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时间：2016年10月28日8:30

地点：21#426

主题：Privacy-preserving Average Consensus: Theory and Algorithm

报告人：何建平

主办单位：科学技术研究院

承办单位：计算机科学与技术学院

报告人简介：

Jianping He (M’15) received the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2013. He is currently an Associate Research Fellow with the Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada. His current research interests include the control and optimization of cyber-physical systems, the scheduling and optimization in VANETs and social networks, and the investment decision in financial market and electricity market. Dr. He serves as an Associate Editor for the KSII Transactions on Internet and Information Systems. He is also a Guest Editor of the International Journal of Robust and Nonlinear Control, Neurocomputing, and the International Journal of Distributed Senor Networks. He is the winner of Outstanding Thesis Award, Chinese Association of Automation, 2015

报告摘要：

The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of the privacy protection. In this talk, we analyze the privacy of the PPAC algorithm in the sense of the maximum disclosure probability that the other nodes can infer one node's initial state within a given small interval. We first introduce a privacy definition, named (ϵ,σ)-data-privacy, to depict the maximum disclosure probability. We prove that PPAC provides (ϵ,σ)-data-privacy, and obtain the closed-form expression of the relationship betweenϵand σ. We also prove that the added noise with uniform distribution is optimal in terms of achieving the highest (ϵ,σ)-data-privacy. Then, we prove that the disclosure probability will converge to one when all information used in the consensus process is available, i.e., the privacy is compromised. Finally, we propose an optimal privacy-preserving average consensus (OPAC) algorithm to achieve the highest (ϵ,σ)-data-privacy. Simulations are conducted to verify the results.