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Automatically Compute Information Flow Quantity via Probabilistic Semantics



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Journal of Information Systems Security
Volume 5, Number 4 (2009)
Pages 4785
ISSN 1551-0123 (Print)
ISSN 1551-0808 (Online)
Chunyan Mu — King's College London, UK
David Clark — King's College London, UK
Information Institute Publishing, Washington DC, USA




Measuring information flow in software has recently become an active research topic in the security community. Information about confidential inputs may flow to public outputs in batch programs. It would be useful to quantify such flows in the computational world. In this paper, we present an automatic analyser for measuring information flow within software systems. We quantify leakage in terms of information theory and incorporate this computation into probabilistic semantics. Our semantic functions provide information flow measurement for programs given secure inputs under any probability distribution. The major contribution is an automatically quantitative analyser based on the leakage definition for such a language. While-loops are handled by applying entropy of generalized distributions and some properties e.g. the partition property and the mean entropy property in order to provide a more precise analysis with respect to the time of observation.




Language, Security, Non-interference, Semantics, Information Theory, Flow




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