Enhancing frequent location privacy-preserving strategy based on geo-Indistinguishability

Luo, Huiwen; Zhang, Haoming; Long, Shigong; Lin, Yiming

doi:10.1007/s11042-021-10789-0

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…In order to test the performance of the location privacy protection method proposed in this paper, the algorithm has been fully experimented in terms of data availability, privacy protection degree, and algorithm running time. The experiment is implemented using Python, and the data sets are Gowalla data set and Geolife data set [18,19]. The experimental environment of this article is PyCharm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Differential Privacy Location Protection Method Based on the Markov Model

Wang

Feng

et al. 2021

Wireless Communications and Mobile Computing

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Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Differential Privacy Location Protection Method Based on the Markov Model

Wang

Feng

et al. 2021

Wireless Communications and Mobile Computing

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“…Arain et al 31 proposes an algorithm to protect the information of mobile vehicle’s users and use geo-indistinguishability to obtain a set of POIs near the source location and destination location. Luo et al 32 first classified the location set through a density-based clustering algorithm and then perturbed the real locations according to geo-indistinguishability so as to solve the problem of privacy leakage caused by frequent check-in. Xiong et al 33 applied geo-indistinguishability to spatial crowdsourcing and combined location obfuscation and path optimization to provided strong privacy protection with minimal cost.…”

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%

“…Arain et al 31 proposes an algorithm to protect the information of mobile vehicle's users and use geo-indistinguishability to obtain a set of POIs near the source location and destination location. Luo et al 32 34 investigated the potential correlations between obfuscated locations generated according to geo-indistinguishability in continuous query services.The location perturbation mechanism based on geo-indistinguishability releases approximate locations to obtain corresponding location services. Therefore, the quality of location-based service obtained by users varies with the fluctuation of the distance between the disturbed location and the real location.…”

Section: Related Workmentioning

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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“…In our experiments, DPLPA and L-cluster algorithms are implemented in Python and runs on Windows10 platform with 3.6GHz CPU and 8.00 random access memory (RAM). The datasets used in experiment are real datasets of Geolife [29] and Gowalla [30]. Then we compared DPLPA with P-STM algorithm and LPPA-PSRDU algorithm.…”

Section: Experimental Settingmentioning

confidence: 99%

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

Wang

Guo

et al. 2022

Preprint

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Location-based services (LBSS) are widely used with the rapid development of mobile devices and location technology. LBSs not only brings convenience to life, but also brings privacy threats to users. Users upload accurate location information to LBSs to obtain the corresponding services. However, uploading unprocessed location data will directly cause the leakage of users’ privacy information. In this work, an efficient location protection method based on differential privacy is proposed. First of all, the users’ locations information is merged based on the distance and density relationship between multiple groups of continuous locations, and a location clustering (L-cluster) algorithm is proposed to divide the continuous locations into various clusters. Then, differential privacy based location protection algorithm (DPLPA) is proposed to efficiently preserve users’ location privacy, where Laplace noise conforming to the differential privacy mechanism is added to resident points and centroids in the cluster to protect the location privacy. Experimental results show that DPLPA gets high data utility and low time consumption while protecting the privacy of location information.

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Enhancing frequent location privacy-preserving strategy based on geo-Indistinguishability

Cited by 10 publications

References 21 publications

Differential Privacy Location Protection Method Based on the Markov Model

Differential Privacy Location Protection Method Based on the Markov Model

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

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

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