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.

Microdata is collected by companies in order to enhance their quality of service as well as the accuracy of their recommendation systems. These data often become publicly available after they have been sanitized. Recent reidentification attacks on publicly available, sanitized datasets illustrate the privacy risks involved in microdata collections. Currently, users have to trust the provider that their data will be safe in case data is published or if a privacy breach occurs. In this work, we empower users by developing a novel, user-centric tool for privacy measurement and a new lightweight privacy metric. The goal of our tool is to estimate users' privacy level prior to sharing their data with a provider. Hence, users can consciously decide whether to contribute their data. Our tool estimates an individuals' privacy level based on published popularity statistics regarding the items in the provider's database, and the users' microdata. In this work, we describe the architecture of our tool as well as a novel privacy metric, which is necessary for our setting where we do not have access to the provider's database. Our tool is user friendly, relying on smart visual results that raise privacy awareness. We evaluate our tool using three real world datasets, collected from major providers. We demonstrate strong correlations between the average anonymity set per user and the privacy score obtained by our metric. Our results illustrate that our tool which uses minimal information from the provider, estimates users' privacy levels comparably well, as if it had access to the actual database.