We present the Time-Bounded Task-PIOA modeling framework, an extension of the Probabilistic I/O Automata (PIOA) framework that is intended to support modeling and verification of security protocols. Time-Bounded Task-PIOAs directly model probabilistic and nondeterministic behavior, partial-information adversarial scheduling, and time-bounded computation. Together, these features are adequate to support modeling of key aspects of security protocols, including secrecy requirements and limitations on the knowledge and computational power of adversarial parties. They also support security protocol verification, using methods that are compatible with informal approaches used in the computational cryptography research community. We illustrate the use of our framework by outlining a proof of functional correctness and security properties for a well-known Oblivious Transfer protocol.
This paper presents the framework of switched probabilistic input/output au tomata (or switched PIOA), augmenting the original PIOA framework with an explicit control exchange mechanism. Using this mechanism, we model a network of processes passing a single token among them, so that the location of this token determines which process is scheduled to make the next move. This token struc ture therefore implements a distributed scheduling scheme: scheduling decisions are always made by the (unique) active component.
Distributed scheduling allows us to draw a clear line between local and global non-deterministic choices.We then require that local non-deterministic choices are resolved using strictly local information. This eliminates unrealistic schedules that arise under the more common centralized scheduling scheme. As a result, we are able to prove that our trace-style semantics is compositional.
We introduce a notion of finite testing, based on statistical hypothesis tests, via a variant of the well-known trace machine. Under this scenario, two processes are deemed observationally equivalent if they cannot be distinguished by any finite test. We consider processes modeled as image finite probabilistic automata and prove that our notion of observational equivalence coincides with the trace distribution equivalence proposed by Segala. Along the way, we give an explicit characterization of the set of probabilistic generalize the Approximation Induction Principle by defining an also prove limit and convex closure properties of trace distributions in an appropriate metric space.
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