Ke Sun | INRIA | Research

Information and Decision Making - IDEM

• Outline: This research undertakes fundamental research in applied mathematics at the intersection of information theory and game theory. The joint use of these theories allows to quantitatively model agents that possesses different amounts of information. Specifically, classical games are studied under an imperfect information exchange scenario, in which the physical channels through which information is sent is imperfect, e.g. noisy, interference, etc. This research is supported by the Exploratory Action of INRIA. Link

• Collaborator: Dr. Samir M. Perlaza, Dr. Alain Jean-Marie

Information-Theoretic Data Injection Attacks on the Smart Grid

• Outline: Using information-theoretic measures to quantify security in cyber-physical systems, in particular data injection attacks in the smart grid. Random matrix theory tools were used to analyze the performance of the attack under imperfect system model knowledge conditions, which led to the derivation of bounds on the performance of the attacks for non-asymptotic scenarios and of closed-form expressions for the asymptotic case.

• Collaborator: Dr. Iñaki Esnaola, Dr. Samir M. Perlaza, Prof. Antonia M. Tulino, and Prof. H. Vincent Poor

• Selected Publications:

  • K. Sun, I. Esnaola, A. M. Tulino and H. V. Poor, ‘‘Asymptotic learning requirements for stealth attacks’’, IEEE Trans. Smart Grid, Nov. 2021.

  • K. Sun, I. Esnaola, S. M. Perlaza, and H. V. Poor, ’’Stealth attacks on the smart grid," IEEE Trans. Smart Grid, vol. 11 , no. 2, pp. 1276-1285, Mar. 2020.

Circular 4.0: Data Driven Intelligence for a Circular Economy

• Outline: This research aims to identify how data from products in use can inform intelligent decisions surrounding the implementation of circular economy strategies within manufacturing. Leveraging information-theoretic and data science tools, we (i) develop stochastic models for the data streams, (ii) provide quantitative measure for the amount information conveyed by data streams, and (iii) propose guidelines for optimal acquisition/sensing of the data stream based on the measure. Industrial collaborators include Rolls-Royce, Airbus, and Riversimple.

• Collaborator: Dr. Iñaki Esnaola, Prof. Ashutosh Tiwari, Prof. Fiona Charnley, and Dr. Okechukwu Okorie

• Selected Publications:

  • K. Sun, I. Esnaola, O. Okorie, F. Charnley, M. Moreno, A. Tiwari, ’’Data-driven modeling and monitoring of fuel cell performance’’, International Journal of Hydrogen Energy, vol. 46, no. 66, pp. 33206-33217, Sep. 2021.