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Recently proposed systems aim at achieving privacy using locality-sensitivehashing. We show how these approaches fail by presenting attacks against twosuch systems: Google's FLoC proposal for privacy-preserving targetedadvertising and the MinHash Hierarchy, a system for processing mobile users'traffic behavior in a privacy-preserving way. Our attacks refute the pre-imageresistance, anonymity, and privacy guarantees claimed for these systems.  In the case of FLoC, we show how to deanonymize users using Sybil attacks andto reconstruct 10% or more of the browsing history for 30% of its users usingGenerative Adversarial Networks. We achieve this only analyzing the hashes usedby FLoC. For MinHash, we precisely identify the movement of a subset ofindividuals and, on average, we can limit users' movement to just 10% of thepossible geographic area, again using just the hashes. In addition, we refutetheir differential privacy claims.

Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System