Fast and Secure Location-Based Services in Smart Cities on Outsourced Data

With the advancement of mobile internet, cloud computing, and smart sensing devices, Location-Based Services (LBSs) have become more and more indispensable in the Internet of Things (IoT) based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This paper proposes two Secure Dynamic Spatial Keyword Queries (SDSKQ) constructions that support expressive query types and dynamic update. Firstly, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure for spatio-textual data, named Encrypted Textual Signature Quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world datasets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.