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While privacy-focused browsers have taken steps to block third-party cookiesand browser fingerprinting, novel tracking methods that bypass existingdefenses continue to emerge. Since trackers need to exfiltrate information fromthe client- to server-side through link decoration regardless of the trackingtechnique they employ, a promising orthogonal approach is to detect andsanitize tracking information in decorated links. We present PURL, amachine-learning approach that leverages a cross-layer graph representation ofwebpage execution to safely and effectively sanitize link decoration. Ourevaluation shows that PURL significantly outperforms existing countermeasuresin terms of accuracy and reducing website breakage while being robust to commonevasion techniques. We use PURL to perform a measurement study on top-millionwebsites. We find that link decorations are widely abused by well-knownadvertisers and trackers to exfiltrate user information collected from browserstorage, email addresses, and scripts involved in fingerprinting.

PURL: Safe and Effective Sanitization of Link Decoration