PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

Abstract

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.