Privacy-preserving Average Consensus: Theory and Algorithm学术报告通知



时间:2016年10月24日 下午14:30



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,



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.