Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering

doi:10.3390/ijgi11070404

	Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering
	Yan, Yan 1; Sun, Zichao 1; Mahmood, Adnan 2; Xu, Fei 1; Dong, Zhuoyue 1; Sheng, Quan Z.2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2022-07-01
卷号	11 期号:7
关键词	statistical release of big data location privacy differential privacy privacy spatial decomposition grid clustering
DOI	10.3390/ijgi11070404
英文摘要	Statistical partitioning and publishing is commonly used in location-based big data services to address queries such as the number of points of interest, available vehicles, traffic flows, infected patients, etc., within a certain range. Adding noise perturbation to the location-based statistical data according to the differential privacy model can reduce various risks caused by location privacy leakage while keeping the statistical characteristics of the published data. The traditional statistical partitioning and publishing methods realize the decomposition and indexing of 2D space from top to bottom. However, they can easily cause the over-partitioning or under-partitioning phenomenon, and therefore need multiple times of data scan. This paper proposes a grid clustering and differential privacy protection method for location-based statistical big data publishing scenarios. We implement location-based big data statistics in units of equal-sized grids and perform density classification on uniformly distributed grids by discrete wavelet transform. A bottom-up grid clustering algorithm is designed to perform on the blank and the uniform grids of the same density level based on neighborhood similarity. The Laplacian noise is incorporated into the clustering results according to the differential privacy model to form the published statistics. Experimental comparison of the real-world datasets manifests that the grid clustering and differential privacy publishing method proposed in this paper is superior to other existing partition publishing methods in terms of range querying accuracy and algorithm operating efficiency.
WOS研究方向	Computer Science ; Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000833275000001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159449]
专题	计算机与通信学院
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Macquarie Univ, Fac Sci & Engn, Sch Comp, Sydney, NSW 2109, Australia
推荐引用方式 GB/T 7714	Yan, Yan,Sun, Zichao,Mahmood, Adnan,et al. Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2022,11(7).
APA	Yan, Yan,Sun, Zichao,Mahmood, Adnan,Xu, Fei,Dong, Zhuoyue,&Sheng, Quan Z..(2022).Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,11(7).
MLA	Yan, Yan,et al."Achieving Differential Privacy Publishing of Location-Based Statistical Data Using Grid Clustering".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11.7(2022).