Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks | Zendy

Jinquan Zhang | Zendy; Bowen Zhao | Zendy; Song Guochao | Zendy; Lii | Zendy; Jiguo Yu | Zendy

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

Author(s) -

Jinquan Zhang,

Bowen Zhao,

Song Guochao,

Lii,

Jiguo Yu

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.01.190

Subject(s) - computer science , data publishing , cluster analysis , feature (linguistics) , tree (set theory) , data mining , publishing , k anonymity , information privacy , internet privacy , machine learning , mathematical analysis , linguistics , philosophy , mathematics , political science , law

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.

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