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Title	Vehicle-Based Secure Location Clustering For Iot-Equipped Building And Facility Management In Smart City
ID_Doc	60973
Authors	Wu H.; Li L.; Liu Y.; Wu X.
Year	2022
Published	Building and Environment, 214
DOI	http://dx.doi.org/10.1016/j.buildenv.2022.108937
Abstract	Vehicles equipped with various sensing devices have the strong ability to generate location information, which is beneficial to a lot of applications in smart city. In special, it is important to take full advantage of the traffic information to manage the buildings and facilities with special services, including charging/gas stations, car parks and leisure centers. However, location data are outsourced to the third party (e.g., vehicles and the vehicular cloud), which causes high security risks. In this paper, we first define a personalized local differential privacy criterion for location data, which offers a strong private guarantee to drivers. Then, we propose a density-based location clustering mechanism, i.e., PLPP-CLIQUE, which not only satisfies the proposed privacy criterion but also maximizes the utility of vehicles for location-based services. Moreover, we design a more efficient mechanism, i.e., Improved-PLPP-CLIQUE, to increase the utility of location clustering for vehicular clouds under the same privacy requirement. We demonstrate that our mechanisms satisfy the privacy criterion and have the high utility by theoretical analysis. Finally, we perform extensive experiments to show the good capability of our mechanisms. © 2022
Author Keywords	Building and facility planning; Location clustering; Privacy preservation; Utility optimization

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