Game current-state opacity formulation in probabilistic resource automata

Abstract

We investigate the quantitative current-state opacity problem with resource constraints in the framework of probabilistic resource automata which is partially observed. Resource information states and event payoffs capture the variations of a resource that can be consumed or replenished during a system evolution. There are a number of ways to quantify opacity in discrete event systems, but these quantification methods have certain limitations. This research mitigates this restriction by extending current-state opacity and step-based almost current-state opacity formulations to probabilistic resource automata under partial observation with payoff constraints. We define game current-state opacity with an aim of offering a quantitative measure for opacity. We also construct a game current-state analyzer for the verification of game current-state opacity and prove the decidability of verifying the game current-state opacity problem.