These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Conditional Entropy
We discuss information-theoretic anonymity metrics, that use entropy over the distribution of all possible recipients to quantify anonymity. We identify a common misconception: the entropy of the distribution describing the potential receivers does not always decrease given more information. We show the relation of these a-posteriori distributions with the Shannon conditional entropy, which is an average over all possible observations.
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Technical Privacy Metrics: A Systematic Survey
Uploaded on Oct 25, 2022The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way...
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