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学术讲座【Recent Studies in Social Networks and Privacy Preservation】

时间:2012-06-15浏览:509设置

时间:2012年6月19日(星期二) 下午14:30

地点:成功楼603教室

主讲:台湾高雄大学 王學亮教授

主办:数计学院

专家简介:Leon Shyue-Liang Wang received his Ph.D. from State University of New York at Stony Brook in 1984.  From 1984 to 1994, he joined the University of New Haven and New York Institute of Technology as assistant/associate professor.  From 1994 to 2002, he joined I-Shou University in Taiwan and served as director of computing center, chairman of information management department, and director of library. From 2003 to 2007, he rejoined NYIT.  From 2009 to 2011, he was professor and chairman of Information Management Department at National University of Kaohsiung, Taiwan.  He is now Dean of College of Management. He has published over 180 papers in the areas of data mining, privacy preservation, soft computing, and served as PC member and session chair of more than 50 international conferences.  He is a member of the board of Chinese American Academic and Professional Society, USA.

报告摘要:In recent years, social network research has advanced significantly.  People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of social network analysis and mining in academia, politics, homeland security and business.  However, growing popularity of on-line social networking, not only brings the convenience of information sharing but also concerns of privacy breaches, as sensitive information, through links and inferences, individual’s identity, health, financial status, political affiliations, etc, might be disclosed.  In order to preserve privacy of users, anonymization is required prior to attempts to make the data more widely available to public.  In this talk, we will first review the basic concepts of social networks analysis and then concentrate on introducing recent studies in privacy preservation on published data such as relational, transactional, social network data, and spatial data.

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