Automatically Calculating Quantitative Integrity Measures for Imperative Programs

This paper presents a framework for calculating measures of data integrity for programs in a small imperative language. We develop a Markov chain semantics for our language which calculates Clarkson and Schneider’s definitions of data contamination and suppression. These definitions are based on conditional mutual information and entropy; we present a result relating them to mutual information, which can be calculated by a number of existing tools. We extend a quantitative information flow tool (CH-IMP) to calculate these measures of integrity and demonstrate this tool with examples based on error correcting codes, the Dining Cryptographers protocol and the attempts by a number of banks to influence the Libor rate.