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Recent years have seen a strong uptick in both the prevalence and real-worldconsequences of false information spread through online platforms. At the sametime, encrypted messaging systems such as WhatsApp, Signal, and Telegram, arerapidly gaining popularity as users seek increased privacy in their digitallives.  The challenge we address is how to combat the viral spread of misinformationwithout compromising privacy. Our FACTS system tracks user complaints onmessages obliviously, only revealing the message's contents and originator oncesufficiently many complaints have been lodged.  Our system is private, meaning it does not reveal anything about the sendersor contents of messages which have received few or no complaints; secure,meaning there is no way for a malicious user to evade the system or gain anoutsized impact over the complaint system; and scalable, as we demonstrateexcellent practical efficiency for up to millions of complaints per day.  Our main technical contribution is a new collaborative counting Bloom filter,a simple construction with difficult probabilistic analysis, which may haveindependent interest as a privacy-preserving randomized count sketch datastructure.  Compared to prior work on message flagging and tracing in end-to-endencrypted messaging, our novel contribution is the addition of a high thresholdof multiple complaints that are needed before a message is audited or flagged.  We present and carefully analyze the probabilistic performance of our datastructure, provide a precise security definition and proof, and then measurethe accuracy and scalability of our scheme via experimentation.

Fighting Fake News in Encrypted Messaging with the Fuzzy Anonymous Complaint Tally System (FACTS)