Cloud-Assisted Threshold Multi-Party Private Set Intersection

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.

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Publication Details

Journal
Computer Science and Application
Published
2025-01-01
DOI
https://doi.org/10.12677/csa.2025.154080
Primary Topic
Privacy-Preserving Technologies in Data
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article
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article

Cloud-Assisted Threshold Multi-Party Private Set Intersection

芗宇 刘
Computer Science and Application
Privacy-Preserving Technologies in Data
article

Cloud-Assisted Threshold Multi-Party Private Set Intersection

芗宇 刘
article en

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.

Computer Science and ApplicationVol. 15(04)
Openalex Percentile: Top 97%
Privacy-Preserving Technologies in Data
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Cloud-Assisted Threshold Multi-Party Private Set Intersection — 芗宇 刘 · Computer Science and Application (2025) | TGRS Research Map | TGRS