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

Abstract

Recently proposed systems aim at achieving privacy using locality-sensitive hashing. We show how these approaches fail by presenting attacks against two such systems: Google's FLoC proposal for privacy-preserving targeted advertising and the MinHash Hierarchy, a system for processing location trajectories in a privacy-preserving way. Our attacks refute the pre-image resistance, anonymity, and privacy guarantees claimed for these systems. In the case of FLoC, we show how to deanonymize users using Sybil attacks and to reconstruct 10% or more of the browsing history for 30% of its users using Generative Adversarial Networks. We achieve this only analyzing the hashes used by FLoC. For MinHash, we precisely identify the location trajectory of a subset of individuals and, on average, we can limit users' trajectory to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims.

Zero Knowledge Bi-Party Computation Using Oblivious Transfers for Recommender Systems

“…The paper [2] explains how to perform user Aggregation and cohort selection based on user browser history.…”

Zero Knowledge Bi-Party Computation Using Oblivious Transfers for Recommender Systems

“…The paper [2] explains how to perform user Aggregation and cohort selection based on user browser history. This indicates the possibility of a more secure architecture with relative protection of the user's personal information due to k-anonymity.…”