Privacy-preserving of system model with perturbed state trajectories using differential privacy: With application to a supply chain network

Releasing state samples generated by a dynamical system model, for data aggregation purposes, can allow an adversary to perform reverse engineering and estimate sensitive model parameters. Upon identification of the system model, the adversary may even use it for predicting sensitive data in the future. Hence, preserving a confidential dynamical...

Protecting the grid topology and user consumption patterns during state estimation in smart grids based on data obfuscation

Smart grids promise a more reliable, efficient, economically viable, and environment-friendly electricity infrastructure for the future. State estimation in smart grids plays a pivotal role in system monitoring, reliable operation, automation, and grid stabilization. However, the power consumption data collected from the users during state...

Privacy-Aware State Estimation based on Obfuscated Transformation and Differential Privacy: With applications to smart grids and supply chain economics

With the emergence of many modern automated systems around us that rely heavily on the private data collected from individuals, the problem of privacy-preserving data analysis is now gaining a significant attention in the field of systems and control. In this thesis, we investigate the privacy concerns of these systems arising in the process of...