Location Privacy Protection Scheme for LBS in IoT. (17th August 2021)

• Record Type: Journal Article
• Authors: Li, Hongtao; Xue, Xingsi; Li, Zhiying; Li, Long; Xiong, Jinbo
• Other Names: Pu Cong Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-08-17
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/9948543
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user's location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user's location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user's location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.