[2302.13635] Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System

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Computer Science > Cryptography and Security

arXiv:2302.13635 (cs)

[Submitted on 27 Feb 2023]

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

Authors:Florian Turati (1), Carlos Cotrini (1), Karel Kubicek (1), David Basin (1) ((1) ETH Zurich)<br>View a PDF of the paper titled Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System, by Florian Turati (1) and 3 other authors

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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 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.

Comments:<br>14 pages, 9 figures submitted to PETS 2023

Subjects:

Cryptography and Security (cs.CR)

ACM classes:<br>K.6.5

Cite as:<br>arXiv:2302.13635 [cs.CR]

(or<br>arXiv:2302.13635v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2302.13635

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arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.56553/popets-2023-0101

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DOI(s) linking to related resources

Submission history<br>From: Karel Kubicek [view email]<br>[v1]<br>Mon, 27 Feb 2023 10:03:32 UTC (2,631 KB)

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