In this work we study information leakage through discussions in online social networks. In particular, we focus on articles published by news pages, in which a person's name is censored, and we examine whether the person is identifiable (decensored) by analyzing comments and social network graphs of commenters. As a case study for our proposed methodology, in this paper we considered 48 articles (Israeli, military related) with censored content, followed by a threaded discussion. We qualitatively study the set of comments and identify comments (in this case referred as "leakers") and the commenter and the censored person. We denote these commenters as "leakers". We found that such comments are present for some 75% of the articles we considered. Finally, leveraging the social network graphs of the leakers, and specifically the overlap among the graphs of the leakers, we are able to identify the censored person. We show the viability of our methodology through some illustrative use cases.
|Title of host publication||Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015|
|Editors||Jian Pei, Jie Tang, Fabrizio Silvestri|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||6|
|State||Published - 25 Aug 2015|
|Event||IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France|
Duration: 25 Aug 2015 → 28 Aug 2015
|Name||Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015|
|Conference||IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015|
|Period||25/08/15 → 28/08/15|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission under the agreement No. PCIG11-GA-2012-321980. This work is also partially supported by the TENACE PRIN Project 20103P34XC funded by the Italian MIUR, and by the Project "Tackling Mobile Malware with Innovative Machine Learning Techniques" funded by the University of Padua. This research was partially funded by Israel Ministry of Science and Technology research grant 3-9770 Data Leakage in Social Networks: Detection and Prevention.
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