short-paper

Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems

Authors:

Julien Mirval,

Luc Bouganim,

Iulian Sandu-PopaAuthors Info & Claims

SSDBM '21: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management

Pages 259 - 264

https://doi.org/10.1145/3468791.3468821

Published: 11 August 2021 Publication History

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Abstract

Personal Data Management Systems (PDMS) are flourishing, boosted by legal and technical means like smart disclosure, data portability and data altruism. A PDMS allows its owner to easily collect, store and manage data, directly generated by her devices, or resulting from her interactions with companies or administrations. PDMSs unlock innovative usages by crossing multiple data sources from one or many users, thus requiring aggregation primitives. Indeed, aggregation primitives are essential to compute statistics on user data, but are also a fundamental building block for machine learning algorithms. This paper proposes a protocol allowing for secure aggregation in a massively distributed PDMS environment, which adapts to selective participation and PDMSs characteristics, and is reliable with respect to failures, with no compromise on accuracy. Preliminary experiments show the effectiveness of our protocol which can adapt to several contexts with varying PDMSs characteristics in terms of communication speed or CPU resources and can adjust the aggregation strategy to the estimated selective participation.

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Cited By

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Mirval JBouganim LSandu Popa I(2024)Handling Dropouts in Federating Learning with Personal Data Management SystemsTransactions on Large-Scale Data- and Knowledge-Centered Systems LVI10.1007/978-3-662-69603-3_2(37-75)Online publication date: 21-Jul-2024
https://doi.org/10.1007/978-3-662-69603-3_2
Ansah DSusha I(2024)Exploring Data Altruism as Data Donation: A Review of Concepts, Actors and ObjectivesElectronic Participation10.1007/978-3-031-70804-6_12(179-193)Online publication date: 3-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70804-6_12
Mirval JBouganim LSandu Popa I(2023)Federated Learning on Personal Data Management Systems: Decentralized and Reliable Secure Aggregation ProtocolsProceedings of the 35th International Conference on Scientific and Statistical Database Management10.1145/3603719.3603730(1-12)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3603719.3603730
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Published In

SSDBM '21: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management

July 2021

275 pages

ISBN:9781450384131

DOI:10.1145/3468791

Editors:
Qiang Zhu
University of Michigan - Dearborn, USA
,
Xingquan (Hill) Zhu
Florida Atlantic University, USA
,
Yicheng Tu
University of South Florida, USA
,
Zichen (Frank) Xu
Nanchang University, China
,
Anand Kumar
Amazon Inc., USA

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

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Published: 11 August 2021

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SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management

July 6 - 7, 2021

FL, Tampa, USA

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Mirval JBouganim LSandu Popa I(2024)Handling Dropouts in Federating Learning with Personal Data Management SystemsTransactions on Large-Scale Data- and Knowledge-Centered Systems LVI10.1007/978-3-662-69603-3_2(37-75)Online publication date: 21-Jul-2024
https://doi.org/10.1007/978-3-662-69603-3_2
Ansah DSusha I(2024)Exploring Data Altruism as Data Donation: A Review of Concepts, Actors and ObjectivesElectronic Participation10.1007/978-3-031-70804-6_12(179-193)Online publication date: 3-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70804-6_12
Mirval JBouganim LSandu Popa I(2023)Federated Learning on Personal Data Management Systems: Decentralized and Reliable Secure Aggregation ProtocolsProceedings of the 35th International Conference on Scientific and Statistical Database Management10.1145/3603719.3603730(1-12)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3603719.3603730
Bouganim LLoudet JSandu Popa I(2022)Highly distributed and privacy-preserving queries on personal data management systemsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00753-132:2(415-445)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1007/s00778-022-00753-1

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Practical Secure Aggregation for Privacy-Preserving Machine Learning

Federated Learning on Personal Data Management Systems: Decentralized and Reliable Secure Aggregation Protocols

Spectral Anonymization of Data

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