PRRkNN: Efficient and Privacy-Preserving Range-Based Reverse kNN over Encrypted Data
Yandong Zheng, Hui Zhu, Rongxing Lu, Fengwei Wang · 2023
Cloud computing's ease of management and on-demand availability features have triggered the boom of outsourcing query services to the cloud. Considering the not-fully trustfulness of the cloud server, the outsourced query services should take the data privacy into consideration, especially when sensitive data are involved. Although many query types have been studied in the context of cloud computing, the range-based reverse k nearest neighbors (RkNN) query is still an unexplored area, which searches data records having any record in the query range as kNN and has wide applications in the taxi dispatching and advertisement placement. As a steppingstone, we propose the first efficient and privacy-preserving range-based RkNN query (PRRkNN) scheme in the cloud. We first design a modified R-tree (MR-tree) to organize the dataset and introduce an efficient MR-tree based RkNN query algorithm to handle RkNN queries with sublinear query efficiency. Then, we propose a sign-preserving matrix encryption (SPME) scheme to privately determine the sign of the scalar product between two records and deploy Paillier homomorphic encryption to introduce a two-party multiplication protocol for secure multiplication between a matrix and a vector. After that, we leverage the SPME scheme and the multiplication protocol to design our PRRkNN scheme. In addition, security analysis shows that our PRRkNN scheme achieves the desired security; and the performance evaluation verifies its efficiency.