Deferentially Private Tagging Recommendation based on Topic Model

Deferentially Private Tagging Recommendation based on Topic Model

时间：2013年11月18日 地点：九龙湖校区计算机学院四楼会议室

报告简介：

Tagging recommender system allows Internet users to annotate resources with personalized tags. The connection among users, resources and annotations, often referred to as folksonomy, provides users the freedom to explore tag, and obtain recommendations. The release of these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender system is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from the tagging dataset with only a little background information. Recently, several privacy techniques have been proposed to address the problem, but most of these lack a strict privacy notion, and rarely prevent individuals being re-identified from the dataset. We proposes a privacy preserving tag release algorithm, PriTop, which is designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset. The proposed PriTop algorithm includes three privacy preserving operations: Private Topic Model Generation structures the uncontrolled tags, Private Weight Perturbation adds Laplace noise into the weights to hide the numbers of tags; while Private Tag Selection finally finds the most suitable replacement tags for the original tags, so the exact tags can be hidden. We present extensive experimental results on four real world datasets, Delicious, MovieLens, Last.fm and BibSonomy. While the recommendation algorithm is successful in all the cases, our results further suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy.

报告人简介：

Gang Li, PhD (2005), Senior Lecturer, Director of TULIP Lab, School of Information Technology at Deakin University (Australia). His research interest are in the area of data mining, machine learning and business intelligence. He has co-authored three papers that won best paper prizes, including the ACM/IEEE ASONAM2012 best paper award, the 2007 Nightingale Prize by Springer journal Medical and Biological Engineering and Computing. He has also conducted research projects on tourism and hospitality management, and served on the Program Committee for over 60 international conferences in artificial intelligence, data mining and machine learning, tourism and hospitality management. He is a regular reviewer for International Journals in the areas of data mining, computer network, web services, and business intelligence. He has been the organizing chair for behavior informatics workshops, and the guest editor for the Journal of Networks, and Future Generation Computer Systems (Elsevier).

计算机网络和信息集成教育部重点实验室（东南大学）