A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing

doi:10.1007/s00521-021-06815-9

	A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing
	Zhang, Xuejun 1; He, Fucun 1; Chen, Qian 1,2; Jiang, Xinlong 1,2; Bao, Junda ; Ren, Tongwei 1,3; Du, Xiaogang 1
	2022-01-28
发表期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
页码	22
摘要	As an enabling technology for edge computing scenarios, indoor localization has a broad prospect in a variety of location-based applications, such as tracking, navigating, and monitoring in indoor environments. In order to improve the location accuracy, numerous machine learning (ML)-based indoor localization schemes with fingerprint fusion have been proposed recently, which take advantage of the fusion of signal gathered from multiple wireless technologies (e.g., WiFi and BLE) and require a site survey to construct the fingerprint database. However, most solutions are based on cloud framework and thus pose a serious privacy leakage because users' sensitive information (e.g., locations) is computed from the fingerprint database by the untrusted localization service provider. Furthermore, the site survey is time-consuming and labor-intensive. In this paper, we propose a differentially private fingerprint fusion semi-supervised extreme learning machine for indoor localization in the edge computing, called Adp-FSELM. The Adp-FSELM firstly employs a multi-level edge networkbased privacy-preserving system framework to meet the requirements of ML-based fingerprint indoor localization for lightweight, low latency, and real-time response. Then, the Adp-FSELM extends the e-differential privacy to the fingerprint fusion semi-supervised extreme learning machine for indoor localization in edge computing through a three-phase private process consisting of private labeled sample obfuscation, differentially private feature fusion, and differentially private model training. Theoretical and comprehensive experimental results in real indoor environments demonstrate that the AdpFSELM provides a high e-differential privacy guarantee for users' location privacy while reducing human calibration effort and effectively resists Bayesian inference attacks. Compared with the existing semi-supervised learning-based localization methods, the mean absolute error of location accuracy of the Adp-FSELM is restricted to 2.22% at most, and the additional time consumption can be almost ignored. Thus, our mechanism can balance the trade-off among location privacy, location accuracy, and time consumption.
关键词	Location privacy Edge computing Differential privacy Fusion semi-supervised extreme learning machine Indoor localization
DOI	10.1007/s00521-021-06815-9
收录类别	SCI
语种	英语
资助项目	NSFC project[61762058] ; NSFC project[61902379] ; NSFC project[61861024] ; Natural Science Foundation of Gansu Province[21JR7RA282] ; Natural Science Foundation of Gansu Province[20JR5RA404] ; Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University ; Science and Technology Project of State Grid Gansu Electric Power Institute[52272219100P]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000747641400004
出版者	SPRINGER LONDON LTD
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18209
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Xuejun
作者单位	1.Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Worcester Polytech Inst, Dept Comp Sci, Worcester, MA 01609 USA
推荐引用方式 GB/T 7714	Zhang, Xuejun,He, Fucun,Chen, Qian,et al. A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing[J]. NEURAL COMPUTING & APPLICATIONS,2022:22.
APA	Zhang, Xuejun.,He, Fucun.,Chen, Qian.,Jiang, Xinlong.,Bao, Junda.,...&Du, Xiaogang.(2022).A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing.NEURAL COMPUTING & APPLICATIONS,22.
MLA	Zhang, Xuejun,et al."A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing".NEURAL COMPUTING & APPLICATIONS (2022):22.