Abstract Hidden Markov Models: A Monadic Account of Quantitative Information Flow

@article{McIver2015AbstractHM,
  title={Abstract Hidden Markov Models: A Monadic Account of Quantitative Information Flow},
  author={Annabelle McIver and Carroll Morgan and Tahiry M. Rabehaja},
  journal={2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science},
  year={2015},
  pages={597-608}
}
Hidden Markov Models, HMM's, are mathematical models of Markov processes whose state is hidden but from which information can leak via channels. They are typically represented as 3-way joint probability distributions. We use HMM's as denotations of probabilistic hidden-state sequential programs, after recasting them as “abstract” HMM's, i.e. computations in the Giry monad D, and equipping them with a partial order of increasing security. However to encode the monadic type with hiding over state… CONTINUE READING
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