Abstract
We would like to quantify the assurance contained in an authentication secret. For instance, how much assurance does a customer convey to a bank by revealing that his Personal Identification Number (PIN) is 1111? We review a number of previously proposed measures, such as Shannon Entropy and min-entropy. Although each is appropriate under some assumptions, none is robust regarding the attacker’s knowledge about a nonuniform distribution. We therefore offer new measures that are more robust and useful. Uniform distributions are easy to analyze, but are rare in human memory; we therefore investigate ways to ”groom” nonuniform distributions to be uniform. We describe experiments that apply the techniques to highly nonuniform distributions, such as English names.
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Bentley, J., Mallows, C. (2005). How Much Assurance Does a PIN Provide?. In: Baird, H.S., Lopresti, D.P. (eds) Human Interactive Proofs. HIP 2005. Lecture Notes in Computer Science, vol 3517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427896_8
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DOI: https://doi.org/10.1007/11427896_8
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