Maximizing entropy over Markov processes

Fabrizio Biondi, Axel Legay, Bo Friis Nielsen, Andrzej Wasowski

Research output: Contribution to journalConference articleResearchpeer-review


The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity computation reduces to finding a model of a specification with highest entropy.
Entropy maximization for probabilistic process specifications has not been studied before, even though it is well known in Bayesian inference for discrete distributions. We give a characterization of global entropy of a process as a reward function, a polynomial algorithm to verify the existence of a system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy.
We show how to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code.
© 2014 Elsevier Inc. All rights reserved.
Original languageEnglish
JournalJournal of Logical and Algebraic Methods in Programming
Issue number5-6
Pages (from-to)384-399
Publication statusPublished - 2014
Event24th Nordic Workshop on Programming Theory (NWPT 2012) - Bergen, Norway
Duration: 31 Oct 20122 Nov 2012


Workshop24th Nordic Workshop on Programming Theory (NWPT 2012)
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