报告题目：Big Data Differential Privacy Preservation for Cyber Physical Systems

报告人：潘淼

报告时间：2019年6月4日下午2时

报告地点：地质宫330海光学术报告厅

主办单位：仪器科学与电气工程学院

Dr. Miao Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at University of Houston. His research interests include cybersecurity, big data privacy, deep learning privacy, cyber-physical systems, and cognitive radio networks. Dr. Pan is an Associate Editor for IEEE Internet of Things (IoT) Journal from 2015 to 2018. Dr. Pan is a member of ACM and a senior member of IEEE and IEEE Communications Society.

He has published more than 50 papers in prestigious journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Mobile Computing, and IEEE Transactions on Smart Grid, and over 80 papers in top conferences such as IEEE INFOCOM, ICDCS, and IEEE IPDPS.

He was a recipient of NSF CAREER Award in 2014. His work won IEEE TCGCC (Technical Committee on Green Communications and Computing) Best Conference Paper Awards 2019, and Best Paper Awards in ICC 2019, VTC 2018, Globecom 2017 and Globecom 2015, respectively. Dr. Pan serves as a Technical Reviewer for many international journals and conferences.

Abstract:A cyber-physical system (CPS) is largely referred to as the next generation of engineered systems with the integration of communication, computation, and control to achieve the goals of stability and efficiency for physical systems. Cyber-physical systems are often collect huge amounts of information for data analysis and decision making. The collection of information helps the system make smart decisions through sophisticated machine learning algorithms. But, it can be an undesirable loss of privacy for the participants, thereby putting their promised benefits at risk. To study the aforementioned issue, we employ differential privacy technique to preserve the privacy of the data and exploit data-driven approach in big data characterization simultaneously.

In this talk, we will present two works on big data differential privacy preservation for cyber system, i) the privacy preservation of users’ preference content and revenue maximization in information-centric network; and ii) the privacy preservation of consumers’ demand and cost minimization in smart grid.