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Information leakage Detection in Online Social Networks

Date: 2017-03-28
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Information leakage Detection in Online Social Networks

I. Details of the lecture 

Topic: Information leakage Detection in Online Social Networks

Time: 14:00-15:00, March 30, 2017

Location: Room 1-315, FIT Building, Tsinghua University

Speaker: David G.Schwartz  Professor of information systems, former vice-chairman at the Graduate School of Business Administration of Bar-Ilan University, Israel

II. Introduction to the speaker 

David is professor of information systems and former vice-chairman at the Graduate School of Business Administration of Bar-Ilan University, Israel. His research has appeared in publications such as Information Systems Research, IEEE Intelligent Systems, Information Systems, International Journal of Human-Computer Studies, Review of Marketing Science, and the Journal of Organizational Behavior. His books include Cooperating Heterogeneous Systems, Internet-Based Knowledge Management and Organizational Memory, and the Encyclopedia of Knowledge Management. From 1998 to 2011 he served as editor of the journal Internet Research, David earned his Ph.D. in Computer Science from Case Western Reserve University, USA; MBA from McMaster University, Canada; and B.Sc. from the University of Toronto, Canada.

III. Content of lecture 

We investigate the interplay between online news, reader comments, and social networks, to detect and characterize comments leading to the revelation of censored information. Censorship of identity occurs in different contexts - for example, the military censors the identity of personnel and the judiciary censors the identity of minors and victims. We address three questions: (1) assessing the relevance of identity censorship in the presence of user-generated comments, (2) understanding the fashion of censorship circumvention - what people say and how, and (3) determining how comment analysis can aid in identifying de-censorship and information leakage through comments. Examining 3582 comments made on 48 articles containing censored identities, we find that a systematic examination of comments can seriously compromise identity censorship. We introduce DUIL, a system for the Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material nonpublic information.

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