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.
Scope
SUBMIT A METRIC
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SUBMIT Anonymity Set Size 32 related use cases
The anonymity set for an individual u, denoted ASu is the set of users that the adversary cannot distinguish from u. It can be seen as the size of the crowd into which the target u can blend.
privASS ≡ |ASu |
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Amount of Leaked Information 15 related use cases
This metric counts the information items S disclosed by a system, e.g., the number of compromised users. However, this metric does not indicate the severity of a leak because it does not account for the
sensitivity of the leaked information.
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Conditional Entropy 1 related use case
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 alw...
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False Acceptance Rate (FAR)
False acceptance rate (FAR) is a security metric used to measure the performance of biometric systems such as voice recognition, fingerprint recognition, face recognition, or iris recognition. It represents the likelihood of a biometric system mistakenly ac...
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False Rejection Rate (FRR)
False rejection rate (FRR) is a security metric used to measure the performance of biometric systems such as voice recognition, fingerprint recognition, face recognition, or iris recognition. It represents the likelihood of a biometric system mistakenly rej...
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