University of Illinois Urbana-Champaign

Statistical verification and differential privacy in cyber-physical systems

Wang, Yu

Content Files
WANG-DISSERTATION-2018.pdf

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https://hdl.handle.net/2142/102388

Description

Title
Statistical verification and differential privacy in cyber-physical systems
Author(s)
Wang, Yu
Issue Date
2018-08-21
Director of Research (if dissertation) or Advisor (if thesis)
Dullerud, Geir E.
Doctoral Committee Chair(s)
Dullerud, Geir E.
Committee Member(s)
  • Mitra, Sayan
  • West, Matthew
  • Egerstedt, Magnus
  • Viswanathan, Mahesh
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2019-02-06T19:32:38Z
Keyword(s)
  • Model Reduction
  • Markov Chains
  • Temporal Logic
  • Multi-Agent Systems
  • Laplace Mechanisms
Abstract
This thesis studies the statistical verification and differential privacy in Cyber-Physical Systems. The first part focuses on the statistical verification of stochastic hybrid system, a class of formal models for Cyber-Physical Systems. Model reduction techniques are performed on both Discrete-Time and Continuous-Time Stochastic Hybrid Systems to reduce them to Discrete-Time Markov Chains and Continuous-Time Markov Chains, respectively; and statistical verification algorithms are proposed to verify Linear Inequality LTL and Metric Interval Temporal Logic on these discrete probabilistic models. In addition, the advantage of stratified sampling in verifying Probabilistic Computation Tree Logic on Labeled Discrete-Time Markov Chains is studied; this method can potentially be extended to other statistical verification algorithms to reduce computational costs. The second part focuses on the Differential Privacy in multi-agent systems that involve share information sharing to achieve overall control goals. A general formulation of the systems and a notion of Differential Privacy are proposed, and a trade-off between the Differential Privacy and the tracking performance of the systems is demonstrated. In addition, it is proved that there is a trade-off between Differential Privacy and the entropy of the unbiased estimator of the private data, and an optimal algorithm to achieve the best trade-off is given.
Graduation Semester
2018-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/102388
Copyright and License Information
Copyright 2018 Yu Wang

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