  Browser fingerprinting consists into collecting attributes from a web
browser. Hundreds of attributes have been discovered through the years. Each
one of them provides a way to distinguish browsers, but also comes with a
usability cost (e.g., additional collection time). In this work, we propose
FPSelect, an attribute selection framework allowing verifiers to tune their
browser fingerprinting probes for web authentication. We formalize the problem
as searching for the attribute set that satisfies a security requirement and
minimizes the usability cost. The security is measured as the proportion of
impersonated users given a fingerprinting probe, a user population, and an
attacker that knows the exact fingerprint distribution among the user
population. The usability is quantified by the collection time of browser
fingerprints, their size, and their instability. We compare our framework with
common baselines, based on a real-life fingerprint dataset, and find out that
in our experimental settings, our framework selects attribute sets of lower
usability cost. Compared to the baselines, the attribute sets found by FPSelect
generate fingerprints that are up to 97 times smaller, are collected up to
3,361 times faster, and with up to 7.2 times less changing attributes between
two observations, on average.
