Paper 2021/654
Non-Interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning
Carlo Brunetta, Georgia Tsaloli, Bei Liang, Gustavo Banegas, and Aikaterini Mitrokotsa
Abstract
We propose a novel primitive called NIVA that allows the distributed aggregation of multiple users' secret inputs by multiple untrusted servers. The returned aggregation result can be publicly verified in a non-interactive way, i.e. the users are not required to participate in the aggregation except for providing their secret inputs. NIVA allows the secure computation of the sum of a large amount of users' data and can be employed, for example, in the federated learning setting in order to aggregate the model updates for a deep neural network. We implement NIVA and evaluate its communication and execution performance and compare it with the current state-of-the-art, i.e. Segal et al. protocol (CCS 2017) and Xu et al. VerifyNet protocol (IEEE TIFS 2020), resulting in better user's communicated data and
Note: Code publicly released.
Metadata
- Available format(s)
-
PDF
- Publication info
- Published elsewhere. Major revision. ACISP 2021
- Keywords
- secure aggregationprivacyverifiabilitydecentralization
- Contact author(s)
- brunetta @ chalmers se
- History
- 2021-05-20: received
- Short URL
- https://ia.cr/2021/654
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2021/654,
author = {Carlo Brunetta and Georgia Tsaloli and Bei Liang and Gustavo Banegas and Aikaterini Mitrokotsa},
title = {Non-Interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning},
howpublished = {Cryptology {ePrint} Archive, Paper 2021/654},
year = {2021},
url = {https://eprint.iacr.org/2021/654}
}
