Privacy-Preserving Reverse Nearest Neighbor Query Over Encrypted Spatial Data

With the advent of cloud computing, it has become more and more popular to outsource various services to the cloud for releasing the burden of local data storage and maintenance. However, it may cause serious privacy problems because the cloud may be untrusted. In this article, we study the privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. First, we introduce the concept of reference-locked order-preserving encryption (RL-OPE) with its construction and security proof, which reveals less information than traditional order-preserving encryption (OPE). Then, we present a novel PPRNN scheme in static setting based on structured encryption (SE) and the proposed RL-OPE, called sPPRNN. After that, we design a generic method that extends a PPRNN scheme in static setting to the counterpart in dynamic setting, called dPPRNN. Furthermore, we present a thorough privacy analysis of our proposal. Finally, we demonstrate its efficiency and effectiveness for practical deployment through extensive experiments.

Automated summary

This article investigates privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. It introduces a new PPRNN scheme sPPRNN and its extension to the dynamic setting dPPRNN, and conducts a thorough privacy analysis, demonstrating the efficiency and effectiveness of the proposal through extensive experiments.