A Quantitative Approach to Measuring Assurance with Uncertainty in Data Provenance

Abstract

A data provenance framework is subject to security threats and risks, which increase the uncertainty, or lack of trust, in provenance information. Information assurance is challenged by incomplete information; one cannot exhaustively characterize all threats or all vulnerabilities. One technique that specifically incorporates a probabilistic notion of uncertainty is subjective logic. Subjective logic allows belief and uncertainty, due to incomplete information, to be specified and operated upon in a coherent manner. A mapping from the standard definition of information assurance to a more quantitative subjective logic framework is suggested with a focus on the specific application of data provenance. Finally, specific consideration is given to the notion of uncertainty within subjective logic and its relation to information entropy. Information entropy is an alternative measure of uncertainty and a fundamental relationship is hypothesized between uncertainty in subjective logic and entropy

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