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

References

[1] Hua J, Liu Y, Shen Y, Tian X, Luo Y, Jin C . A privacy-enhancing scheme against contextual knowledge-based attacks in location-based services. Frontiers of Computer Science, 2020, 14( 3): 143605

[2] Zhang S, Li X, Tan Z, Peng T, Wang G . A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services. Future Generation Computer Systems, 2019, 94: 40–50

[3] Lim K H, Chan J, Karunasekera S, Leckie C . Tour recommendation and trip planning using location-based social media: a survey. Knowledge and Information Systems, 2019, 60( 3): 1247–1275

[4] Liu H, Jin C, Zhou A . Popular route planning with travel cost estimation from trajectories. Frontiers of Computer Science, 2020, 14( 1): 191–207

[5] Yu H, Jia X, Zhang H, Yu X, Shu J . PSRide: Privacy-preserving shared ride matching for online ride hailing systems. IEEE Transactions on Dependable and Secure Computing, 2021, 18( 3): 1425–1440

[6] Jiang H, Li J, Zhao P, Zeng F, Xiao Z, Iyengar A . Location privacy-preserving mechanisms in location-based services: a comprehensive survey. ACM Computing Surveys, 2021, 54( 1): 4

[7] Zhang J, Li C, Wang B . A performance tunable CPIR-based privacy protection method for location based service. Information Sciences, 2022, 589: 440–458

[8] Vidyalakshmi B S, Wong R K, Chi C H . Health mentions on twitter: a case study to identify privacy leaks. IEEE Consumer Electronics Magazine, 2020, 9( 5): 85–90

[9] Wang H, Xu Z, Zhang X, Peng X, Li K . An optimal differentially private data release mechanism with constrained error. Frontiers of Computer Science, 2022, 16( 1): 161608

[10] Zhao P, Li J, Zeng F, Xiao F, Wang C, Jiang H . ILLIA: Enabling K-anonymity-based privacy preserving against location injection attacks in continuous LBS queries. IEEE Internet of Things Journal, 2018, 5( 2): 1033–1042

[11] Wang N, Fu J, Zeng J, Bhargava B K . Source-location privacy full protection in wireless sensor networks. Information Sciences, 2018, 444: 105–121

[12] Yang Y, Zheng X, Guo W, Liu X, Chang V . Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system. Information Sciences, 2019, 479: 567–592

[13] Zhang Z, Qi X, Wang Y, Jin C, Mao J, Zhou A . Distributed top−k similarity query on big trajectory streams. Frontiers of Computer Science, 2019, 13( 3): 647–664

[14] Zhang S, Mao X, Choo K K R, Peng T, Wang G . A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services. Information Sciences, 2020, 527( 2): 406–419

[15] Kido H, Yanagisawa Y, Satoh T. An anonymous communication technique using dummies for location-based services. In: Proceedings of International Conference on Pervasive Services, 2005. 2005, 88−97

[16] Niu B, Li Q, Zhu X, Cao G, Li H. Achieving k-anonymity in privacy-aware location-based services. In: Proceedings of IEEE INFOCOM 2014-IEEE Conference on Computer Communications. 2014, 754−762

[17] Sun G, Chang V, Ramachandran M, Sun Z, Li G, Yu H, Liao D . Efficient location privacy algorithm for Internet of Things (IoT) services and applications. Journal of Network and Computer Applications, 2017, 89: 3–13

[18] Sun G, Cai S, Yu H, Maharjan S, Chang V, Du X, Guizani M . Location privacy preservation for mobile users in location-based services. IEEE Access, 2019, 7: 87425–87438

[19] Sun Y, Chen M, Hu L, Qian Y, Hassan M M . ASA: Against statistical attacks for privacy-aware users in Location Based Service. Future Generation Computer Systems, 2017, 70: 48–58

[20] Peng T, Liu Q, Wang G . Enhanced location privacy preserving scheme in location-based services. IEEE Systems Journal, 2014, 11( 1): 219–230

[21] Zhang S, Choo K K R, Liu Q, Wang G . Enhancing privacy through uniform grid and caching in location-based services. Future Generation Computer Systems, 2018, 86: 881–892

[22] Yadav V K, Verma S, Venkatesan S . Linkable privacy-preserving scheme for location-based services. IEEE Transactions on Intelligent Transportation Systems, 2022, 23( 7): 7998–8012

[23] Peng T, Liu Q, Meng D, Wang G . Collaborative trajectory privacy preserving scheme in location-based services. Information Sciences, 2017, 387: 165–179

[24] Shaham S, Ding M, Liu B, Lin Z H, Li J. Transition-Entropy: a novel metric for privacy preservation in location-based services. In: Proceedings of IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 2019, 1−6

[25] Shaham S, Ding M, Liu B, Dang S, Lin Z, Li J . Privacy preservation in location-based services: A novel metric and attack model. IEEE Transactions on Mobile Computing, 2021, 20( 10): 3006–3019

[26] Wu Z, Wang R, Li Q, Lian X, Xu G, Chen E, Liu X . A location privacy-preserving system based on query range cover-up or location-based services. IEEE Transactions on Vehicular Technology, 2020, 69( 5): 5244–5254

[27] Kuang L, Wang Y, Zheng X, Huang L, Sheng Y . Using location semantics to realize personalized road network location privacy protection. EURASIP Journal on Wireless Communications and Networking, 2020, 2020( 1): 1

[28] Tan Z, Wang C, Yan C, Zhou M, Jiang C . Protecting privacy of location-based services in road networks. IEEE Transactions on Intelligent Transportation Systems, 2020, 22( 10): 6435–6448

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