailable, as in our setting.…”

Section: Resultsmentioning

confidence: 99%

Purpose Restrictions on Information Use

Tschantz

Datta

Wing

2013

Lecture Notes in Computer Science

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Abstract. Privacy policies in sectors as diverse as Web services, finance and healthcare often place restrictions on the purposes for which a governed entity may use personal information. Thus, automated methods for enforcing privacy policies require a semantics of purpose restrictions to determine whether a governed agent used information for a purpose. We provide such a semantics using a formalism based on planning. We model planning using Partially Observable Markov Decision Processes (POMDPs), which supports an explicit model of information. We argue that information use is for a purpose if and only if the information is used while planning to optimize the satisfaction of that purpose under the POMDP model. We determine information use by simulating ignorance of the information prohibited by the purpose restriction, which we relate to noninterference. We use this semantics to develop a sound audit algorithm to automate the enforcement of purpose restrictions.

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“…4 In particular, using several different Facebook accounts with distinctly different user profiles, we install and accept the requested permissions for each of the 997 applications. To monitor the application behavior, we use a modified version of a Firefox plug-in [7], allowing us to record all the HTTP and HTTPS traffic. Similarly, we investigated each of the 377 working applications listed on the RenRen App center.…”

Section: Methodsmentioning

confidence: 99%

A Closer Look at Third-Party OSN Applications: Are They Leaking Your Personal Information?

Chaabane

Yuan

Dey

et al. 2014

Passive and Active Measurement

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Abstract. We examine third-party Online Social Network (OSN) applications for two major OSNs: Facebook and RenRen. These third-party applications typically gather, from the OSN, user personal information. We develop a measurement platform to study the interaction between OSN applications and fourth parties. We use this platform to study the behavior of 997 Facebook applications and 377 RenRen applications. We find that the Facebook and RenRen applications interact with hundreds of different fourth-party tracking entities. More worrisome, 22% of Facebook applications and 69% of RenRen applications provide users' personal information to one or more fourth-party tracking entities.

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Third-Party Web Tracking: Policy and Technology

Cited by 372 publications

References 32 publications

Evaluating the privacy properties of telephone metadata

Evaluating the privacy properties of telephone metadata

Purpose Restrictions on Information Use

A Closer Look at Third-Party OSN Applications: Are They Leaking Your Personal Information?

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