Most Influential Paper Award Talk
On Link Privacy in Social Networks(Presentation Slides)
Link prediction in social networks has been well studied in past two decades. Many applications of social networks require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of relationship. In the first part of this talk, we first revisit earlier works on social network anonymization and then presented our paper “On link privacy in randomizing social networks”, which also received best student paper runner-up award in PAKDD 2009. The work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not well protect link privacy. The paper investigated how well an edge based graph randomization approach can protect sensitive links and showed via theoretical studies and empirical evaluations that various similarity measures can be exploited by attackers to significantly improve their confidence and accuracy of predicted sensitive links. In the second part of the talk, we discuss research works in the past decade on link and/or node privacy protection including differential privacy preserving graph data release, network embedding, and graph mining. We also present challenges and future directions on privacy protection in social networks.
Xiaowei Ying received his Bachelor Degree from Fudan University in 2006, and Ph.D. degree in Information System from University of North Carolina at Charlotte in 2011. His research mainly focused on privacy preserving data mining techniques and spectral analysis in social network data. After Ph.D. research, he has been working in industry, developing machine learning techniques in various applications such as fraud detection and ads recommendation. He is currently Sr. Scientist at Pandora Media, mainly focusing on ad effectiveness and recommendation.
Dr. Xintao Wu
Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at the University of Arkansas. He got his BS degree in Information Science from the University of Science and Technology of China in 1994, ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and Ph.D. in Information Technology from George Mason University in 2001. Dr. Wu's major research interests include data mining, privacy and security, fairness aware learning, and big data analysis. Dr. Wu has published over 120 scholarly papers and served on editorial boards of six international journals and many program committees of top international conferences in data mining and AI. Dr. Wu is also a recipient of NSF CAREER Award (2006) and several paper awards including PAKDD'13 Best Application Paper Award, BIBM'13 Best Paper Award, CNS'19 Best Paper Award, and PAKDD'19 Most Influential Paper Award.