Differentially-Private Distributed Fault Diagnosis for Large-Scale Nonlinear Uncertain Systems

被引：4

作者：

Rostampour, Vahab ^{[1
]}

Ferrari, Riccardo ^{[1
]}

Teixeira, Andre M. H. ^{[2
]}

Keviczky, Tamas ^{[1
]}

机构：

[1] Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 2, NL-2628 CD Delft, Netherlands

[2] Uppsala Univ, Dept Engn Sci, POB 534, SE-75121 Uppsala, Sweden

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 24期

基金：

欧盟地平线“2020”;

关键词：

Privacy Preserving; Differential Privacy; Distributed Fault Diagnosis; Uncertain Network of Nonlinear Systems; NETWORKS;

D O I：

10.1016/j.ifacol.2018.09.703

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Distributed fault diagnosis has been proposed as an effective technique for monitoring large scale, nonlinear and uncertain systems. It is based on the decomposition of the large scale system into a number of interconnected subsystems, each one monitored by a dedicated Local Fault Detector (LFD). Neighboring LFDs, in order to successfully account for subsystems interconnection, are thus required to communicate with each other some of the measurements from their subsystems. Anyway, such communication may expose private information of a given subsystem, such as its local input. To avoid this problem, we propose here to use differential privacy to pre-process data before transmission. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：975 / 982

页数：8

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