🤖 AI Summary
Weak bisimulation fails to detect backtracking-induced covert channels in reversible computing, leading to information leakage. Method: This paper proposes a non-interference analysis framework based on branching bisimulation, the first systematic application of branching bisimulation to information-security verification in reversible systems. It establishes a classification scheme for non-interference properties tailored to bidirectional operations and rigorously proves the incompleteness of weak bisimulation in reversible settings. Contribution/Results: The work identifies branching bisimulation as a more precise semantic foundation for reversible non-interference, demonstrates its theoretical advantages in compositionality and preservation, and provides a systematic comparison with the classical Focardi–Gorrieri model. The resulting framework constitutes the first analytically rigorous and practically decidable tool for information-flow security in reversible systems.
📝 Abstract
The theory of noninterference supports the analysis of information leakage and the execution of secure computations in multi-level security systems. Classical equivalence-based approaches to noninterference mainly rely on weak bisimulation semantics. We show that this approach is not sufficient to identify potential covert channels in the presence of reversible computations. As illustrated via a database management system example, the activation of backward computations may trigger information flows that are not observable when proceeding in the standard forward direction. To capture the effects of back-and-forth computations, it is necessary to switch to a more expressive semantics, which has been proven to be branching bisimilarity in a previous work by De Nicola, Montanari, and Vaandrager. In this paper we investigate a taxonomy of noninterference properties based on branching bisimilarity along with their preservation and compositionality features, then we compare it with the taxonomy of Focardi and Gorrieri based on weak bisimilarity.