research-article

Combining Differential Privacy and Secure Multiparty Computation

Authors:

Peeter LaudAuthors Info & Claims

ACSAC '15: Proceedings of the 31st Annual Computer Security Applications Conference

Pages 421 - 430

https://doi.org/10.1145/2818000.2818027

Published: 07 December 2015 Publication History

Abstract

We consider how to perform privacy-preserving analyses on private data from different data providers and containing personal information of many different individuals. We combine differential privacy and secret sharing based secure multiparty computation in the same system to protect the privacy of both the data providers and the individuals. We have implemented a prototype of this combination and have found that the overhead of adding differential privacy to secure multiparty computation is small enough to be usable in practice.

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Cited By

Chen WChen HHan TTong WZhong S(2025)Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential PrivacyIEEE Transactions on Mobile Computing10.1109/TMC.2024.346474424:1(276-292)Online publication date: Jan-2025
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ACSAC '15: Proceedings of the 31st Annual Computer Security Applications Conference

December 2015

489 pages

ISBN:9781450336826

DOI:10.1145/2818000

Copyright © 2015 ACM.

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Published: 07 December 2015

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ACSAC 2015: 2015 Annual Computer Security Applications Conference

December 7 - 11, 2015

CA, Los Angeles, USA

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Cited By

Chen WChen HHan TTong WZhong S(2025)Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential PrivacyIEEE Transactions on Mobile Computing10.1109/TMC.2024.346474424:1(276-292)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3464744
Pentyala SPereira MDe Cock MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)CaPSProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693709(40397-40413)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693709
Chen CZhou ZTang PHe LSu S(2024)Enforcing group fairness in privacy-preserving Federated LearningFuture Generation Computer Systems10.1016/j.future.2024.06.040160(890-900)Online publication date: Nov-2024
https://doi.org/10.1016/j.future.2024.06.040
Chen CLiu JTan HLi XWang KLi PSakurai KDou D(2024)Trustworthy federated learning: privacy, security, and beyondKnowledge and Information Systems10.1007/s10115-024-02285-2Online publication date: 26-Nov-2024
https://doi.org/10.1007/s10115-024-02285-2
Chen DJiang XZhong HCui J(2023)Building Trusted Federated Learning: Key Technologies and ChallengesJournal of Sensor and Actuator Networks10.3390/jsan1201001312:1(13)Online publication date: 6-Feb-2023
https://doi.org/10.3390/jsan12010013
Nguyen Duy DAffenzeller MNikzad-Langerodi RSilva SPaquete L(2023)Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty ComputationProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596337(2420-2428)Online publication date: 15-Jul-2023
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Cummings RSarathy J(2023)Centering Policy and Practice: Research Gaps Around Usable Differential Privacy2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPS-ISA58951.2023.00024(122-135)Online publication date: 1-Nov-2023
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Tang PChen RSu SGuo SJu LLiu G(2023)Multi-Party Sequential Data Publishing Under Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324166135:9(9562-9577)Online publication date: 1-Sep-2023
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