Fair Private Matching with Semi-Trusted Third Party
E-Yong Kim, Jeongdae Hong, Jung-Hee Cheon, Kun-Soo Park · 2008
Private Matching is the problem of computing the intersection of private datasets of two parties without revealing their own datasets. Freedman et al.[1] introduced a solution for the problem, where only one party gets private matching. When both parties want to get private matching simultaneously, we can consider the use of Kissner and Song[2]'s method which is a privacy-preserving set intersection with group decryption in multi-party case. In this paper we propose new protocols for fair private matching. Instead of group decryption we introduce a Semi-Trusted Third Party for fairness. We also propose an update procedure without restarting the PM protocol.