Paper 2022/1461

ACORN: Input Validation for Secure Aggregation

Abstract

Secure aggregation enables a server to learn the sum of client-held vectors in a privacy-preserving way, and has been successfully applied to distributed statistical analysis and machine learning. In this paper, we both introduce a more efficient secure aggregation construction and extend secure aggregation by enabling input validation, in which the server can check that clients' inputs satisfy required constraints such as $$, $$, and $$ bounds. This prevents malicious clients from gaining disproportionate influence on the computed aggregated statistics or machine learning model. Our new secure aggregation protocol improves the computational efficiency of the state-of-the-art protocol of Bell et al. (CCS 2020) both asymptotically and concretely: we show via experimental evaluation that it results in $$-$$X speedups in client computation in practical scenarios. Likewise, our extended protocol with input validation improves on prior work by more than $$X in terms of client communiation (with comparable computation costs). Compared to the base protocols without input validation, the extended protocols incur only $$X additional communication, and can process binary indicator vectors of length $$M, or 16-bit dense vectors of length $$K, in under $$s of computation per client.

Note: Updated experimental results and the proof of valid RLWE encodings.