Enforcing Browser Anonymity with Quantitative Information Flow

Web tracking companies use device fingerprinting to distinguish the users of the websites by checking the numerous properties of their machines and web browsers. One way to protect the users' privacy is to make them switch between different machine and browser configurations. We propose a formalization of this privacy enforcement mechanism. We use information-theoretic channels to model the knowledge of the tracker and the fingerprinting program, and show how to synthesise a randomisation mechanism that defines the distribution of configurations for each user. This mechanism provides a strong guarantee of privacy (the probability of identifying the user is bounded by a given threshold) while maximising usability (the user switches to other configurations rarely). To find an optimal solution, we express the enforcement problem of randomization by a linear program. We investigate and compare several approaches to randomization and find that more efficient privacy enforcement would often provide lower usability. Finally, we relax the requirement of knowing the fingerprinting program in advance, by proposing a randomisation mechanism that guarantees privacy for an arbitrary program.