Hiding in the crowd: An analysis of the effectiveness of browser fingerprinting at large scale

Abstract

Browser fingerprinting is a stateless technique, which consists in collecting a wide range of data about a device through browser APIs. Past studies have demonstrated that modern devices present so much diversity that fingerprints can be exploited to identify and track users online. With this work, we want to evaluate if browser fingerprinting is still effective at uniquely identifying a large group of users when analyzing millions of fingerprints over a few months. We collected 2,067,942 browser fingerprints from one of the top 15 French websites. The analysis of this novel dataset sheds a new light on the ever-growing browser fingerprinting domain. The key insight is that the percentage of unique fingerprints in our dataset is much lower than what was reported in the past: only 33.6% of fingerprints are unique by opposition to over 80% in previous studies. We show that non-unique fingerprints tend to be fragile. If some features of the fingerprint change, it is very probable that the fingerprint will become unique. We also confirm that the current evolution of web technologies is benefiting users» privacy significantly as the removal of plugins brings down substantively the rate of unique desktop machines.