Paper 2022/1461

ACORN: Input Validation for Secure Aggregation

James Bell, Google (United Kingdom)
Adrià Gascón, Google (United States)
Tancrède Lepoint, Amazon (United States)
Baiyu Li, Google (United States)
Sarah Meiklejohn, Google (United Kingdom)
Mariana Raykova, Google (United States)
Cathie Yun, Google (United States)

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.

Available format(s)
Cryptographic protocols
Publication info
Secure Aggregation Zero-Knowledge Single-server Multi-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
2022-10-25: approved
2022-10-25: received
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Creative Commons Attribution


      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},
      note = {\url{}},
      url = {}
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