Parameter and Insertion Function Co-synthesis for Opacity Enhancement in Parametric Stochastic Discrete Event Systems

TL;DR

This paper presents a method to enhance system opacity and enforce task specifications in parametric stochastic discrete event systems by co-synthesizing insertion functions through nonlinear programming.

Contribution

It introduces a novel co-synthesis approach combining parametric Markov decision processes and nonlinear programming for opacity enhancement.

Findings

Valid solutions guarantee both security and task enforcement.

The method effectively encodes insertion strategies in a parametric MDP.

The approach ensures simultaneous opacity enhancement and task compliance.

Abstract

Opacity is a property that characterizes the system's capability to keep its "secret" from being inferred by an intruder that partially observes the system's behavior. In this paper, we are concerned with enhancing the opacity using insertion functions, while at the same time, enforcing the task specification in a parametric stochastic discrete event system. We first obtain the parametric Markov decision process that encodes all the possible insertions. Based on which, we convert this parameter and insertion function co-synthesis problem into a nonlinear program. We prove that if the output of this program satisfies all the constraints, it will be a valid solution to our problem. Therefore, the security and the capability of enforcing the task specification can be simultaneously guaranteed.