Flag: A Framework for Lightweight Robust Secure Aggregation

Laasya Bangalore, Mohammad Hossein Faghihi Sereshgi, Carmit Hazay, Muthuramakrishnan Venkitasubramaniam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this work, we introduce a lightweight secure aggregation protocol that guarantees liveness (i.e., guaranteed output delivery), robust against faulty inputs and security against malicious clients. First, we improve upon prior works in the "star"-like topology network with a central coordinating (also output) party, Bonawitz et al. (ACM CCS 2017) and Bell et al. (ACM CCS 2020), which are not robust against faulty inputs. Recent works, RoFL (Burkhalter et al.) and (concurrent work) ACORN (Bell et al.) show how to rely on zero-knowledge proofs to address such attacks at expense of significantly high computation costs. We also compare our protocol against the PRIO system by Gibbs and Boneh (USENIX 2017) which achieves the same task in an incomparable security model. We benchmark our protocol with implementation and demonstrate its concrete efficiency. Our solution scales to 1000s of clients, requires only a constant number of rounds, outperforms prior work in computational cost, and has competitive communication cost.

Original languageEnglish
Title of host publicationASIA CCS 2023 - Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages14-28
Number of pages15
ISBN (Electronic)9798400700989
DOIs
StatePublished - 10 Jul 2023
Event18th ACM ASIA Conference on Computer and Communications Security, ASIA CCS 2023 - Melbourne, Australia
Duration: 10 Jul 202314 Jul 2023

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference18th ACM ASIA Conference on Computer and Communications Security, ASIA CCS 2023
Country/TerritoryAustralia
CityMelbourne
Period10/07/2314/07/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

Funding

ACKNOWLEDGMENTS We thank the anonymous reviewers of ASIACCS‘23 for their helpful comments. The first and fourth authors were supported by a JPMorgan Chase Faculty Research Award, Technology, and Humanity Fund from the McCourt School of Public Policy at Georgetown University, and a Google Research Award. The third author was supported by ISF grant No. 1316/18 and by the Algorand Centres of Excellence programme managed by Algorand Foundation. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of Algorand Foundation. REFERENCES

FundersFunder number
Algorand Foundation
JPMorgan
McCourt School of Public Policy
Google
Georgetown University
Iowa Science Foundation1316/18

    Keywords

    • Federated Learning
    • Input Certification
    • Secure Aggregation
    • Secure Multi-Party Computation

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