DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

Abstract

Since smartphones embedded with positioning systems and digital maps are widely used, location-based services (LBSs) are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives; however, they also cause great concern about privacy leakage. In particular, location queries can be used to infer users’ sensitive private information, such as home addresses, places of work and appointment locations. Hence, many schemes providing query anonymity have been proposed, but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS. To address this challenge, a novel dual privacy-preserving scheme (DPPS) is proposed that includes two privacy protection mechanisms. First, to prevent privacy disclosure caused by correlations between locations, a correlation model is proposed based on a hidden Markov model (HMM) to simulate users’ mobility and the adversary’s prediction probability. Second, to provide query probability anonymity of each single location, an advanced k-anonymity algorithm is proposed to construct cloaking regions, in which realistic and indistinguishable dummy locations are generated. To validate the effectiveness and efficiency of DPPS, theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft, i.e., GeoLife dataset.

Graphical abstract

Keywords

location-based services / privacy-preserving / hidden Markov model / k-anonymity / query probability

Long Li received his PhD degree from Guilin University of Electronic Technology, China in 2018. He is now a lecturer with the School of Computer Science and Information Security, Guilin University of Electronic Technology, and undertakes postdoctoral research in Jinan University, China. His research interests include cryptographic protocols, privacy-preserving technologies and AI security

Jianbo Huang received his MS degree from Guilin University of Electronic Technology, China in 2020. He is currently working in Nanning Campus, Guilin University of Technology, China. His research interests include location privacy protection and big data processing

Liang Chang received his PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently a Professor with the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. His research interests include information security, knowledge representation and reasoning, description logics, and the semantic Web

Jian Weng received his PhD degree from Shanghai Jiao Tong University, China in 2008. He is a professor with the College of Information Science and Technology, Jinan University, China. His research interests include public key cryptography, cloud security, etc. He has published 80 papers in international conferences and journals such as CRYPTO, EUROCRYPT, ASIACRYPT, TCC, PKC, CT-RSA, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Dependable and Secure Computing

Jia Chen, PhD candidate. He is now a lecturer with the Department of Computer Applications, Guilin University of Technology, China. His main research interests include network architecture, network security and big data processing

Jingjing Li received her PhD degree from Guilin University of Electronic Technology, China in 2020. She is now a lecturer with the College of Cyber Security, Jinan University, China. Her research interests include cryptographic protocols, machine learning and AI security

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62172350), the Fundamental Research Funds for the Central Universities (No. 21621028) and the Innovation Project of GUET Graduate Education (No. 2022YCXS083).

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