Maximizing Entropy over Markov Processes

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

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftTidsskriftartikelForskningpeer review

Abstract

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.
OriginalsprogEngelsk
BogserieLecture Notes in Computer Science
Vol/bind7810
Sider (fra-til)128-140
ISSN0302-9743
StatusUdgivet - 2013

Emneord

  • Channel Capacity
  • Deterministic Systems
  • Confidential Data
  • Interval Markov Chains
  • Entropy Maximization

Fingeraftryk

Dyk ned i forskningsemnerne om 'Maximizing Entropy over Markov Processes'. Sammen danner de et unikt fingeraftryk.

Citationsformater