A Precise Information Flow Measure from Imprecise Probabilities
Abstract
Dempster-Shafer theory of imprecise probabilities has proved useful to incorporate both nonspecificity and conflict uncertainties in an inference mechanism. The traditional Bayesian approach cannot differentiate between the two, and is unable to handle non-specific, ambiguous, and conflicting information without making strong assumptions. This paper presents a generalization of a recent Bayesian-based method of quantifying information flow in Dempster-Shafer theory. The generalization concretely enhances the original method removing all its weaknesses that are highlighted in this paper. In so many words, our generalized method can handle any number of secret inputs to a program, it enables the capturing of an attacker's beliefs in all kinds of sets (singleton or not), and it supports a new and precise quantitative information flow measure whose reported flow results are plausible in that they are bounded by the size of a program's secret input, and can be easily associated with the exhaustive search effort needed to uncover a program's secret information, unlike the results reported by the original metric.
Cite
@article{arxiv.1206.5487,
title = {A Precise Information Flow Measure from Imprecise Probabilities},
author = {Sari Haj Hussein},
journal= {arXiv preprint arXiv:1206.5487},
year = {2015}
}
Comments
10 pages. Appeared in the 6th International Conference on Software Security and Reliability (SERE 2012), Washington D.C., The United States, Proceedings of the 6th International Conference on Software Security and Reliability (SERE 2012), Washington D.C., The United States