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Adaptive Cascade

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Applied Quantum Cryptography

Part of the book series: Lecture Notes in Physics ((LNP,volume 797))

Abstract

Quantum cryptographic key exchange is a promising technology for future secret transmission, which avoids computational infeasibility assumptions, while (almost) not presuming pre-shared secrets to be available in each peer’s machine. Nevertheless, a modest amount of pre-shared secret information is required in adjacent link devices, but this information is only needed for authentication purposes. So quantum key distribution cannot create keys from nothing, rather it is a method of key expansion. The remarkable feature of quantum cryptography is its ability to detect eavesdropping by the incident of an unnaturally high quantum bit error rate. On the other hand, it has no defense against person-in-the-middle attacks by itself, which is why authentication is of crucial importance.

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References

  1. Barron, A., Schervish, M.J., Wasserman, L.: The consistency of posterior distributions in nonparametric problems. Ann. Stat. 27(2), 536–561 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bennett, C.H., Bessette, F., Brassard, G., Salvail, L., Smolin, J.: Experimental quantum cryptography. J. Cryptol. 5, 3–28 (1992)

    Article  MATH  Google Scholar 

  3. Bollerslev, T., Engle, R.F., Nelson, D.B.: Handbook of Econometrics, Vol. IV, Chap. 49: ARCH Models, pp. 2959–3038. Elsevier Science B.V., Amsterdam (1994)

    Google Scholar 

  4. Borgelt, C., Kruse, R.: Graphical Models – Methods for Data Analysis and Mining. John Wiley & Sons, UK (2002)

    Google Scholar 

  5. Brassard, G., Salvail, L.: Secret-key reconciliation by public discussion. In: Heile-Beth, T (ed.) EUROCRYPT. Springer, New York, pp. 410–423 (1993)

    Google Scholar 

  6. Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, New York (1996)

    Google Scholar 

  7. Buntine, W.L.: Chain graphs for learning. In: Besnard, P. and Hanks, S. (eds.) Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA., pp. 46–54 (1995)

    Google Scholar 

  8. Chickering, D.M.: Learning bayesian networks is NP-complete. In: D. Fisher, H.J. Lenz (eds.) Learning from Data: Artificial Intelligence and Statistics V, Chap. 12, pp. 121–130. Springer-Verlag New York (1996)

    Google Scholar 

  9. Cooper, G.F.: The computational complexity of probabilistic inference using bayesian belief networks (research note). Artif. Intell. 42(2–3), 393–405 (1990)

    Article  MATH  Google Scholar 

  10. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)

    Google Scholar 

  11. Dagum, P., Chavez, R.M.: Approximating probabilistic inference in bayesian belief networks. IEEE Trans. Pattern Anal. Mach. Intell. 15(3), 246–255 (1993). DOI http://dx.doi.org/10.1109/34.204906

    Google Scholar 

  12. Dagum, P., Luby, M.: Approximating probabilistic inference in bayesian belief networks is NP-hard. Artif. Intell. 60(1), 141–153 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  13. Diaconis, P., Freedman, D.: On the consistency of bayes estimates. Ann. Stat. 14(1), 1–26 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  14. Dobson, A.J.: An introduction to generalized linear models, 2nd edn. Chapman & Hall, CRC (2002)

    MATH  Google Scholar 

  15. Doucet, A. (ed.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)

    Google Scholar 

  16. Faraway, J.J.: Extending the Linear Model with R. Chapman & Hall/CRC, Boca Ration (2006)

    Google Scholar 

  17. Gilbert, G., Hamrick, M.: Practical quantum cryptography: A comprehensive analysis (part one) (2000). URL http://www.citebase.org/abstract?id=oai:arXiv.org:quant-ph/00%09027

  18. Gouriéroux, C.: ARCH Models and Financial Applications. Springer, New York (1997)

    Google Scholar 

  19. Grandell, J.: Doubly Stochastic Poisson Processes. Springer, New York (1976)

    Google Scholar 

  20. Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., Myers Kadie, C.: Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res. 1, 49–75 (2000)

    Article  Google Scholar 

  21. Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: The combination of knowledge and statistical data. In: KDD Workshop, pp. 85–96 (1994)

    Google Scholar 

  22. Illian, J., Penttinen, A., Stoyan, H., Stoyan, D.: Statistical Analysis and Modeling of Spatial Point Patterns. Wiley, Chichestor (2008)

    Google Scholar 

  23. Kariya, T., Kurata, H.: Generalized Least Squares. Wiley, Chichestor (2004)

    Book  Google Scholar 

  24. Kingman, J.: Poisson Processes. Oxford Science Publications, Oxford, UK (1993)

    Google Scholar 

  25. Larrañaga, P., Poza, M., Murga, R., Kuijpers, C.: Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE J. Pattern An. Mach. Intell. 18(9), 912–926 (1996)

    Article  Google Scholar 

  26. Lauritzen, S.L.: Graphical Models. Oxford Statistical Science Series 17. Oxford Science Publications, New York (1996)

    Google Scholar 

  27. Lessiak, K., Kollmitzer, C., Schauer, S., Pilz, J., Rass, S.: Statistical analysis of QKD networks in real-life environments. In: Proceedings of the Third International Conference on Quantum, Nano and Micro Technologies (2009). (to appear)

    Google Scholar 

  28. Lindsey, J.K.: Applying Generalized Linear Models. Springer, New York (1997)

    Google Scholar 

  29. Mason, R.L.: Statistical Design and Analysis of Experiments with Applications to Engineering and Science. Series in Probability and Statistics. Wiley, New York (2003)

    Google Scholar 

  30. McCullagh, P., Nelder, J.: Generalized Linear Models, 2nd edn. Monographs on Statistics and Applied Probability 37. Chapman & Hall, London (1989)

    MATH  Google Scholar 

  31. Pankratz, A.: Forecasting with Univariate Box-Jenkins Models. Wiley, New York (1983)

    Book  Google Scholar 

  32. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA (1988)

    Google Scholar 

  33. Pitman, J.: Probability. Springer, New York (2000)

    Google Scholar 

  34. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge University Press, New York (1992)

    MATH  Google Scholar 

  35. Rasch, D., Herrendörfer, G., Bock, J., Busch, K.: Verfahrensbibliothek, Versuchsplanung und -auswertung. Deutscher Landwirtschaftsverlag, Berlin (1978)

    Google Scholar 

  36. Ripley, B.D.: Stochastic Simulation. Wiley, New York (1987)

    Book  Google Scholar 

  37. Robert, C.P.: The Bayesian Choice. Springer-Verlag, New York (2001)

    Google Scholar 

  38. Ross, S.M.: Stochastic Processes. Series in Probability and Mathematical Statistics. Wiley, New York (1983)

    Google Scholar 

  39. Sherman, J., Morrison, W.J.: Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Ann. Math. Statistics 21, 124–127 (1950)

    Article  MATH  MathSciNet  Google Scholar 

  40. Å tulajter, F.: Predictions in Time Series Using Regression Models. Springer, New York (2002)

    Google Scholar 

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Correspondence to S. Rass or C. Kollmitzer .

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Rass, S., Kollmitzer, C. (2010). Adaptive Cascade. In: Kollmitzer, C., Pivk, M. (eds) Applied Quantum Cryptography. Lecture Notes in Physics, vol 797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04831-9_4

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  • DOI: https://doi.org/10.1007/978-3-642-04831-9_4

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