k-Anonymity Location Privacy Algorithm Based on Clustering

• Published in: IEEE access Vol. 6; pp. 28328 - 28338
• Main Authors: Zheng, Lijuan, Yue, Huanhuan, Li, Zhaoxuan, Pan, Xiao, Wu, Mei, Yang, Fan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Education; Information security; k-anonymity; location privacy; Outliers (statistics); Partitioning algorithms; Privacy; quality of query service; Queries; Security; Servers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2780111

The accuracy of user location information is inversely proportional to the user's privacy preserving degree <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>, and is proportional to quality of query service. In order to balance the conflict between privacy preserving security and query quality caused by the accuracy of location information, a clustering algorithm aiming at eliminating outliers based on the k-anonymity location privacy preserving model is proposed, which is used to realize the establishment of anonymous group in the anonymous model. The distribution of user in the anonymous group is optimized. The idea of replacing the user location query by the center of the anonymous group is proposed. The number of repeated queries is reduced, and the quality of query service is improved on the premise of ensuring security through the experimental analysis and comparison with other schemes.