Worldwide, the number of people and the time spent browsing the web keeps increasing. Accordingly, the technologies to enrich the user experience are evolving at an amazing pace. Many of these evolutions provide for a more interactive web (e.g., boom of JavaScript libraries, weekly innovations in HTML5), a more available web (e.g., explosion of mobile devices), a more secure web (e.g., Flash is disappearing, NPAPI plugins are being deprecated), and a more private web (e.g., increased legislation against cookies, huge success of extensions such as Ghostery and AdBlock). Nevertheless, modern browser technologies, which provide the beauty and power of the web, also provide a darker side, a rich ecosystem of exploitable data that can be used to build unique browser fingerprints. Our work explores the validity of browser fingerprinting in today's environment. Over the past year, we have collected 118,934 fingerprints composed of 17 attributes gathered thanks to the most recent web technologies. We show that innovations in HTML5 provide access to highly discriminating attributes, notably with the use of the Canvas API which relies on multiple layers of the user's system. In addition, we show that browser fingerprinting is as effective on mobile devices as it is on desktops and laptops, albeit for radically different reasons due to their more constrained hardware and software environments. We also evaluate how browser fingerprinting could stop being a threat to user privacy if some technological evolutions continue (e.g., disappearance of plugins) or are embraced by browser vendors (e.g., standard HTTP headers).

With this paper, we survey the research performed in the domain of browser fingerprinting, while providing an accessible entry point to newcomers in the field. We explain how this technique works and where it stems from. We analyze the related work in detail to understand the composition of modern fingerprints and see how this technique is currently used online. We systematize existing defense solutions into different categories and detail the current challenges yet to overcome.

Abstract-Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed.In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking.FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98, 598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.

The rich programming interfaces (APIs) provided by web browsers can be diverted to collect a browser fingerprint. A small number of queries on these interfaces are sufficient to build a fingerprint that is statistically unique and very stable over time. Consequently, the fingerprint can be used to track users. Our work aims at mitigating the risk of browser fingerprinting for users privacy by 'breaking' the stability of a fingerprint over time. We add randomness in the computation of selected browser functions, in order to have them deliver slightly different answers for each browsing session. Randomization is possible thanks to the following properties of browsers implementations: (i) some functions have a nondeterministic specification, but a deterministic implementation; (ii) multimedia functions can be slightly altered without deteriorating user's perception. We present FPRandom, a modified version of Firefox that adds randomness to mitigate the most recent fingerprinting algorithms, namely canvas fingerprinting, AudioContext fingerprinting and the unmasking of browsers through the order of JavaScript properties. We evaluate the effectiveness of FPRandom by testing it against known fingerprinting tests. We also conduct a user study and evaluate the performance overhead of randomization to determine the impact on the user experience.