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云辅助阈值多方隐私集合交集
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Abstract:
隐私集合交集(Private Set Intersection, PSI)协议是一种具有重要实际意义的安全多方计算协议,广泛应用于多方私有输入集合求交集的场景。阈值多方PSI协议作为PSI协议的一种灵活形式,能够适应更多复杂场景。本文给出了一种一次的云辅助阈值多方PSI模型(Cloud-assisted Threshold Multi-party Private Set Intersection, CTMPSI),旨在优化发送方在资源受限场景下的性能。该协议通过引入云服务器辅助计算,显著降低了发送方的计算和通信开销,同时在半诚实模型下确保了输入集合元素的隐私性。此外,CTMPSI实现了发送方上传加密数据后即可离线的功能,进一步提升了协议的实用性。本文详细描述了CTMPSI协议的设计框架和性能评估。实验结果表明,在不平衡输入集合场景中,CTMPSI协议相较于现有的多方PSI协议,在性能上取得了显著提升。该协议为资源受限场景下的阈值多方PSI应用提供了高效且安全的解决方案,具有重要的理论价值和实际意义。
Private Set Intersection (PSI) protocol is a secure multi-party computation protocol with significant practical applications, widely used in scenarios where multiple parties need to compute the intersection of their private input sets. As a flexible variant of PSI, threshold multi-party PSI can adapt to more complex scenarios. This paper proposes a one-round cloud-assisted threshold multi-party PSI model (Cloud-assisted Threshold Multi-party Private Set Intersection, CTMPSI), aiming to optimize the performance of senders in resource-constrained scenarios. By introducing cloud server-assisted computation, the protocol significantly reduces the computational and communication overhead for senders while ensuring the privacy of input set elements in the semi-honest model. Additionally, CTMPSI enables senders to go offline after uploading encrypted data, further enhancing the practicality of the protocol. This paper provides a detailed description of the design framework and performance evaluation of CTMPSI. Experimental results demonstrate that, in scenarios with unbalanced input sets, CTMPSI achieves significant performance improvements compared to existing multi-party PSI protocols. The protocol offers an efficient and secure solution for threshold multi-party PSI applications in resource-constrained environments, holding important theoretical and practical significance.
[1] | 黄翠婷, 张帆, 孙小超, 等. 隐私集合求交技术的理论与金融实践综述[J]. 信息通信技术与政策, 2021, 47(6): 50-56. |
[2] | 魏立斐, 刘纪海, 张蕾, 等. 面向隐私保护的集合交集计算综述[J]. 计算机研究与发展, 2022, 59(8): 1782-1799. |
[3] | Hetz, L., Schneider, T. and Weinert, C. (2024) Scaling Mobile Private Contact Discovery to Billions of Users. In: Tsudik, G., Conti, M., Liang, K. and Smaragdakis, G., Eds., Computer Security—ESORICS 2023, Springer, 455-476. https://doi.org/10.1007/978-3-031-50594-2_23 |
[4] | Ruan, O., Huang, X. and Mao, H. (2020) An Efficient Private Set Intersection Protocol for the Cloud Computing Environments. 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Baltimore, 25-27 May 2020, 254-259. https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00053 |
[5] | Ion, M., Kreuter, B., Nergiz, E., et al. (2017) Private Intersection-Sum Protocol with Applications to Attributing Aggregate Ad Conversions. Cryptology ePrint Archive. https://eprint.iacr.org/2017/738 |
[6] | Yang, X., Zhao, Y., Zhou, S. and Wang, L. (2024) A Lightweight Delegated Private Set Intersection Cardinality Protocol. Computer Standards & Interfaces, 87, Article ID: 103760. https://doi.org/10.1016/j.csi.2023.103760 |
[7] | Inbar, R., Omri, E. and Pinkas, B. (2018) Efficient Scalable Multiparty Private Set-Intersection via Garbled Bloom Filters. In: Catalano, D. and De Prisco, R., Eds., Security and Cryptography for Networks, Springer, 235-252. https://doi.org/10.1007/978-3-319-98113-0_13 |
[8] | Zhou, J., Su, D. and Deng, J. (2023) Multi-Party Threshold Private Set Intersection Cardinality Based on Encrypted Bloom Filter. 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Danzhou, 17-21 December 2023, 503-511. https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00098 |
[9] | Bay, A., Erkin, Z., Hoepman, J., Samardjiska, S. and Vos, J. (2022) Practical Multi-Party Private Set Intersection Protocols. IEEE Transactions on Information Forensics and Security, 17, 1-15. https://doi.org/10.1109/tifs.2021.3118879 |