Paper Info
Title
k-Anonymity Location Privacy Algorithm Based on Clustering.
Abstract
The accuracy of user location information is inversely proportional to the user's privacy preserving degree k, 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.
Year
DOI
Venue
2018
10.1109/ACCESS.2017.2780111
IEEE ACCESS
Keywords
Field
DocType
Clustering,k-anonymity,location privacy,quality of query service
Data mining,Computer science,Server,k-anonymity,Outlier,Computer network,Information security,Premise,Cluster analysis
Journal
Volume
ISSN
Citations 
6
2169-3536
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Lijuan Zheng1619.56
Huanhuan Yue210.68
Zhaoxuan Li310.68
Xiao Pan4128.24
Mei Wu510.35
Fan Yang619648.38