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

Rostampour, Vahab; Ferrari, Riccardo M.G.; Teixeira, André M.H.; Keviczky, T.

doi:10.1016/j.ifacol.2018.09.703

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

Title

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

Author

Rostampour, Vahab (TU Delft Team Tamas Keviczky)
Ferrari, Riccardo M.G. (TU Delft Team Jan-Willem van Wingerden)
Teixeira, André M.H. (Uppsala University)
Keviczky, T. (TU Delft Team Tamas Keviczky)

Date

2018

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.

Subject

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

To reference this document use:

http://resolver.tudelft.nl/uuid:e99f03e1-1baf-428d-9b3a-efd1bf63a86f

DOI

https://doi.org/10.1016/j.ifacol.2018.09.703

ISSN

2405-8963

Source

IFAC-PapersOnLine, 51 (24), 975-982

Event

10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, 2018-08-29 → 2018-08-31, Warsaw, Poland