Security Analysis Of Epsilon-almost Dual Universal2 Hash Functions Smoothing Of Min Entropy Vs. Smoothing Of Renyi Entropy Of Order 2 | Awesome Learning to Hash Add your paper to Learning2Hash

Awesome Learning to Hash

A Webpage dedicated to the latest research on Hash Function Learning. Maintained by Sean Moran.

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Security Analysis Of Epsilon-almost Dual Universal2 Hash Functions Smoothing Of Min Entropy Vs. Smoothing Of Renyi Entropy Of Order 2

Hayashi Masahito. IEEE Transactions on Information Theory Volume 2013

[Paper]    
Independent

Recently, ϵ-almost dual universal2 hash functions has been proposed as a new and wider class of hash functions. Using this class of hash functions, several efficient hash functions were proposed. This paper evaluates the security performance when we apply this kind of hash functions. We evaluate the security in several kinds of setting based on the L1 distinguishability criterion and the modified mutual information criterion. The obtained evaluation is based on smoothing of R'{e}nyi entropy of order 2 and/or min entropy. We clarify the difference between these two methods.

Similar Work