标题:Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks
作者:Zhang, Jinquan; Zhao, Bowen; Song, Guochao; Ni, Lina; Yu, Jiguo
通讯作者:Ni, LN;Ni, LN;Ni, LN
作者机构:[Zhang, Jinquan; Zhao, Bowen; Song, Guochao; Ni, Lina] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Shandong, Peoples R China.; 更多
会议名称:7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)
会议日期:OCT 19-21, 2018
来源:2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
出版年:2019
卷:147
页码:643-646
DOI:10.1016/j.procs.2019.01.190
关键词:Privacy protection; Data publishing; Social networks;; (alpha,L)-diversity; K-anonymity; Clustering feature (CF) tree
摘要:Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm. (C) 2019 The Authors. Published by Elsevier B.V.
收录类别:CPCI-S
WOS核心被引频次:1
资源类型:会议论文
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