Title: New control-theoretic approaches for cyber-physical privacy
Advanced information and communications technologies (ICT) are increasingly permeating through our world. The technological advances are stimulating the rapid emergence of new-generation large-scale cyber-physical systems (CPS), including the smart grid, smart buildings, intelligent transportation systems, medical device networks and mobile robotic networks. CPS consists of a large number of geographically dispersed entities and thus distributed data sharing is necessary to achieve network-wide goals. However, distributed data sharing also raises the significant concern that the private or confidential information of legitimate entities could be leaked to unauthorized entities. Privacy has become an issue of high priority to address before certain CPS can be widely deployed. Existing techniques to protect the data privacy of ICT systems are not sufficient to ensure CPS privacy. This project will develop new control-theoretic schemes to assure the successful completion of control tasks for large-scale CPS and simultaneously preserve the privacy of legitimate entities. The outcomes of this project will provide engineering guidelines to build trustworthy CPS in adversarial operating environments. The proposed activities on education and outreach will contribute to training an energetic generation of skilled professionals and engineers with multidisciplinary background to satisfy the rapid growth of CPS.
This project will systematically study CPS privacy from a fresh control-theoretic perspective. The proposed research will develop new schemes by leveraging technical tools of two disparate domains: (1) decision and control (control theory, optimization, game theory, distributed algorithms); (2) computer science (data privacy, cryptography). The new schemes will achieve mathematically provable privacy and control-theoretic performance. The research agenda consists of three thrusts: (i) developing new homomorphic encryption schemes to solve distributed optimization problems where the computation is carried over encrypted data; (ii) designing feedback perturbations on the inputs and outputs of dynamic networks such that network privacy is protected, system utilities; e.g., controllability, are maintained, and the costs caused by the perturbations are minimized; and (iii) evaluating the developed theory using the case studies of power systems, smart buildings and machine learning. The project will involve collaborations with research laboratories in federal, military and industrial sectors. The collaborations will promote technology transfer and make an impact beyond academia. The successful completion of this research will discover the unique role dynamic systems play in CPS privacy, provide the new understandings of the interplay between control theory and data privacy, and enable CPS to operate in a trustworthy manner.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date
|3/1/19 → 2/29/24
- National Science Foundation: $500,000.00