Conditional Computational Entropy, or Toward Separating Pseudoentropy from Compressibility

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Chun-Yuan Hsiao, Chi-Jen Lu, Leonid Reyzin

Published: 2007, Last Modified: 14 May 2025EUROCRYPT 2007EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: We study conditional computational entropy: the amount of randomness a distribution appears to have to a computationally bounded observer who is given some correlated information. By considering conditional versions of HILL entropy (based on indistinguishability from truly random distributions) and Yao entropy (based on incompressibility), we obtain: