Anunay Kulshrestha and Jonathan Mayer, Princeton University
End-to-end encryption (E2EE) poses a challenge for automated detection of harmful media, such as child sexual abuse material and extremist content. The predominant approach at present, perceptual hash matching, is not viable because in E2EE a communications service cannot access user content.
In this work, we explore the technical feasibility of privacy-preserving perceptual hash matching for E2EE services. We begin by formalizing the problem space and identifying fundamental limitations for protocols. Next, we evaluate the predictive performance of common perceptual hash functions to understand privacy risks to E2EE users and contextualize errors associated with the protocols we design.
Our primary contribution is a set of constructions for privacy-preserving perceptual hash matching. We design and evaluate client-side constructions for scenarios where disclosing the set of harmful hashes is acceptable. We then design and evaluate interactive protocols that optionally protect the hash set and do not disclose matches to users. The constructions that we propose are practical for deployment on mobile devices and introduce a limited additional risk of false negatives.
Open Access Media
USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.