LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing

Xiong, Ping; Li, Gui-Rong; Ren, Wei; Zhu, Tianqing

doi:10.1007/s12652-021-03266-x

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In order to solve the problem of privacy leakage caused by frequent check‐ins, Luo et al 20 classified the locations through a density‐based clustering algorithm and perturbed the real locations according to geo‐indistinguishability. Xiong et al 21 combined the location obfuscation of geo‐indistinguishability with the path optimization of spatial crowd‐sourcing to provide strong privacy protection with minimal cost. Yan et al 22 suggested to combine location semantic information with geo‐indistinguishability in order to reduce the probability of semantic inference attack and improve the quality of location service.…”

Section: Related Workmentioning

confidence: 99%

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

Yan,

Mahmood

et al. 2024

Concurrency and Computation

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SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

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Section: Related Workmentioning

confidence: 99%

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

Yan,

Mahmood

et al. 2024

Concurrency and Computation

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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][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%

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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“…At present, the research on carpooling service problem mainly focuses on shortening the travel detours and minimizing vehicle mileage and passenger travel cost [27]. However, with the rapid development of electric vehicles and transportation networks in smart cities, vehicle mileage and detours are no longer the primary concerns, and commuting time is more of a problem.…”

Section: Introductionmentioning

confidence: 99%

Time-optimal and privacy preserving route planning for carpool policy

Zhu

et al. 2022

World Wide Web

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To alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.

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LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing

Cited by 6 publications

References 30 publications

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

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

Time-optimal and privacy preserving route planning for carpool policy

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