科学

IEEE计算机学会南京分会近期将举行2次学术报告会，信息如下。欢迎参加。

IEEE CS Nanjing Chapter

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3月5日（星期五） 15:00-16:15,  南京大学蒙民伟楼404会议室

题目: Reconstruction from Randomized Graph via Low Rank Approximation

报告人: Xintao Wu

              Associate Professor, University of North Carolina at Charlotte

摘要：The privacy concerns associated with data analysis over social networks have spurred recent research on privacy-preserving social network analysis, particularly on privacy-preserving publishing of social network data. In this talk, we focus on whether we can reconstruct a graph  from the edge randomized graph such that accurate feature values can be recovered. In particular, we present a low rank approximation based reconstruction algorithm. We exploit spectral properties of the graph data and show why noise could be separated from the perturbed graph using low rank approximation. We also show key differences from previous findings of point-wise reconstruction methods on numerical data through empirical evaluations and theoretical justifications.

报告人简介：Dr. Xintao Wu is an Associate Professor of Software and Information Systems Department at University of North Carolina at Charlotte. He got his Ph.D. in Information Technology from George Mason University in August 2001, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and an BS degree in Information Science from the University of Science and Technology of China in 1994.  His major research interests include data mining, data privacy and security, and social network analysis. Dr. Wu is an editor of Springer’s Journal of Intelligent Information Systems and Spanish Higher Research Council’s Transaction on Data Privacy, and serves on program committees of many international conferences, including KDD, ICDM, SDM, PKDD, and PAKDD. He is serving as the program co-chair of the 2nd International Symposium on Data, Privacy and E-Commerce (ISDPE’10). Dr. Wu is a recipient of NSF CAREER Award.

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3月8日（星期一） 10:30-11:45,  南京大学蒙民伟楼404会议室

题目: Spectrum based Fraud Detection in Social Networks

报告人: Xintao Wu

              Associate Professor, University of North Carolina at Charlotte

摘要：Social networks have reached an important role in people’s lifes. Unfortunately, social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this work we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. We present an effective algorithm to detect RLAs using the set of suspects filtered by their spectral characteristics.  Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.

报告人简介：Dr. Xintao Wu is an Associate Professor of Software and Information Systems Department at University of North Carolina at Charlotte. He got his Ph.D. in Information Technology from George Mason University in August 2001, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and an BS degree in Information Science from the University of Science and Technology of China in 1994.  His major research interests include data mining, data privacy and security, and social network analysis. Dr. Wu is an editor of Springer’s Journal of Intelligent Information Systems and Spanish Higher Research Council’s Transaction on Data Privacy, and serves on program committees of many international conferences, including KDD, ICDM, SDM, PKDD, and PAKDD. He is serving as the program co-chair of the 2nd International Symposium on Data, Privacy and E-Commerce (ISDPE’10). Dr. Wu is a recipient of NSF CAREER Award.