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A Better Chain Rule for HILL Pseudoentropy - Beyond Bounded Leakage

  • Maciej SkórskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10015)

Abstract

Chain rules are inequalities used to estimate by how much entropy decreases when conditioning on some extra knowledge. Their popular application is to argue about security, by proving that the entropy of a secret remains sufficiently high even in the presence of leakage. We provide a chain rule for HILL/Metric conditional pseudoentropy (applicable for leakage-resilient cryptography), with the following new features:
  1. (a)

    Better quality loss - when conditioning on already conditioned distribution, the loss due to the “internal” conditional part is additive, not multiplicative as conjectured in folklore,

     
  2. (b)

    Better quantity loss - the leakage length is replaced by the effective leakage length which equals the “pseudoentropy gap” of the leakage conditioned on the secret,

     
  3. (c)

    Flexible quality loss - the loss can be continuously traded between both computational resources: time and advantage.

     

The relevance of these results is as follows: (a) is a result complementary to recent negative results (TCC’13) on the chain rule for HILL pseudoentropy - it explains that an efficient chain rule for HILL pseudoentropy is possible under certain conditions. With (b) we can extend some leakage resilient constructions, beyond the bounded leakage model, to capture noisy leakages (studied extensively in recent EUROCRYPT papers); interestingly, we show that the new chain rule can handle specific noisy leakages better than the noisy-leakage framework. Finally using (c) we can unify all previous results and techniques about pseudoentropy chain rules.

Keywords

Chain Rule Quality Loss Stream Cipher Pseudorandom Generator Provable Security 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of WarsawWarsawPoland

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