Privacy-preserving k-NN computation over encrypted cloud data without key sharing
Shao-hui Wang, Tian Liu, Fu Xiao, Ruchuan Wang · 2016
As a frequently used query operation for online applications, secure k-nearest neighbors (k-NN) computation on encrypted cloud data has received much attention. In this paper, we focus on the problem of supporting efficient k-NN computation over encrypted cloud data while data owner cannot share his secret keys with query users. We give a security analysis of the scheme proposed by Zhu et al. [16], and the analysis shows the scheme cannot provide query privacy at all. And we design a new efficient construction to support efficient k-NN computation over encrypted cloud data without key sharing. Additionally, security analysis is presented to demonstrate the new scheme can provide data privacy and query privacy in the Known Ciphertext Model.