Differential Privacy and Minimum-Variance Unbiased Estimation in Multi-agent Control Systems

被引:5
|
作者
Wang, Yu [1 ]
Mitra, Sayan [1 ]
Dullerud, Geir E. [1 ]
机构
[1] Univ Illinois, Coordinated Sci Lab, Champaign, IL 61820 USA
来源
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
关键词
epsilon-differential privacy; minimum-variance unbiased estimation; multi-agent control systems; Laplace-noise-adding mechanisms; AVERAGE CONSENSUS; NETWORKS;
D O I
10.1016/j.ifacol.2017.08.1612
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In a discrete-time linear multi-agent control system, where the agents are coupled via an environmental state, knowledge of the environmental state is desirable to control the agents locally. However, since the environmental state depends on the behavior of the agents, sharing it directly among these agents jeopardizes the privacy of the agents' profiles, defined as the combination of the agents' initial states and the sequence of local control inputs over time. A commonly used solution is to randomize the environmental state before sharing - this leads to a natural trade-off between the privacy of the agents' profiles and the variance of estimating the environmental state. By treating the multi-agent system as a probabilistic model of the environmental state parametrized by the agents' profiles, we show that when the agents' profiles is E.-differentially private, there is a lower bound on the l(1) induced norm of the covariance matrix of the minimum-variance unbiased estimator of the environmental state. This lower bound is achieved by a randomized mechanism that uses Laplace noise. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:9521 / 9526
页数:6
相关论文
共 50 条
  • [1] Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent Systems by Distributed Cooperative Filters
    Liu, Changqing
    Wang, Youqing
    Zhou, Donghua
    Shen, Xiao
    IEEE ACCESS, 2018, 6 : 18128 - 18141
  • [2] Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent System with Heterogeneous Unknown Input
    Shi, Yukun
    Liu, Changqing
    Shen, Xiao
    Wang, Youqing
    2018 IEEE 8TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (IEEE-CYBER), 2018, : 926 - 931
  • [3] Minimum-variance unbiased unknown input and state estimation for multi-agent systems with direct feedthrough by using distributed cooperative filters
    Liu, Changqing
    Wang, Youqing
    IFAC PAPERSONLINE, 2018, 51 (24): : 286 - 291
  • [4] UNBIASED MINIMUM-VARIANCE LINEAR STATE ESTIMATION
    KITANIDIS, PK
    AUTOMATICA, 1987, 23 (06) : 775 - 778
  • [5] Unbiased minimum-variance state estimation for linear systems with unknown input
    Cheng, Yue
    Ye, Hao
    Wang, Yongqiang
    Zhou, Donghual
    AUTOMATICA, 2009, 45 (02) : 485 - 491
  • [6] Unbiased minimum-variance estimation for systems with measurement-delay and unknown inputs
    Cui, Beibei
    Song, Xinmin
    Tian, Lin
    2018 CHINESE AUTOMATION CONGRESS (CAC), 2018, : 514 - 519
  • [7] Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs
    Hsieh, Chien-Shu
    AUTOMATICA, 2009, 45 (09) : 2149 - 2153
  • [8] Implementation Issues of Unbiased Minimum-Variance State Estimation for Systems with Unknown Inputs
    Hsieh, Chien-Shu
    Majidi, Mohammad Ali
    2014 CACS INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS 2014), 2014, : 323 - 328
  • [9] On the global optimality of unbiased minimum-variance state estimation for systems with unknown inputs
    Hsieh, Chien-Shu
    AUTOMATICA, 2010, 46 (04) : 708 - 715
  • [10] Optimal filtering for systems with unknown inputs via unbiased minimum-variance estimation
    Hsieh, Chien-Shu
    TENCON 2006 - 2006 IEEE REGION 10 CONFERENCE, VOLS 1-4, 2006, : 420 - 423