AI RESEARCH
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
arXiv CS.LG
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ArXi:2603.19949v1 Announce Type: cross Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two-server protocols (e.g., Prio and successors) set a practical baseline by validating inputs while preventing any single party from learning users' values, but they impose symmetric costs on both servers and communication that scales with the per-client input dimension $L.