Differentially-private distributed fault diagnosis for large-scale nonlinear uncertain systems 

Journal article (2018)

Authors

Vahab Rostampour Team Tamas Keviczky - Mechanical, Maritime and Materials Engineering
Riccardo M.G. Ferrari Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
André M.H. Teixeira Uppsala University
T. Keviczky Team Tamas Keviczky - Mechanical, Maritime and Materials Engineering
Research Group
Team Tamas Keviczky (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 Vahab Rostampour, Riccardo M.G. Ferrari, André M.H. Teixeira, T. Keviczky
DOI
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https://doi.org/10.1016/j.ifacol.2018.09.703
Differential Privacy Privacy Preserving Distributed Fault Diagnosis Uncertain Network of Nonlinear Systems
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Tamas Keviczky

Abstract

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.