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

arXiv: https://arxiv.org/abs/2302.13635 · HTML: https://mlsanity.com/paper/2302.13635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Differentially Private Datastore Generation for Retrieval-Augmented Inference](https://mlsanity.com/paper/2606.01413/) (2026)

## Abstract (arXiv)

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 mobile users' traffic behavior 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 movement of a subset of individuals and, on average, we can limit users' movement to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims.