Streaming Data Collection With a Private Sketch-Based Protocol

doi:10.1109/JIOT.2024.3397908

	Streaming Data Collection With a Private Sketch-Based Protocol
	Li, Ying 1,2; Lee, Xiaodong 1; Peng, Botao 1; Palpanas, Themis 3,4; Xue, Jingan 5
	2024-08-01
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
卷号	11 期号:15 页码:25950-25967
摘要	Data stream collection is critical to analyze service conditions and detect anomalies in time, especially in Internet of Things. However, it may undermine the individual privacy. Local differential privacy (LDP) has recently become a popular privacy-preserving technique protecting users' privacy. However, most of them are still limited to the assumption of one-item collection, resulting in poor utility when extended to the multi-item collection from a very large domain. This article proposes a private streaming data collection framework, private sketch-based framework (PSF), which takes advantage of sketches. Combining the proposed background information and a decode-first collection-side workflow, the framework improves the utility by reducing the errors introduced by the sketching algorithm and the privacy budget utilization when collecting multiple items. We analytically prove the superior accuracy and privacy characteristics of PSF. In order to support specific computing tasks, we build two private protocols based on PSF, PrivSketch and PrivSketch+, aiming at frequency estimation and mean estimation, respectively. We demonstrate the utility of PrivSketch and PrivSketch+ theoretically, and also evaluate them experimentally. Our evaluation, with several diverse synthetic and real data sets, demonstrates that PrivSketch is 1-3 orders of magnitude better than the competitors in terms of utility in both frequency estimation and frequent item estimation, while being up to similar to 100x faster. PrivSketch+ performs similar to 4 orders of magnitude better than advanced solutions, such as piecewise mechanism (PM) and hybrid mechanism (HM), under a limited privacy budget.
关键词	Privacy Estimation Data collection Frequency estimation Protocols Protection Internet local differential privacy (LDP) mean estimation sketch
DOI	10.1109/JIOT.2024.3397908
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62202450] ; Huawei[TC20201119008] ; Postdoctoral Exchange Program[YJ20210185]
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:001277988600083
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39628
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Lee, Xiaodong; Peng, Botao
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Lab Internet Infrastruct, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China 3.Univ Paris Cite, LIPADE, F-75006 Paris, France 4.French Univ Inst, F-75006 Paris, France 5.Huawei Technol, Cent Res Inst, Labs 2012, Shenzhen 518129, Peoples R China
推荐引用方式 GB/T 7714	Li, Ying,Lee, Xiaodong,Peng, Botao,et al. Streaming Data Collection With a Private Sketch-Based Protocol[J]. IEEE INTERNET OF THINGS JOURNAL,2024,11(15):25950-25967.
APA	Li, Ying,Lee, Xiaodong,Peng, Botao,Palpanas, Themis,&Xue, Jingan.(2024).Streaming Data Collection With a Private Sketch-Based Protocol.IEEE INTERNET OF THINGS JOURNAL,11(15),25950-25967.
MLA	Li, Ying,et al."Streaming Data Collection With a Private Sketch-Based Protocol".IEEE INTERNET OF THINGS JOURNAL 11.15(2024):25950-25967.