An Efficient Privacy-Preserving Multi-Keyword Query Scheme in Location Based Services

Abstract

With the proliferation of location-aware mobile devices and the prevalence of wireless communications, location-based services (LBS) have attracted much particular attention in recent years. For flexibility and cost savings, the LBS provider outsources the LBS data to the cloud in order to serve the increasing number of mobile users. To guarantee users' privacy and data confidentiality, some excellent works have been proposed which focus on secure query over the location server. However, these existing works have two limitations. On the one hand, they cannot preserve users' location and query content privacy simultaneously. On the other hand, they fail to support multi-keyword queries. In this paper, aiming at a multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the data users. To improve the efficiency of our proposed scheme, we utilize the linear quad-tree technique to build a grid system to represent the location information in the query condition as well as a searchable index. To protect the location privacy, we combine decimal Morton code and public-key cryptography techniques to build a searchable index or to generate a trapdoor. To enable the cloud server to perform a secure multikeyword query, we systematically construct a privacy-preserving query scheme with bilinear pairing-based cryptography. In particular, our proposed scheme is scalable and very suitable for multi-user environments due to the flexible user registration and revocation mechanisms. Furthermore, a detailed security analysis shows that the proposed scheme can ensure the confidentiality of LBS data, and protect the location and query content privacy. Extensive experiments are conducted on a real LBS dataset, and the simulation results confirm the security and efficiency of our scheme.

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

“…The experiment compares the proposed algorithm (QLPSO), the traditional generalized user selection algorithm (baseline), and the optimal inhibition rate (OPT). We also select two methods to make a comparison containing MKQ (Zhang et al, 2020) and DAAL (Zeng et al, 2021). Figure 3 describes the influence of the user number on the inhibition rate.…”

Updatable privacy-preserving $$\textit{K}$$-nearest neighbor query in location-based s-ervice

“…It can be seen that, except [23], the other three schemes are resistant to server attacks because they store the location points in ciphertext on the server. The scheme proposed by Zhang et al [10] supports multi-user environment while no update of location points. The update of location points in [22] holds with a heavy overhead.…”

“…Many privacy protection techniques have been proposed according to different application scenarios and protection means. They can be broadly classified according to the privacy protection techniques used: spatial obfuscation techniques [5][6][7], data transformation techniques [8], false location perturbation techniques [9], and encryption techniques [10][11][12].…”

A real‐time privacy‐preserving scheme based on grouping queries for continuous location‐based services

“…LBS privacy protection 12,13 is commonly implemented according to three system architectures: centralized, distributed, and hybrid. The centralized architecture is comprised of mobile terminals, a trusted anonymous server, and an LBS server.…”