[2603.19949] TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)

Computer Science > Cryptography and Security arXiv:2603.19949 (cs) [Submitted on 20 Mar 2026] Title:TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+) Authors:Harish Karthikeyan, Antigoni Polychroniadou View a PDF of the paper titled TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+), by Harish Karthikeyan and Antigoni Polychroniadou View PDF HTML (experimental) Abstract: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 dimensions: (i) no trusted setup or preprocessing, (ii) server-side communication that is independent of $L$ (iii) post-quantum security based solely on standard lattice assumptions (LWE, SIS), and (iv) stronger robustness with identifiable abort and full malicious security for the servers. A key design choice is intentional asymmetry: one server bears the $O(L)$ aggregation a...