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Browser fingerprinting consists into collecting attributes from a webbrowser. Hundreds of attributes have been discovered through the years. Eachone of them provides a way to distinguish browsers, but also comes with ausability cost (e.g., additional collection time). In this work, we proposeFPSelect, an attribute selection framework allowing verifiers to tune theirbrowser fingerprinting probes for web authentication. We formalize the problemas searching for the attribute set that satisfies a security requirement andminimizes the usability cost. The security is measured as the proportion ofimpersonated users given a fingerprinting probe, a user population, and anattacker that knows the exact fingerprint distribution among the userpopulation. The usability is quantified by the collection time of browserfingerprints, their size, and their instability. We compare our framework withcommon baselines, based on a real-life fingerprint dataset, and find out thatin our experimental settings, our framework selects attribute sets of lowerusability cost. Compared to the baselines, the attribute sets found by FPSelectgenerate fingerprints that are up to 97 times smaller, are collected up to3,361 times faster, and with up to 7.2 times less changing attributes betweentwo observations, on average.

FPSelect: Low-Cost Browser Fingerprints for Mitigating Dictionary Attacks against Web Authentication Mechanisms