End-to-end encrypted messaging services (EEMSs) empower private communication through encrypting messages, yet also make content moderation for combating the spread of abusive messages challenging. There is an urgent call for supporting content moderation in EEMSs while ensuring user privacy. In this paper, we present a new system design for privacy-assured content moderation in EEMSs. At a high level, users in our system can privately report abusive messages, and the EEMS traces the source if a message has an aggregated report count exceeding a predefined threshold and is audited to be abusive. Our system mainly departs from prior works in that it allows flexible and adaptable thresholds, offers robustness against dishonest reporters providing malformed reports, and better ensures the privacy of all users during the moderation process. We also take a step further and propose a privacy-aware detection mechanism that relies on a blocklist built with transparency to mitigate the further spread of identified abusive messages from forwarders. Formal security analysis is provided and extensive experiments demonstrate the practical efficiency of our system.

Enforcing end-to-end data encryption is vital for protecting the interests of requesters in crowdsourcing services, who initiate crowdsourcing tasks and need to pay the service provider and reward workers for the crowdsourced data. It ensures that the data encrypted by workers can be decrypted by the requester only. This yet makes it challenging to protect workers in getting rewards as the data is now only accessible to the requester, who may falsely report workers’ data quality. There is thus an urgent call for enforcing end-to-end data encryption while achieving robustness against such false-reporting. However, this is not yet sufficient for worker protection because most platforms with quality awareness involve a screening process for worker selection in advance, which requires collecting personal worker profiles for assessment against task requirements and raises privacy concerns. In light of the above, we propose PrivRo, a new system framework for privacy-preserving crowdsourcing service with robust quality awareness. PrivRo supports private profile matching for secure screening as well as secure data collection with verifiable quality reporting through custom secure protocols. To our best knowledge, no prior work has simultaneously and adequately supported the secure functionalities compared to PrivRo. Extensive experiments demonstrate the practical efficiency of PrivRo.