学术报告通知
时间：2016年10月24日 下午14:30
地点：北航新主楼D639会议室
报告题目：Privacy-preserving
Average Consensus: Theory and Algorithm
主讲人：Dr.Jianping He
(Associate Research Fellow, University of
Victoria, Canada)
报告人Jianping He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.
报告内容摘要：
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.
