@inproceedings{728a0fad02b54b6eb247ea0d7a72b58b,
title = "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{\textquoteright} 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{\textquoteright} 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{\textquoteright}s communicated data and execution time.",
keywords = "Decentralization, Privacy, Secure aggregation, Verifiability",
author = "Carlo Brunetta and Georgia Tsaloli and Bei Liang and Gustavo Banegas and Aikaterini Mitrokotsa",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 26th Australasian Conference on Information Security and Privacy, ACISP 2021 ; Conference date: 01-12-2021 Through 03-12-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.1007/978-3-030-90567-5\_26",
language = "English",
isbn = "9783030905668",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "510--528",
editor = "Joonsang Baek and Sushmita Ruj",
booktitle = "Information Security and Privacy - 26th Australasian Conference, ACISP 2021, Proceedings",
}