Semantic Location Privacy Protection Based on Privacy Preference for Road Network

Wang, Yonglu; Kai-zhong, Zuo; Li, Rui; Guo, Lei

doi:10.1007/978-3-030-37352-8_30

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…In order to solve the location privacy leakage problem in LBS, various methods for environments both in free space and road network have been proposed, such as K -anonymity 12 – 18 , local differential privacy 19 – 27 , geo-indistinguishability 10 , 11 , 28 – 36 , and location semantics 37 – 45 .…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, they generated virtual locations for users using the probability density function to achieve K -anonymity with privacy awareness in LBS. Wang et al 18 proposed a greedy strategy to generate secure anonymous regions based on users’ privacy requirements and real-time location. The intersection of anonymous user sets at different times is calculated and user’s identity is updated by using a dynamic pseudonym mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…In order to solve the location privacy leakage problem in LBS, various methods for environments both in free space and road network have been proposed, such as K-anonymity [12][13][14][15][16][17][18] , local differential privacy [19][20][21][22][23][24][25][26][27] , geo-indistinguishability 10,11,[28][29][30][31][32][33][34][35][36] , and location semantics [37][38][39][40][41][42][43][44][45] . Marco Gruteser et al introduced the concept of K-anonymity in relational databases into the field of privacy protection of location-based services and proposed the location K-anonymity model 12 .…”

Section: Related Workmentioning

confidence: 99%

“…The definitions are depicted in the following Eqs. (18) and (19). ( 18) Figure 7 compares the mean of distances generated by different perturbation mechanisms under various privacy parameters over different time periods using the logarithmic coordinates.…”

Section: Comparison Of Service Quality Lossmentioning

confidence: 99%

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Perturb and optimize users’ location privacy using geo-indistinguishability and location semantics

Yan

Mahmood

et al. 2022

Sci Rep

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Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users’ specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services. Moreover, the areas where humans work and live have different location semantics and sensitivities according to their different social functions. Although the privacy protection of a user’s real location can be achieved by the perturbation algorithm, the attackers may employ the semantics information of the perturbed location to infer a user’s real location semantics in an attempt to spy on a user’s privacy to certain extent. In order to mitigate the above semantics inference attack, and further improve the quality of the location-based services, this paper hereby proposes a user side location perturbation and optimization algorithm based on geo-indistinguishability and location semantics. The perturbation area satisfying geo-indistinguishability is thus generated according to the planar Laplace mechanism and optimized by combining the semantics information and time characteristics of the location. The optimum perturbed location that is able to satisfy the minimum loss of location-based service quality is selected via a linear programming method, and can be employed to replace the real location of the user so as to prevent the leakage of the privacy. Experimental comparison of the actual road network and location semantics dataset manifests that the proposed method reduces approximately 37% perturbation distance in contrast to the other state-of-the-art methods, maintains considerably lower similarity of location semantics, and improves region counting query accuracy by a margin of around 40%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%