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 $L_0$, $L_2$, and $L_\infty$ 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 $2$-$8$X speedups in client computation in practical scenarios. Likewise, our extended protocol with input validation improves on prior work by more than $30$X in terms of client communiation (with comparable computation costs). Compared to the base protocols without input validation, the extended protocols incur only $0.1$X additional communication, and can process binary indicator vectors of length $1$M, or 16-bit dense vectors of length $250$K, in under $80$s of computation per client.
Note: Updated experimental results and the proof of valid RLWE encodings.
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Minor revision. USENIX Security '23
- Keywords
- Secure AggregationZero-KnowledgeSingle-serverMulti-Party Computation
- Contact author(s)
-
jhbell @ google com
adriag @ google com
tlepoint @ amazon com
baiyuli @ google com
meiklejohn @ google com
marianar @ google com
cathieyun @ google com - History
- 2023-08-08: revised
- 2022-10-25: received
- See all versions
- Short URL
- https://ia.cr/2022/1461
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2022/1461,
author = {James Bell and Adrià Gascón and Tancrède Lepoint and Baiyu Li and Sarah Meiklejohn and Mariana Raykova and Cathie Yun},
title = {{ACORN}: Input Validation for Secure Aggregation},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/1461},
year = {2022},
url = {https://eprint.iacr.org/2022/1461}
}