Location big data differential privacy dynamic partition release method

被引：0
|
作者：
Yan Y. [1 ]
Zhang L.X. [1 ]
Wang B.Q. [1 ]
Gao X. [1 ]
机构：
[1] School of Computer and Communication, Lanzhou University of Technology, Lanzhou
来源：
International Journal of Security and Networks | 2020年 / 15卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Differential privacy; DPR; Dynamic partition release; LBD; Location big data; Spatial redundancy; Temporal redundancy;
D O I：
10.1504/IJSN.2020.106505
中图分类号：
学科分类号：
摘要：
Aiming at the privacy protection requirements in real-time statistical publishing process of location big data, a dynamic partition method is proposed based on differential privacy mechanism. The temporal redundancy between adjacent data snapshots has been eliminated by sampling and differential processing of dynamic location big data, and the spatial redundancy of location big data has been reduced by adaptive density meshing and uniformity heuristic quadtree partitioning. Differential privacy protection has been realised by adjusting partition structures of the current dataset on the spatial structure of previous moment and adding Laplace noise. Experiments carried out on the cloud computing platform and real location big datasets show that the proposed algorithm can meet the dynamic partition release requirements of real-time location big data, and the query precision of single-released location big data is better than other similar methods. Copyright © 2020 Inderscience Enterprises Ltd.
引用
收藏
页码：25 / 35
页数：10
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