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.
|Publication status||Published - 2018|
|Event||10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - Warsaw, Poland|
Duration: 29 Aug 2018 → 31 Aug 2018
- Differential Privacy
- Distributed Fault Diagnosis
- Privacy Preserving
- Uncertain Network of Nonlinear Systems