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  • 标题:Differentially-Private Distributed Fault Diagnosis for Large-Scale Nonlinear Uncertain Systems ⁎
  • 本地全文:下载
  • 作者:Vahab Rostampour ; Riccardo Ferrari ; André M.H. Teixeira
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:24
  • 页码:975-982
  • DOI:10.1016/j.ifacol.2018.09.703
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractDistributed 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.
  • 关键词:KeywordsPrivacy PreservingDifferential PrivacyDistributed Fault DiagnosisUncertain Network of Nonlinear Systems
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