Reconciling Safety and Privacy: A Systematic Review of Moderation in End-to-End Encrypted Messaging

End-to-end encrypted (E2EE) communications present a fundamental tension between user privacy and the ability to moderate harmful content. This work systematizes the range of techniques proposed to perform content moder-ation in E2EE systems while preserving security guarantees. We examine methods including user reporting (e.g., message franking), metadata-based detection, content matching via per-ceptual hashing, and machine learning classifiers, detailing their mechanisms and inherent trade-offs. Our analysis finds that currently deployed approaches often compromise some aspect of E2EE: for instance, user-driven reporting and metadata analysis largely uphold privacy but depend on user participation, whereas more automated schemes like traceability and client-side content scanning can undermine privacy or deniability and are susceptible to evasion. In response, researchers have developed new cryptographic primitives (e.g., committing encryption, pri-vate membership tests) and privacy-preserving AI techniques to better reconcile moderation with encryption. We evaluate these emerging solutions, outline their security guarantees, highlight limitations (such as adversarial attacks on perceptual hashes and unsolved scalability challenges), and identify which strategies are most promising under various scenarios. Our systematization clarifies the state of the art and points to concrete directions for future research to enable safer messaging platforms without weakening E2EE protections.