Secure Spatial Network Queries on Cloud Platform

Abstract

With massive real-world networks added with spatial attributes, query processing in spatial networks has been widely used in real-life applications. Considering the high costs of spatial network queries, outsourcing querying services to cloud provides a cost-effective way for data owners. However, directly outsourcing such services may cause serious privacy concerns. To address this privacy problem, existing studies apply anonymous or differential privacy techniques on spatial network queries, which are not under the semi-trusted secure model. Other existing works primarily focus on query processing over either encrypted spatial or network data, which cannot directly be applied to work out the secure spatial network query problem. To this end, we define and study Secure Spatial Network kNN Query (SSNQ) problem on cloud platform. We first present Basic Secure Spatial Network kNN Query (BSSNQ) method, in which we compute secure kNN for the query node to construct candidate sequences using secure subprotocols. With pre-encrypted network distances, we then compute shortest paths between the query node and each candidate, and securely update the candidate sequences according to Euclidean restriction to derive final query results. To improve the efficiency of BSSNQ, we further propose Heuristic Secure Spatial Network kNN Query (HSSNQ) method, which securely calculates shortest paths from the query node to the visiting nodes iteratively, and use optimistic estimate distances as the key to lead the heuristic search within a priority queue. Thorough analysis shows the security and complexity of the proposed methods, and extensive experimental results on real datasets demonstrate the efficiency of the query performance.