Cryptology ePrint Archive: Report 2021/654

Non-Interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning

Carlo Brunetta and Georgia Tsaloli and Bei Liang and 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

Category / Keywords: secure aggregation, privacy, verifiability, decentralization

Original Publication (with major differences): ACISP 2021

Date: received 19 May 2021, last revised 20 May 2021

Contact author: brunetta at chalmers se

Available format(s): PDF | BibTeX Citation

Note: Code publicly released.

Version: 20210520:203110 (All versions of this report)

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