title: Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.
abstract: This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.
authors.affiliation: School of Computing and Mathematics, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW, 2678, Australia, yalsaggaf@csu.edu.au.
authors.forename: Yeslam
authors.initials: Y
authors.lastname: Al-Saggaf
authors.forename: Md Zahidul
authors.initials: MZ
authors.lastname: Islam
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000941:ethics / Q000379:methods; D016208:Databases, Factual; D003663:Decision Trees; D006801:Humans; D018907:Privacy; D061108:Social Media; D060756:Social Networking