We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision.
In the past several years it has been shown @cite_30 that may web sites identify a web client based on super-cookies". These are identifier which are stored on the local host in various persistent ways outside the control of a browser, hence the browser can not impose that standard restriction as of HTTP cookies.
@cite_30
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Many web services aim to track clients as a basis for analyzing their behavior and providing personalized services. Despite much debate regarding the collection of client information, there have been few quantitative studies that analyze the effectiveness of host-tracking and the associated privacy risks. In this paper, we perform a large-scale study to quantify the amount of information revealed by common host identifiers. We analyze month-long anonymized datasets collected by the Hotmail web-mail service and the Bing search engine, which include millions of hosts across the global IP address space. In this setting, we compare the use of multiple identifiers, including browser information, IP addresses, cookies, and user login IDs. We further demonstrate the privacy and security implications of host-tracking in two contexts. In the first, we study the causes of cookie churn in web services, and show that many returning users can still be tracked even if they clear cookies or utilize private browsing. In the second, we show that host-tracking can be leveraged to improve security. Specifically, by aggregating information across hosts, we uncover a stealthy malicious attack associated with over 75,000 bot accounts that forward cookies to distributed locations. This work was done while Ting-Fang was an intern at Microsoft Research. Martin Abadi is also affiliated with the University of California, Santa Cruz. In August 2009, we demonstrated that popular websites were using “Flash cookies” to track users. Some advertisers had adopted this technology because it allowed persistent tracking even where users had taken steps to avoid web profiling. We also demonstrated “respawning” on top sites with Flash technology. This allowed sites to reinstantiate HTTP cookies deleted by a user, making tracking more resistant to users’ privacy-seeking behaviors.In this followup study, we reassess the Flash cookies landscape and examine a new tracking vector, HTML5 local storage and Cache-Cookies via ETags. We found over 5,600 standard HTTP cookies on popular sites, over 4,900 were from third parties. Google-controlled cookies were present on 97 of the top 100 sites, including popular government websites. Seventeen sites were using HTML5, and seven of those sites had HTML5 local storage and HTTP cookies with matching values. Flash cookies were present on 37 of the top 100 sites. We found two sites that were respawning cookies, including one site – hulu.com – where both Flash and cache cookies were employed to make identifiers more persistent. The cache cookie method used ETags, and is capable of unique tracking even where all cookies are blocked by the user and “Private Browsing Mode” is enabled.Our 2009 study is also available at SSRN: http: ssrn.com abstract=1446862. Different countries have different privacy regulatory models. These models impact the perspectives and laws surrounding internet privacy. However, little is known about how effective the regulatory models are when it comes to limiting online tracking and advertising activity. In this paper, we propose a method for investigating tracking behavior by analyzing cookies and HTTP requests from browsing sessions originating in different countries. We collect browsing data from visits to top websites in various countries that utilize different regulatory models. We found that there are significant differences in tracking activity between different countries using several metrics. We also suggest various ways to extend this study which may yield a more complete representation of tracking from a global perspective. This article provides a quantitative analysis of privacy-compromising mechanisms on 1 million popular websites. Findings indicate that nearly 9 in 10 websites leak user data to parties of which the user is likely unaware; more than 6 in 10 websites spawn third-party cookies; and more than 8 in 10 websites load Javascript code from external parties onto users' computers. Sites that leak user data contact an average of nine external domains, indicating that users may be tracked by multiple entities in tandem. By tracing the unintended disclosure of personal browsing histories on the Web, it is revealed that a handful of U.S. companies receive the vast bulk of user data. Finally, roughly 1 in 5 websites are potentially vulnerable to known National Security Agency spying techniques at the time of analysis.