Paper 2021/1180

The Effect of False Positives: Why Fuzzy Message Detection Leads to Fuzzy Privacy Guarantees?

István András Seres and Balázs Pejó and Péter Burcsi

Abstract

Fuzzy Message Detection (FMD) is a recent cryptographic primitive invented by Beck et al. (CCS'21) where an untrusted server performs coarse message filtering for its clients in a recipient-anonymous way. In FMD - besides the true positive messages - the clients download from the server their cover messages determined by their false-positive detection rates. What is more, within FMD, the server cannot distinguish between genuine and cover traffic. In this paper, we formally analyze the privacy guarantees of FMD from four different angles. First, we evaluate what privacy provisions are offered by FMD. We found that FMD does not provide relationship anonymity without additional cryptographic techniques protecting the senders' identities. Moreover, FMD only provides a reasonable degree of recipient unlinkability when users apply considerable false-positive rates, and concurrently there is significant traffic. Second, we perform a differential privacy (DP) analysis and coin a relaxed DP definition to capture the privacy guarantees FMD yields. Third, we study FMD through a game-theoretic lens and argue why FMD is not sustainable without altruistic users. Finally, we simulate FMD on real-world communication data. Our theoretical and empirical results assist FMD users to adequately select their false-positive detection rates for various applications with given privacy requirements.