4 papers
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
Harish Karthikeyan, Antigoni Polychroniadou
Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry.…
: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
Harish Karthikeyan, Antigoni Polychroniadou
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. I…
Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation
Yiping Ma, Yue Guo, Harish Karthikeyan +1
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients (within the toler…