A semantic k-anonymity privacy protection method for publishing sparse location data

被引:2
|
作者
Yang, Xudong [1 ]
Gao, Ling [1 ]
Wang, Hai [1 ]
Zheng, Jie [1 ]
Guo, Hongbo [1 ]
机构
[1] Northwest Univ, Dept Informat Sci & Technol, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
k-anonymity; privacy protection; semantic privacy protection;
D O I
10.1109/CBD.2019.00047
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of location technology, location-based services greatly facilitate people's life. However, due to the location information contains a large amount of user sensitive informations,the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data. To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set.Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.
引用
收藏
页码:216 / 222
页数:7
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