Non-Interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning
Paper in proceeding, 2021

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.



Secure Aggregation



Carlo Brunetta

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Georgia Tsaloli

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Bei Liang

Beijing Institute of Mathematical Sciences and Applications

Gustavo Souza Banegas

Institut National de Recherche en Informatique et en Automatique (INRIA)

Aikaterini Mitrokotsa

University of St Gallen

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13083 LNCS 510-5128
9783030905668 (ISBN)

26th Australasian Conference on Information Security and Privacy
Perth, Australia,

Subject Categories

Other Computer and Information Science

Communication Systems

Computer Science

Areas of Advance

Information and Communication Technology



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