Efficient secure similarity computation on encrypted trajectory data

摘要: Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, query processing. Trajectory contain rich spatio-temporal relationships reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them be encrypted before being outsourced privacy concerns. However, efficient processing over trajectory very challenging task. Though some achievements have been reported recently simple queries SQL queries, kNN queries) on data, there rather limited progress secure evaluation because they are more complex need special treatment. In this paper, we focus similarity computation that the cornerstone More specifically, propose an solution securely compute between two trajectories, reveals nothing about but final result. We theoretically prove our against semi-honest adversaries model as all intermediate in protocols can simulated polynomial time. Finally empirically study efficiency proposed method, demonstrates feasibility solution.