Simple and Tight Bounds for Information Reconciliation and Privacy Amplification

@inproceedings{Renner2005SimpleAT,
  title={Simple and Tight Bounds for Information Reconciliation and Privacy Amplification},
  author={Renato Renner and Stefan Wolf},
  booktitle={ASIACRYPT},
  year={2005}
}
Shannon entropy is a useful and important measure in information processing, for instance, data compression or randomness extraction, under the assumption—which can typically safely be made in communication theory—that a certain random experiment is independently repeated many times. In cryptography , however, where a system’s working has to be proven with respect to a malicious adversary, this assumption usually translates to a restriction on the latter’s knowledge or behavior and is generally… CONTINUE READING
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