A Unified Code

  • Xian Liu
  • Patrick Farrell
  • Colin Boyd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1746)


We have proposed a novel scheme based on arithmetic coding, an optimal data compression algorithm in the sense of shortest length coding. Our scheme can provide encryption, data compression, and error detection, all together in a one-pass operation. The key size used is 248 bits. The scheme can resist existing attacks on arithmetic coding encryption algorithms. A general approach to attacking this scheme on data secrecy is difficult. The statistical properties of the scheme are very good and the scheme is easily manageable in software. The compression ratio for this scheme is only 2 % worse than the original arithmetic coding algorithm. As to error detection capabilities, the scheme can detect almost all patterns of errors inserted from the channel, regardless of the error probabilities, and at the same time it can provide both encryption and data compression.


Compression Ratio Data Compression Forward Error Correct Initial Interval Arithmetic Code 
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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  1. 1.
    Bell T., Cleary J., and Witten I.: Text compression, Prentice Hall, 1990.Google Scholar
  2. 2.
    Bergen H. and Hogan J.: “A chosen plaintext attack on an adaptive arithmetic coding compression algorithm”, Computers and Security, Vol.12, 1993, pp. 157–167.CrossRefGoogle Scholar
  3. 3.
    Boyd C., Clearr J., Irvine S., Rinsma-Melchert I., and Witten I.: “Integrating error detection into arithmetic coding”, IEEE Trans. COM, Vol.45,No.1, 1997, pp.1–3.CrossRefGoogle Scholar
  4. 4.
    Cleary J., Irvine S., and Rinsma-Melchert I.: “On the insecurity of arithmetic coding”, Computers and Security, Vol.14, 1995, pp.167–180.CrossRefGoogle Scholar
  5. 5.
    Crypt-X, Statistical Package Manual, Measuring the Strength of Stream and Block Ciphers. Information Security Research Centre, Queensland University of Technology, 1990.Google Scholar
  6. 6.
    Irvine S. and Cleary J.: “The subset sum problem and arithmetic coding”, private communication, 1995.Google Scholar
  7. 7.
    Klove T. and Korzhik V.: Error Detecting Codes, General theory and their application in feedback communication systems, Kluwer Academic Publishers, 1995.Google Scholar
  8. 8.
    Liu X., Farrell P., and Boyd C.: “Resisting the Bergen-Hogan attack on adaptive arithmetic coding”, LNCS-1355, Cryptography and Coding, Springer, December, 1997, pp.199–208.CrossRefGoogle Scholar
  9. 9.
    Liu X., Farrell P., and Boyd C.: “Arithmetic coding and data integrity”, Proceedings of WCC’99, pp.291–299, Paris, 11th-14th January, 1999.Google Scholar
  10. 10.
    Liu X. and Farrell P.: “Arithmetic coding with error correction”, Proceedings of PREP’99, pp.330–333, Manchester, 5th-7th January, 1999.Google Scholar
  11. 11.
    Witten I. and Cleary J.: “On the privacy afforded by adaptive text compression”, Computers and Security, Vol.7, 1988, pp.397–408.CrossRefGoogle Scholar
  12. 12.
    Witten I., Neal R. and Cleary J.: “Arithmetic coding for data compression”, Communications of the ACM, Vol.30,No.6, 1987, pp.520–540.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Xian Liu
    • 1
  • Patrick Farrell
    • 2
  • Colin Boyd
    • 3
  1. 1.Communications Research Group, School of EngineeringUniversity of ManchesterManchesterUK
  2. 2.Communications Research CentreLancaster UniversityLancasterUK
  3. 3.School of Data CommunicationsQueensland University of TechnologyBrisbaneAustralia

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