PPAQ: Privacy-Preserving Aggregate Queries for Optimal Location Selection in Road Networks

Abstract

Aggregate nearest neighbor (ANN) query, which can find an optimal location with the smallest aggregate distance to a group of query users’ locations, has received considerable attention and been practically useful in many real-world location-based applications. Nevertheless, query users still hesitate to use these applications due to privacy concerns, as there is a worrisome that the location-based service (LBS) providers may abuse their locations after collecting them. In this article, to tackle this issue, we propose a novel privacy-preserving aggregate query (PPAQ) scheme to select an optimal location for query users in road networks. Specifically, we first analyze the problem of the ANN query in road networks and identify two basic operations, i.e., addition and comparison, in the query. Then, we carefully design efficient addition and comparison circuits to securely add and compare two bit-based inputs, respectively. With these two secure circuits, we propose our PPAQ scheme, which can simultaneously protect the users’ locations, query results, and access patterns from leaking. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. In addition, extensive performance evaluations are conducted, and the results indicate that our proposed scheme has an acceptable efficiency for non-real-time applications.