TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
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 $L$. Modern learning tasks routinely involve dimensionalities $L$ in the tens to hundreds of millions of model parameters. We present TAPAS, a two-server asymmetric private aggregation scheme that addresses these limitations along four...
| Authors | Harish Karthikeyan, Antigoni Polychroniadou |
|---|---|
| Primary category | cs.CR |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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