Efficient Privacy-Preserving Geographic Keyword Boolean Range Query Over Encrypted Spatial Data
Zhimao Gong, Junyi Li, Yaping Lin, Jianhao Wei, Lanciné Camara
Abstract
With the widespread popularity of mobile devices and geolocation-related services, spatial keyword data has exploded in recent years. As an application, people are accustomed to using specific keywords to search for data in a given geometric range. To protect user privacy, searchable encryption technologies are used to encrypt data and user queries. Most existing works focus on either spatial attributes or keyword attributes over encrypted spatial keyword data, which cannot solve the problem of geographic keyword range queries directly. And several other works considering these two attributes have some limitations in terms of query efficiency and security assurance. In this article, we propose an efficient privacy-preserving geographic keyword Boolean range query (EPBRQ) scheme to solve existing challenges in the current work. In particular, we design a recoding algorithm to break the limits of the current work to achieve lower time complexity and employ secure Knn computation to protect user data privacy comprehensively. The security analysis shows that our solution can well protect the privacy of data and queries from cloud server threats. And numerous experiments based on real-world data also show that our scheme provides better query efficiency than existing works.