Repeated games for privacy-aware distributed state estimation in interconnected networks

The conflict between cooperation in distributed state estimation and the resulting leakage of private state information (competitive privacy) is studied for a system composed of two interconnected agents. The distributed state estimation problem is studied using an information theoretic rate-distortion-leakage tradeoff model and a repeated non-cooperative game framework. The objective is to investigate the conditions under which the repetition of the agents´ interaction enables data sharing among the agents beyond the minimum requirement. In the finite horizon case, similarly to the one-shot interaction, data sharing beyond the minimum requirement is not a credible commitment for either of the agents. However, non-trivial mutual data sharing is sustainable in the long term, i.e., in the infinite horizon case.