Anonymization for multiple released social network graphs

政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 > Item 140.119/74630

Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/74630

Title:	Anonymization for multiple released social network graphs
Authors:	Wang, C.-J.L.;Wang, E.T.;Chen, Arbee L P
Contributors:	資科系
Keywords:	Anonymization;Data utilities;Effect of time;Privacy preservation;Privacy preserving;Query answering;Real data sets;Time-serial data;Data mining;Data privacy;Graphic methods;Time series analysis;Social networking (online)
Date:	2013
Issue Date:	2015-04-16 17:30:33 (UTC+8)
Abstract:	Recently, people share their information via social platforms such as Facebook and Twitter in their daily life. Social networks on the Internet can be regarded as a microcosm of the real world and worth being analyzed. Since the data in social networks can be private and sensitive, privacy preservation in social networks has been a focused study. Previous works develop anonymization methods for a single social network represented by a single graph, which are not enough for the analysis on the evolution of the social network. In this paper, we study the privacy preserving problem considering the evolution of a social network. A time-series of social network graphs representing the evolution of the corresponding social network are anonymized to a sequence of sanitized graphs to be released for further analysis. We point out that naively applying the existing approaches to each time-series graph will break the privacy purposes, and propose an effective anonymization method extended from an existing approach, which takes into account the effect of time for releasing multiple anonymized graphs at one time. We use two real datasets to test our method and the experiment results demonstrate that our method is very effective in terms of data utility for query answering. © Springer-Verlag 2013.
Relation:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),Volume 7819 LNAI(2), Pages 99-110,17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013; Gold Coast, QLD; Australia; 14 April 2013 到 17 April 2013; 代碼 102450 10.1007/978-3-642-37456-2_9
Data Type:	conference
Appears in Collections:	[資訊科學系] 會議論文

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