research-article

Two-party Private Set Intersection with an Untrusted Third Party

Authors:

Samuel Ranellucci,

S. Dov GordonAuthors Info & Claims

CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

Pages 2403 - 2420

https://doi.org/10.1145/3319535.3345661

Published: 06 November 2019 Publication History

Abstract

We construct new protocols for two parties to securely compute on the items in their intersection. Our protocols make use of an untrusted third party that has no input. The use of this party allows us to construct highly efficient protocols that are secure against a single malicious corruption.

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Cited By

Sharma SLi YMehrotra SPanwar NGupta PGhosh D(2024) Prism: Pr ivacy-Preserving and Ver i fiable S et Computation Over M ulti-Owner Secret Shared Outsourced Databases IEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.327935621:3(1355-1371)Online publication date: May-2024
https://doi.org/10.1109/TDSC.2023.3279356
Dong CWeng JLi MLiu JLiu ZCheng YYu S(2024)Privacy-Preserving and Byzantine-Robust Federated LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326469721:2(889-904)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2023.3264697
Ma HHuang SGuo JLam KYang T(2024)Blockchain-Based Privacy-Preserving Federated Learning for Mobile CrowdsourcingIEEE Internet of Things Journal10.1109/JIOT.2023.334063011:8(13884-13899)Online publication date: 15-Apr-2024
https://doi.org/10.1109/JIOT.2023.3340630
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Index Terms

Two-party Private Set Intersection with an Untrusted Third Party
1. Security and privacy
  1. Cryptography
  2. Security services
    1. Privacy-preserving protocols

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Abstract
In this paper, we propose a new private set intersection (PSI) protocol with bi-oblivious data transfer that computes the following functionality. The two parties ( $P_{1}$ and $P_{2}$ ) input two sets of items (X and Y, respectively) and one of the parties ( ${...}_{}$ $_{}_{}$ $_{}$ $_{}$ $_{} \overset{}{}$ $_{}$ $_{}$ $_{}$

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Published In

cover image ACM Conferences

CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

November 2019

2755 pages

ISBN:9781450367479

DOI:10.1145/3319535

General Chairs:
Lorenzo Cavallaro
King's College London, UK
,
Johannes Kinder
Bundeswehr University Munich, Germany
,
Program Chairs:
XiaoFeng Wang
Indiana University, USA
,
Jonathan Katz
George Mason University, USA

Copyright © 2019 ACM.

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2019

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Funding Sources

Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center Pacific (SSC Pacific)

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CCS '19

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SIGSAC

CCS '19: 2019 ACM SIGSAC Conference on Computer and Communications Security

November 11 - 15, 2019

London, United Kingdom

Acceptance Rates

CCS '19 Paper Acceptance Rate 149 of 934 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

Upcoming Conference

CCS '24

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

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Citations

Cited By

Sharma SLi YMehrotra SPanwar NGupta PGhosh D(2024) Prism: Pr ivacy-Preserving and Ver i fiable S et Computation Over M ulti-Owner Secret Shared Outsourced Databases IEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.327935621:3(1355-1371)Online publication date: May-2024
https://doi.org/10.1109/TDSC.2023.3279356
Dong CWeng JLi MLiu JLiu ZCheng YYu S(2024)Privacy-Preserving and Byzantine-Robust Federated LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326469721:2(889-904)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2023.3264697
Ma HHuang SGuo JLam KYang T(2024)Blockchain-Based Privacy-Preserving Federated Learning for Mobile CrowdsourcingIEEE Internet of Things Journal10.1109/JIOT.2023.334063011:8(13884-13899)Online publication date: 15-Apr-2024
https://doi.org/10.1109/JIOT.2023.3340630
Zhao QJiang BZhang YWang HMao YZhong S(2024)Unbalanced private set intersection with linear communication complexityScience China Information Sciences10.1007/s11432-022-3717-967:3Online publication date: 5-Feb-2024
https://doi.org/10.1007/s11432-022-3717-9
Li YXiao HHan PZhou Z(2024)Practical Private Intersection-Sum Protocols with Good ScalabilityBlockchain Technology and Application10.1007/978-981-97-3203-6_3(49-63)Online publication date: 23-Jun-2024
https://doi.org/10.1007/978-981-97-3203-6_3
Ruan OYan CZhou JAi C(2023)A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced ScenariosApplied Sciences10.3390/app13241321513:24(13215)Online publication date: 13-Dec-2023
https://doi.org/10.3390/app132413215
Bai JSong XZhang XWang QCui SChang ERussello G(2023)Mostree: Malicious Secure Private Decision Tree Evaluation with Sublinear CommunicationProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627131(799-813)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627131
Yeo FYing J(2023)Third-Party Private Set Intersection2023 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT54713.2023.10206690(1633-1638)Online publication date: 25-Jun-2023
https://doi.org/10.1109/ISIT54713.2023.10206690
Morales DAgudo ILopez J(2023)Private set intersectionComputer Science Review10.1016/j.cosrev.2023.10056749:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100567
Wang YLv QZhang HZhao MSun YRan LLi T(2023)Beyond model splitting: Preventing label inference attacks in vertical federated learning with dispersed trainingWorld Wide Web10.1007/s11280-023-01159-x26:5(2691-2707)Online publication date: 8-May-2023
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