|タイトル||A Privacy-Preserving Distributed Strategy Over Multitask Networks|
|開催日時||2019年4月18日 14:30 - 15:30 + 30min.|
|場所||Yagami campus, Keio Univ.
Building 14, Discussion Room One (14-211)
|内容||We develop a privacy-preserving distributed strategy over multitask diffusion networks, where each agent is interested in not only improving its local inference performance via in-network cooperation, but also protecting its own individual task against privacy leakage. In the proposed strategy, at each time instant, each agent sends a noisy estimate, which is its local intermediate estimate corrupted by a zero-mean additive noise, to its neighboring agents. We derive a sufficient condition to determine the amount of noise to add to each agent's intermediate estimate to achieve an optimal trade-off between the steady-state network mean-square-deviation and an inference privacy constraint. We show that the proposed noise powers are bounded and convergent, which leads to mean-square convergence of the proposed privacy-preserving distributed scheme. Simulation results demonstrate that the proposed strategy is able to balance the trade-off between estimation accuracy and privacy preservation.
Speaker Bio: Dr. Chengcheng Wang received the B.Eng. degree in electrical engineering and automation, and the Ph.D degree in control science and engineering from the College of Automation, Harbin Engineering University, Harbin, China, in 2011 and 2017, respectively. She is currently a Research Fellow in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From September 2014 to September 2016, she was a visiting graduate researcher in the Adaptive Systems Laboratory at the University of California, Los Angeles, CA, USA. Her research interests include adaptive and statistical signal processing, and distributed adaptation and learning.