Non-Interactive, Secure Verifiable Aggregation for Decentralized, Privacy-Preserving Learning
Paper i 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, Data- och informationsteknik, Nätverk och system

Georgia Tsaloli

Chalmers, Data- och informationsteknik, Nätverk och system

Bei Liang

Beijing Institute of Mathematical Sciences and Applications

Gustavo Souza Banegas

Inria and Laboratoire d’Informatique de l’Ecole polytechnique

Aikaterini Mitrokotsa

Universität St. Gallen

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

03029743 (ISSN) 16113349 (eISSN)

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


Annan data- och informationsvetenskap


Datavetenskap (datalogi)


Informations- och kommunikationsteknik

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