Privatized distributed anomaly detection for large-scale nonlinear uncertain systems

Vahab Rostampour, Riccardo M.G. Ferrari, Andre M.H. Teixeira, Tamas Keviczky

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper two limitations in current distributed model based approaches for anomaly detection in large-scale uncertain nonlinear systems are addressed. The first limitation regards the high conservativeness of deterministic detection thresholds, against which a novel family of set-based thresholds is proposed. Such set-based thresholds are defined in a way to guarantee robustness in a user-defined probabilistic sense, rather than a deterministic sense. They are obtained by solving a chance-constrained optimization problem, thanks to a randomization technique based on the Scenario Approach. The second limitation regards the requirement, in distributed anomaly detection architectures, for different parties to regularly communicate local measurements. In settings where these parties want to preserve their privacy, communication may be undesirable. In order to preserve privacy and still allow for distributed detection to be implemented, a novel privacy-preserving mechanism is proposed and a so-called privatized communication protocol is introduced. Theoretical guarantees on the achievable level of privacy, along with a characterization of the robustness properties of the proposed

Original languageEnglish
Number of pages15
JournalIEEE Transactions on Automatic Control
DOIs
Publication statusE-pub ahead of print - 2020

Keywords

  • Anomaly detection
  • Integrated circuit interconnections
  • Measurement uncertainty
  • Monitoring
  • Privacy
  • Probabilistic logic
  • Uncertainty

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