G. Edward Suh

This paper provides a comprehensive, system-level comparison of MPC and FHE for Privacy-Preserving Machine Learning (PPML) across various models and environments, moving beyond single-metric latency analysis.

The paper proposes a vision for system-level defenses against indirect prompt injection attacks targeting AI agents, emphasizing structured control and human oversight.

GPIR is a GPU-accelerated Private Information Retrieval (PIR) system that significantly boosts throughput by introducing a stage-aware hybrid execution model and optimizing data layouts for modern GPU architectures.

Privatar introduces a scalable, privacy-preserving framework to offload computationally intensive multi-user avatar reconstruction from VR headsets to untrusted local devices, significantly improving user capacity while maintaining strong privacy guarantees.

Onyx proposes a novel, cost-efficient disk-oblivious Approximate Nearest Neighbor (ANN) search system that significantly reduces both cost and latency compared to state-of-the-art methods.