Partial Device Fingerprints

Abstract

In computing, remote devices may be identified by means of device fingerprinting, which works by collecting a myriad of client-side attributes such as the device’s browser and operating system version, installed plugins, screen resolution, hardware artifacts, Wi-Fi settings, and anything else available to the server, and then merging these attributes into uniquely identifying fingerprints. This technique is used in practice to present personalized content to repeat website visitors, detect fraudulent users, and stop masquerading attacks on local networks. However, device fingerprints are seldom uniquely identifying. They are better viewed as partial device fingerprints, which do have some discriminatory power but not enough to uniquely identify users. How can we infer from partial fingerprints whether different observations belong to the same device? We present a mathematical formulation of this problem that enables probabilistic inference of the correspondence of observations. We set out to estimate a correspondence probability for every pair of observations that reflects the plausibility that they are made by the same user. By extending probabilistic data association techniques previously used in object tracking, traffic surveillance and citation matching, we develop a general-purpose probabilistic method for estimating correspondence probabilities with partial fingerprints. Our approach exploits the natural variation in fingerprints and allows for use of situation-specific knowledge through the specification of a generative probability model. Experiments with a real-world dataset show that our approach gives calibrated correspondence probabilities. Moreover, we demonstrate that improved results can be obtained by combining device fingerprints with behavioral models.