Advertisement

Towards Joint Tardos Decoding: The ‘Don Quixote’ Algorithm

  • Peter Meerwald
  • Teddy Furon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

‘Don Quixote’ is a new accusation process for Tardos traitor tracing codes which is, as far as we know, the first practical implementation of joint decoding. The first key idea is to iteratively prune the list of potential colluders to keep the computational effort tractable while going from single, to pair,…to t-subset joint decoding. At the same time, we include users accused in previous iterations as side-information to build a more discriminative test. The second idea, coming from the field of mismatched decoders and compound channels, is to use a linear decoder based on the worst case perceived collusion channel. The decoder is tested under two accusation policies: to catch one colluder, or to catch as many colluders as possible. The probability of false positive is controlled thanks to a rare event estimator. We describe a fast implementation supporting millions of users and compare our results with two recent fingerprinting codes.

Keywords

traitor tracing fingerprinting transactional watermarking joint decoder 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbe, E., Zheng, L.: Linear universal decoding for compound channels. IEEE Transactions on Information Theory 56(12), 5999–6013 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Amiri, E., Tardos, G.: High rate fingerprinting codes and the fingerprinting capacity. In: Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2009, pp. 336–345. SIAM, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Cérou, F., Furon, T., Guyader, A.: Experimental assessment of the reliability for watermarking and fingerprinting schemes. EURASIP Jounal on Information Security (2008), iD 414962, 12 pagesGoogle Scholar
  4. 4.
    Furon, T., Pérez-Freire, L.: Worst case attacks against binary probabilistic traitor tracing codes. In: Proc. First IEEE Int. Workshop on Information Forensics and Security, London, UK, pp. 46–50 (December 2009)Google Scholar
  5. 5.
    Knuth, D.E.: The Art of Computer Programming, Generating All Combinations and Partitions, vol. 4. Addison-Wesley, Reading (2005); Fascicle 3Google Scholar
  6. 6.
    Moulin, P.: Universal fingerprinting: Capacity and random-coding exponents. In: Proc. IEEE International Symposium on Information Theory, ISIT 2008, Toronto, ON, Canada, pp. 220–224 (July 2008)Google Scholar
  7. 7.
    Nuida, K.: Short collusion-secure fingerprint codes against three pirates. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 86–102. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Nuida, K., Fujitsu, S., Hagiwara, M., Kitagawa, T., Watanabe, H., Ogawa, K., Imai, H.: An improvement of discrete Tardos fingerprinting codes. Designs, Codes and Cryptography 52(3), 339–362 (2009), http://eprint.iacr.org/2008/338 MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Payne, W.H., Ives, F.M.: Combination generators. ACM Transactions on Mathematical Software 5(2), 163–172 (1979)CrossRefGoogle Scholar
  10. 10.
    Pérez-Freire, L., Furon, T.: Blind decoder for binary probabilistic traitor tracing codes. In: Proc. First IEEE Int. Workshop on Information Forensics and Security, London, UK, pp. 56–60 (December 2009)Google Scholar
  11. 11.
    Saito, M., Matsumoto, M.: A PRNG specialized in double precision floating point numbers using an affine transition. In: Proc. Eighth Int. Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2008, pp. 589–602. Springer, Montréal (2008)Google Scholar
  12. 12.
    Skoric, B., Katzenbeisser, S., Celik, M.: Symmetric Tardos fingerprinting codes for arbitrary alphabet sizes. Designs, Codes and Cryptography 46(2), 137–166 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Tardos, G.: Optimal probabilistic fingerprint codes. In: Proc. 35th ACM Symposium on Theory of Computing, San Diego, CA, USA, pp. 116–125 (2003), http://www.renyi.hu/~tardos/publications.html
  14. 14.
    Wu, M., Trappe, W., Wang, Z.J., Liu, K.J.R.: Collusion-resistant fingerprinting for Multimedia. IEEE Signal Processing Magazine 21(2), 15–27 (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Meerwald
    • 1
  • Teddy Furon
    • 1
  1. 1.INRIA Rennes Bretagne AtlantiqueRennesFrance

Personalised recommendations