Privacy-Preserving Group Matching Protocol
Takuya Ibaraki · Journal of Computers · 2018
Many works have been done for privacy-preserving matching protocols.Most of them obtain one-to-one privacy-preserving matching.However, when we consider forming a group of people or objects by their similarity, matching can be applied to problems using many data, such as recommendation systems and a lot of similar communities.In this paper, we consider the characteristics of each user as a vector.We obtain the similarity by securely computing the inner product of vectors.Also, we define a group's characteristics by the members' average characteristics.We propose a privacy-preserving group matching protocol.We show computation cost of the proposed protocol and show simulation results.