Biblio

In this paper we examine the use of covert channels based on CPU load in order to achieve persistent user identification through browser sessions. In particular, we demonstrate that an HTML5 video, a GIF image, or CSS animations on a webpage can be used to force the CPU to produce a sequence of distinct load levels, even without JavaScript or any client-side code. These load levels can be then captured either by another browsing session, running on the same or a different browser in parallel to the browsing session we want to identify, or by a malicious app installed on the device. To get a good estimation of the CPU load caused by the target session, the receiver can observe system statistics about CPU activity (app), or constantly measure time it takes to execute a known code segment (app and browser). Furthermore, for mobile devices we propose a sensor-based approach to estimate the CPU load, based on exploiting disturbances of the magnetometer sensor data caused by the high CPU activity. Captured loads can be decoded and translated into an identifying bit string, which is transmitted back to the attacker. Due to the way loads are produced, these methods are applicable even in highly restrictive browsers, such as the Tor Browser, and run unnoticeably to the end user. Therefore, unlike existing ways of web tracking, our methods circumvent most of the existing countermeasures, as they store the identifying information outside the browsing session being targeted. Finally, we also thoroughly evaluate and assess each presented method of generating and receiving the signal, and provide an overview of potential countermeasures.

Recent works showed that websites can detect browser extensions that users install and websites they are logged into. This poses significant privacy risks, since extensions and Web logins that reflect user's behavior, can be used to uniquely identify users on the Web. This paper reports on the first large-scale behavioral uniqueness study based on 16,393 users who visited our website. We test and detect the presence of 16,743 Chrome extensions, covering 28% of all free Chrome extensions. We also detect whether the user is connected to 60 different websites. We analyze how unique users are based on their behavior, and find out that 54.86% of users that have installed at least one detectable extension are unique; 19.53% of users are unique among those who have logged into one or more detectable websites; and 89.23% are unique among users with at least one extension and one login. We use an advanced fingerprinting algorithm and show that it is possible to identify a user in less than 625 milliseconds by selecting the most unique combinations of extensions. Because privacy extensions contribute to the uniqueness of users, we study the trade-off between the amount of trackers blocked by such extensions and how unique the users of these extensions are. We have found that privacy extensions should be considered more useful than harmful. The paper concludes with possible countermeasures.

This paper discusses possible approaches to address the loss of user privacy when browsing the web and being tracked by websites which compute a browser fingerprint identifying the user computer. The key problem is that the current fingerprinting countermeasures are insufficient to prevent fingerprinting tracking and also frequently produce side-effects on the web browser. The advantages and disadvantages of possible countermeasures are discussed in the context of improving resistance against browser fingerprinting. Finally, using a new browser extension is proposed as the best way to inhibit fingerprinting as it could probably inhibit some of the fingerprinting techniques used and also diminish the side-effects on the user browser experience, compared with existing techniques.