Affiliation(s):
School of Computer Science and Technology, Anhui University of Technology, Maanshan, China;
moreAffiliation(s): School of Computer Science and Technology, Anhui University of Technology, Maanshan, China; Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Maanshan, China; Engineering Research Institute, Anhui University of Technology, Maanshan, China; Shengli No.1 Middle School of Dongying City, Shandong, China;
less
Tao TAO, Funan ZHANG, Xiujun WANG, Xiao ZHENG, Xin ZHAO. An efficient online histogram publication method for data streams with local differential privacy[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300368
@article{title="An efficient online histogram publication method for data streams with local differential privacy", author="Tao TAO, Funan ZHANG, Xiujun WANG, Xiao ZHENG, Xin ZHAO", journal="Frontiers of Information Technology & Electronic Engineering", year="in press", publisher="Zhejiang University Press & Springer", doi="https://doi.org/10.1631/FITEE.2300368" }
%0 Journal Article %T An efficient online histogram publication method for data streams with local differential privacy %A Tao TAO %A Funan ZHANG %A Xiujun WANG %A Xiao ZHENG %A Xin ZHAO %J Frontiers of Information Technology & Electronic Engineering %P %@ 2095-9184 %D in press %I Zhejiang University Press & Springer doi="https://doi.org/10.1631/FITEE.2300368"
TY - JOUR T1 - An efficient online histogram publication method for data streams with local differential privacy A1 - Tao TAO A1 - Funan ZHANG A1 - Xiujun WANG A1 - Xiao ZHENG A1 - Xin ZHAO J0 - Frontiers of Information Technology & Electronic Engineering SP - EP - %@ 2095-9184 Y1 - in press PB - Zhejiang University Press & Springer ER - doi="https://doi.org/10.1631/FITEE.2300368"
Abstract: Many areas are now experiencing data streams that contain privacy-sensitive information. Although the sharing and release of these data are of great commercial value, if these data are released directly, the private user information in the data will be disclosed. Therefore, how to continuously generate publishable histograms (meeting privacy protection requirements) based on sliding data-stream windows has become a critical issue, especially when sending data to an untrusted third party. Existing histogram publication methods are unsatisfactory in terms of time and storage costs, because they must cache all elements in the current sliding window (SW). Our work addresses this drawback by designing the Efficient Online Histogram Publication (EOHP) method for LDP data streams. Specifically, in the EOHP method, the data collector first crafts a histogram of the current SW using an approximate counting method. Second, the data collector reduces the privacy budget by using the Optimized Budget Absorption (OBA) mechanism and adds appropriate noise to the approximate histogram, making it possible to publish the histogram while retaining satisfactory data utility. Extensive experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage cost and improves data utility compared to other existing algorithms.
Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference
Open peer comments: Debate/Discuss/Question/Opinion
Open peer comments: Debate/Discuss/Question/Opinion
<1>