学术报告
同济大学计算机科学与技术系智信讲坛第(107)期
题目：Privacy Characterization and Utility Trade-offs in Data Publishing
报告人：Jian Ren
时间：2017年7月24日周一上午10:00
地点：电信楼403
组织单位：计算机科学与技术系
邀请人：吴俊教授
报告人简介：
Jian Ren received his B.S. degree and M.S. degree from Shaanxi Normal University, China. He received the Ph.D. degree from the Xidian University, China. Currently, he is an Associate Professor in the Department of Electrical and Computer Engineering at Michigan State University, East Lansing, MI. Prior to joining MSU, Dr. Ren was the Leading Secure Architect at Avaya Lab, Bell Lab and Racal Datacom in security architecture and solution development.
His most recent research interests include network security, cost-aware security protocol design, privacy-preserving communications, cloud computing, optimal distributed storage and smart home/grid. His group recently developed a hardware secure access gateway to enable remote managing and monitoring of ZigBee enabled devices using a mobile phone, protected through one-time login and one-time password based secure access control. Dr. Ren has been the principle investigator of eight NSF funded projects, including the US National Science Foundation Faculty Early Career Development (CAREER) award in 2009. He is a senior member of the IEEE. He served as the TPC Chair for ICNC 2017, TPC co-chairs for IEEE Trustcom 2014 and Chinacom 2011. He is serving as the General Chair for IEEE ICNC 2018.
内容提要：
The increasing interest in collecting and publishing large amounts of data for medical research, market analysis and economical measures has created major privacy concerns. However, data privacy and its usefulness are two conflicting issues. Increase privacy protection will decrease data utility. To characterize this trade off, in this talk, we first present a novel multi-variable privacy characterization and quantification model. Based on this model, we are able to analyze the prior and posterior adversarial belief, and sensitivity of any attribute in privacy characterization. Then we discussed optimal privacy data process under the constraints of data utility. The principles introduced in this research can be applied in many scenarios to address the privacy-utility trade offs.
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