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Conference Papers Year : 2021

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

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 execution time.
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Dates and versions

hal-03454325 , version 1 (29-11-2021)

Identifiers

Cite

Carlo Brunetta, Georgia Tsaloli, Bei Liang, Gustavo Banegas, Aikaterini Mitrokotsa. Non-interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning. ACISP 2021 - The 26th Australasian Conference on Information Security and Privacy, Dec 2021, Virtual event, Australia. pp.510-528, ⟨10.1007/978-3-030-90567-5_26⟩. ⟨hal-03454325⟩
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