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

Vahab Rostampour, Riccardo Ferrari, André M.H. Teixeira, Tamás Keviczky

Research output: Contribution to journalConference articleScientificpeer-review

2 Citations (Scopus)
36 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)975-982
JournalIFAC-PapersOnLine
Volume51
Issue number24
DOIs
Publication statusPublished - 2018
Event10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - Warsaw, Poland
Duration: 29 Aug 201831 Aug 2018

Keywords

  • Differential Privacy
  • Distributed Fault Diagnosis
  • Privacy Preserving
  • Uncertain Network of Nonlinear Systems

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