Practical Multi-Party Private Set Intersection with Reducible Zero-Sharing
Yewei Guan, Hua Guo, Man Ho Au, Jiarong Huo, Jin Tan, Zhenyu Guan
Abstract
Multi-party Private Set Intersection (mPSI) enables parties, each holding a set of size , to jointly compute their intersection while preserving the confidentiality of each set, which is essential for privacy-preserving data analysis and secure database queries. Existing mPSI protocols have limitations in achieving both sufficient security and practical efficiency. This paper presents a novel and efficient mPSI construction in the semi-honest model while resisting arbitrary collusion attacks. Our construction works in the offline/online paradigm. Given the corruption threshold , the online phase achieves linear total computational and communication complexity, that is , and solely uses symmetric operations. This makes our construction theoretically outperform the existing works. The technical core of the construction is our newly extracted primitive called reducible zero-sharing, which allows nt-19.57-114.46 2.69-28.41 0.29-28.70 18.73 $ improvement. Compared with works with practical efficiency, our mPSI construction achieves similar performance while providing stronger security.
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