A Simplified Method for Computing the Key Equivocation for Additive-Like Instantaneous Block Encipherers

  • Z. Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4123)


We study the problem of computing the key equivocation rate for secrecy systems with additive-like instantaneous block (ALIB) encipherers. In general it is difficult to compute the exact value of the key equivocation rate for a secrecy system (\(f, {\cal C})\) with ALIB encipherer when the block length n becomes large. In this paper, we propose a simplified method for computing the key equivocation rate for two classes of secrecy systems with ALIB encipherers. 1) The function f is additive-like and the block encipherer C is the set of all n-length key words (sequences) of type P. 2) The function f is additive and the block encipherer C is a linear (n, m) code in the n-dimensional vector space GF(q) n . The method has a potential use for more classes of secrecy systems.


Transformation Group Linear Code Block Length Conditional Entropy Equivalent Class 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahlswede, R., Dueck, G.: Bad codes are good ciphers. Prob. Cont. Info. Theory 11, 337–351 (1982)MATHMathSciNetGoogle Scholar
  2. 2.
    Blom, R.J.: Bounds on key equivocation for simple substitution ciphers. IEEE Trans. Inform. 25, 8–18 (1979)CrossRefMathSciNetMATHGoogle Scholar
  3. 3.
    Blom, R.J.: An upper bound on the key equivocation for pure ciphers. IEEE Trans. Inform. 30, 82–84 (1984)MATHCrossRefGoogle Scholar
  4. 4.
    Csiszár, I., Körner, J.: Information Theory: Coding Theorem for Discrete Memoryless Systems. Academic, New York (1981)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Z. Zhang

There are no affiliations available

Personalised recommendations