In end-to-end encrypted (E2EE) messaging systems, protecting communication metadata, such as who is communicating with whom, at what time, etc., remains a challenging problem. Existing designs mostly fall into the balancing act among security, performance, and trust assumptions: 1) designs with cryptographic security often use hefty operations, incurring performance roadblocks and expensive operational costs for large-scale deployment; 2) more performant systems often follow a weaker security guarantee, like differential privacy, and generally demand more trust from the involved servers. So far, there has been no dominant solution. In this paper, we take a different technical route from prior art, and propose Boomerang, an alternative metadata-private messaging system leveraging the readily available trust assumption on secure enclaves (as those emerging in the cloud). Through a number of carefully tailored oblivious techniques on message shuffling, workload distribution, and proactive patching of the communication pattern, Boomerang brings together low latency, horizontal scalability, and cryptographic security, without prohibitive extra cost. With 32 machines, Boomerang achieves 99th percentile latency of 7.76 seconds for$2^{20}$clients. Upon Boomerang, we also propose and implement a new client instantiation based on modern web browser extensions. We hope Boomerang offers attractive alternative options to the current landscape of metadata-private messaging designs.

SONICUMOS is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a reliable liveness detection system is paramount. Traditional behavior-based liveness detection methods (e.g., eye-blinking, nodding, etc.), which are widely deployed in mission-critical scenarios like finance and banking applications today, are prone to advanced media-based facial forgery attacks. SONICUMOS aims to incorporate the traditional behaviorbased method for active liveness detection without introducing extra user burden. By employing ultrasonic signals, SONICUMOS capitalizes on the head gestures, significantly raising the security bar. Our approach utilizes the frequency-modulated continuouswave (FMCW) ultrasonic radar for robust 3D gesture recognition compatible with face authentication. We also propose a new dual-feature fusion network that integrates audio and video features at the feature level to increase detection accuracy and resilience against numerous attacks. Our prototype has been tested on seven off-the-shelf Android/iOS smartphones, achieving an overall detection accuracy of 95.83% at an equal error rate (EER) of 4.96% when dealing with 3D impersonation attacks