Statistical Inference, Zero Knowledge and Proofs of Identity

  • Jacques Stern
Chapter
Part of the NATO ASI Series book series (ASIC, volume 428)

Abstract

The aim of the present article is to show how computational complexity can interfere with statistical inference in such a way that pieces of information that are completely equivalent from the point of view of information theory become very different computationally. In order to clarify this paradox, we will first describe concepts and techniques that have appeared these last years: asymptotic evaluation of computation time, probabilistic algorithms, one-way functions, zero knowledge proofs etc. Doing so, we will understand how these concepts allow creation and protection of secrecy by tools which are derived from precise mathematical problems that are publicly discussed. This is precisely the viewpoint of modern cryptography.

Keywords

Probabilistic Algorithm Random String Decimal Digit Abstract Machine Polynomial Time Probabilistic Algorithm 
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 Science+Business Media Dordrecht 1994

Authors and Affiliations

  • Jacques Stern
    • 1
  1. 1.Laboratoire d’InformatiqueÉcole Normale SupérieureParis Cedex 05France

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