Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy

doi:10.1109/ACCESS.2019.2951364

IR > 计算机与通信学院

	Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy
	Yan, Yan1,2 ; Zhang, Lianxiu 1; Sheng, Quan Z.2; Wang, Bingqian 1; Gao, Xin 1; Cong, Yiming 1
	2019
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	7 页码:164962-164974
摘要	Data releasing is a key part bridging between the collection of big data and their applications. Traditional methods release the static version of dataset or publish the snapshot with a fixed sampling interval, which cannot meet the dynamic query requirements and query precision for big data. Moreover, the quality of published data cannot reflect the characteristics of the dynamic changes of big data, which often leads to subsequent data analysis and mining errors. This paper proposes an adaptive sampling mechanism and privacy protection method for the release of big location data. In order to reflect the dynamic change of data in time, we design an adaptive sampling mechanism based on the proportional-integral-derivative (PID) controller according to the temporal and spatial correlation of the location data. To ensure the privacy of published data, we propose a heuristic quad-tree partitioning method as well as a corresponding privacy budget allocation strategy. Experiments and analysis prove that the adaptive sampling mechanism proposed in this paper can effectively track the trend of dynamic changes of data, and the designed differential privacy method can improve the accuracy of counting query and enhance the availability of published data under the premise of certain privacy intensity. The proposed methods can also be readily extended to other areas of big data release applications.
关键词	Big location data privacy preserving data publishing adaptive sampling differentia privacy heuristic quad-tree partitioning
DOI	10.1109/ACCESS.2019.2951364
收录类别	SCI ; SCIE
语种	英语
资助项目	National Nature Science Foundation of China[61762059][61762060][61862040]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000498712500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20200208020415
EI主题词	Data privacy
EI分类号	731.1 Control Systems - 903.2 Information Dissemination - 913.3 Quality Assurance and Control - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/76176
专题	计算机与通信学院
通讯作者	Yan, Yan
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China; 2.Macquarie Univ, Fac Sci & Engn, Dept Comp, Sydney, NSW 2109, Australia
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Yan, Yan,Zhang, Lianxiu,Sheng, Quan Z.,et al. Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy[J]. IEEE ACCESS,2019,7:164962-164974.
APA	Yan, Yan,Zhang, Lianxiu,Sheng, Quan Z.,Wang, Bingqian,Gao, Xin,&Cong, Yiming.(2019).Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy.IEEE ACCESS,7,164962-164974.
MLA	Yan, Yan,et al."Dynamic Release of Big Location Data Based on Adaptive Sampling and Differential Privacy".IEEE ACCESS 7(2019):164962-164974.