The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Scalable Private Set Intersection Based on OT Extension
Abstract
Private set intersection (PSI) allows two parties to compute the intersection of their sets without revealing any information about items that are not in the intersection. It is one of the best studied applications of secure computation and many PSI protocols have been proposed. However, the variety of existing PSI protocols makes it difficult to identify the solution that performs best in a respective scenario, especially since they were not compared in the same setting. In addition, existing PSI protocols are several orders of magnitude slower than an insecure naïve hashing solution, which is used in practice. In this article, we review the progress made on PSI protocols and give an overview of existing protocols in various security models. We then focus on PSI protocols that are secure against semi-honest adversaries and take advantage of the most recent efficiency improvements in Oblivious Transfer (OT) extension, propose significant optimizations to previous PSI protocols, and suggest a new PSI protocol whose runtime is superior to that of existing protocols. We compare the performance of the protocols, both theoretically and experimentally, by implementing all protocols on the same platform, give recommendations on which protocol to use in a particular setting, and evaluate the progress on PSI protocols by comparing them to the currently employed insecure naïve hashing protocol. We demonstrate the feasibility of our new PSI protocol by processing two sets with a billion elements each.
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Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,123
Random oracles are practical: a paradigm for designing efficient protocols
Bellare, M., & Rogaway, P. (1993) · Proceedings of the 1st ACM conference on Computer and communications security - CCS '93 · doi:10.1145/168588.168596 · Cited by 4,712
How to generate and exchange secrets
Yao, A. C.-C. (1986) · 27th Annual Symposium on Foundations of Computer Science (sfcs 1986) · doi:10.1109/sfcs.1986.25 · Cited by 3,733
(2011) · IEEE Software · doi:10.1109/ms.2011.67 · Cited by 1,989
Goldreich, O. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511721656 · Cited by 1,805
(2004) · IEEE Security & Privacy Magazine · doi:10.1109/msp.2004.9 · Cited by 1,518
The power of two choices in randomized load balancing
Mitzenmacher, M. (2001) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.963420 · Cited by 1,399
Efficient Private Matching and Set Intersection
Freedman, M. J., Nissim, K., & Pinkas, B. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24676-3_1 · Cited by 1,266
Self-citationPagh, R., & Rodler, F. F. (2004) · Journal of Algorithms · doi:10.1016/j.jalgor.2003.12.002 · Cited by 1,045
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
Demmler, D., Schneider, T., & Zohner, M. (2015) · Proceedings 2015 Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23113 · Cited by 827
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Lindell, Y. (2021) · Communications of the ACM · doi:10.1145/3387108 · Cited by 320
Secure Federated Submodel Learning
Niu, C., Wu, F., Tang, S., Hua, L., Jia, R., Lv, C., Wu, Z., & Chen, G. (2019) · arXiv · doi:10.48550/arxiv.1911.02254 · Cited by 28
Metadata
- Authors
- Pinkas, B., Schneider, T., & Zohner, M.
- Venue
- ACM Transactions on Privacy and Security
- Published
- 2018-01-02
- Volume
- 21
- Issue
- 2
- Pages
- 1-35
- ISSN
- 2471-2566 (ISSN-L)
- 2471-2574
- OpenAlex
- W2782492233
- Cited by
- 313
- References
- 49
- FWCI
- 14.00
- Type
- journal article
- Language
- en
- Open access
- closed
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