讲座题目：Big Data Differential Privacy Preservation for Cyber Physical Systems 主讲人：Miao Pan 主持人：陈铭松 教授 开始时间🙇🏿𓀋：2019-05-21 14:00:00 结束时间：2019-05-21 15:00:00 讲座地址：中北校区数学馆东202 主办单位：上海光辉娱乐平台精密光谱科学与技术国家重点实验室
报告人简介🥪： Dr. Miao Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at University of Houston. He was a recipient of NSF CAREER Award in 2014. Dr. Pan received Ph.D. degree in Electrical and Computer Engineering from University of Florida in August 2012. Dr. Pan's research interests include cybersecurity, big data privacy, deep learning privacy, cyber-physical systems, and cognitive radio networks. 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. 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. He has also been serving as a Technical Organizing Committee for several conferences such as TPC Co-Chair for Mobiquitous 2019, ACM WUWNet 2019, and Technical Program Committee member of several top international conferences, e.g., IEEE INFOCOM 2014-2019, ICDCS 2019, etc. 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. 报告内容： 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. |
