Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

AnonPSI: An Anonymity Assessment Framework for PSI (2311.18118v1)

Published 29 Nov 2023 in cs.CR and cs.IR

Abstract: Private Set Intersection (PSI) is a widely used protocol that enables two parties to securely compute a function over the intersected part of their shared datasets and has been a significant research focus over the years. However, recent studies have highlighted its vulnerability to Set Membership Inference Attacks (SMIA), where an adversary might deduce an individual's membership by invoking multiple PSI protocols. This presents a considerable risk, even in the most stringent versions of PSI, which only return the cardinality of the intersection. This paper explores the evaluation of anonymity within the PSI context. Initially, we highlight the reasons why existing works fall short in measuring privacy leakage, and subsequently propose two attack strategies that address these deficiencies. Furthermore, we provide theoretical guarantees on the performance of our proposed methods. In addition to these, we illustrate how the integration of auxiliary information, such as the sum of payloads associated with members of the intersection (PSI-SUM), can enhance attack efficiency. We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets. Our findings indicate that the methods we propose markedly enhance attack efficiency when contrasted with previous research endeavors. {The effective attacking implies that depending solely on existing PSI protocols may not provide an adequate level of privacy assurance. It is recommended to combine privacy-enhancing technologies synergistically to enhance privacy protection even further.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. Machine learning-based prediction of covid-19 diagnosis based on symptoms, url: https://github.com/nshomron/covidpred.
  2. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, page 308–318, New York, NY, USA, 2016. Association for Computing Machinery.
  3. Information sharing across private databases. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, SIGMOD ’03, page 86–97, New York, NY, USA, 2003. Association for Computing Machinery.
  4. Private matching for compute. Cryptology ePrint Archive, Paper 2020/599, 2020. urlhttps://eprint.iacr.org/2020/599.
  5. Subset sum in time 2n/2/p⁢o⁢l⁢y⁢(n)superscript2𝑛2𝑝𝑜𝑙𝑦𝑛2^{n/2}/poly(n)2 start_POSTSUPERSCRIPT italic_n / 2 end_POSTSUPERSCRIPT / italic_p italic_o italic_l italic_y ( italic_n ), 2023.
  6. Fast and private computation of cardinality of set intersection and union. In Josef Pieprzyk, Ahmad-Reza Sadeghi, and Mark Manulis, editors, Cryptology and Network Security, pages 218–231, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.
  7. Catalic: Delegated psi cardinality with applications to contact tracing. In Advances in Cryptology – ASIACRYPT 2020: 26th International Conference on the Theory and Application of Cryptology and Information Security, Daejeon, South Korea, December 7–11, 2020, Proceedings, Part III, page 870–899, Berlin, Heidelberg, 2020. Springer-Verlag.
  8. Cynthia Dwork. Differential privacy. In Encyclopedia of Cryptography and Security, 2006.
  9. Cynthia Dwork. Differential privacy: A survey of results. In Manindra Agrawal, Dingzhu Du, and Zhenhua Duan, editors, Theory and Applications of Models of Computation: 5th International Conference, TAMC, pages 1–19. 2008.
  10. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference, pages 265–284. 2006.
  11. Distributed measurement with private set-union cardinality. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS ’17, page 2295–2312, New York, NY, USA, 2017. Association for Computing Machinery.
  12. Efficient set intersection with simulation-based security. J. Cryptology, 29:115–155, 2016.
  13. Efficient private matching and set intersection. In Christian Cachin and Jan L. Camenisch, editors, Advances in Cryptology - EUROCRYPT 2004, pages 1–19, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg.
  14. Birds of a feather flock together: How set bias helps to deanonymize you via revealed intersection sizes. In 31st USENIX Security Symposium (USENIX Security 22), pages 1487–1504, Boston, MA, August 2022. USENIX Association.
  15. Instahide: Instance-hiding schemes for private distributed learning. In International Conference on Machine Learning, 2020.
  16. On deploying secure computing: Private intersection-sum-with-cardinality. In 2020 IEEE European Symposium on Security and Privacy (EuroS& P), pages 370–389, 2020.
  17. Solving low density subset sum problems. In 24th Annual Symposium on Foundations of Computer Science (sfcs 1983), pages 1–10, 1983.
  18. Latanyasweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 05 2012.
  19. Two-sided malicious security for private intersection-sum with cardinality, 2020. urlhttps://eprint.iacr.org/2020/385.
  20. Robust de-anonymization of large sparse datasets. In 2008 IEEE Symposium on Security and Privacy (sp 2008), pages 111–125, 2008.
  21. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In 2019 IEEE Symposium on Security and Privacy (SP), pages 739–753, 2019.
  22. Bartosz Przydatek. A fast approximation algorithm for the subset-sum problem. International Transactions in Operational Research, 9, 05 2000.
  23. A members first approach to enabling linkedin’s labor market insights at scale. ArXiv, abs/2010.13981, 2020.
  24. Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP), pages 3–18, 2017.
  25. An exact algorithm for the subset sum problem. European Journal of Operational Research, 136(1):57–66, 2002.
  26. Latanya Sweeney. Simple demographics often identify people uniquely. 2000.
  27. Tianchi. Taobao display advertisement click-through rate prediction dataset, url: https://tianchi.aliyun.com/dataset/datadetail?dataid=56, 2018.
  28. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pages 268–282, 2018.
  29. Can you find the one for me? In Proceedings of the 2018 Workshop on Privacy in the Electronic Society, WPES’18, page 54–65, New York, NY, USA, 2018. Association for Computing Machinery.
Citations (5)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now