Entropy and Attack Models in Information Flow

(Invited Talk)
  • Mário S. Alvim
  • Miguel E. Andrés
  • Catuscia Palamidessi
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 323)


In recent years, there has been a growing interest in considering the quantitative aspects of Information Flow, partly because often the a priori knowledge of the secret information can be represented by a probability distribution, and partly because the mechanisms to protect the information may use randomization to obfuscate the relation between the secrets and the observables.


  1. 1.
    Cachin, C.: Entropy Measures and Unconditional Security in Cryptography. PhD thesis (1997)Google Scholar
  2. 2.
    Chatzikokolakis, K., Palamidessi, C., Panangaden, P.: Anonymity protocols as noisy channels. Inf. and Comp. 206(2-4), 378–401 (2008)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Chatzikokolakis, K., Palamidessi, C., Panangaden, P.: On the Bayes risk in information-hiding protocols. Journal of Computer Security 16(5), 531–571 (2008)Google Scholar
  4. 4.
    Clark, D., Hunt, S., Malacaria, P.: Quantitative information flow, relations and polymorphic types. J. of Logic and Comp. 18(2), 181–199 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Clarkson, M.R., Myers, A.C., Schneider, F.B.: Belief in information flow. Journal of Computer Security 17(5), 655–701 (2009)Google Scholar
  6. 6.
    Csiszár, I.: Generalized cutoff rates and Rényi’s information measures. Transactions on Information Theory 41(1), 26–34 (1995)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Hamadou, S., Sassone, V., Palamidessi, C.: Reconciling belief and vulnerability in information flow. In: Proc. of the IEEE Symposium on Security and Privacy. IEEE, Los Alamitos (to appear, 2010)Google Scholar
  8. 8.
    Köpf, B., Basin, D.A.: An information-theoretic model for adaptive side-channel attacks. In: Proc. of CCS, pp. 286–296. ACM, New York (2007)Google Scholar
  9. 9.
    Malacaria, P.: Assessing security threats of looping constructs. In: Proc. of POPL, pp. 225–235. ACM, New York (2007)Google Scholar
  10. 10.
    Malacaria, P., Chen, H.: Lagrange multipliers and maximum information leakage in different observational models. In: Proc. of PLAS, pp. 135–146. ACM, New York (2008)Google Scholar
  11. 11.
    Massey: Guessing and entropy. In: Proceedings of the IEEE International Symposium on Information Theory, p. 204. IEEE, Los Alamitos (1994)Google Scholar
  12. 12.
    Moskowitz, I.S., Newman, R.E., Crepeau, D.P., Miller, A.R.: Covert channels and anonymizing networks. In: Proc. of PES, pp. 79–88. ACM, New York (2003)Google Scholar
  13. 13.
    Moskowitz, I.S., Newman, R.E., Syverson, P.F.: Quasi-anonymous channels. In: Proc. of CNIS, pp. 126–131. IASTED (2003)Google Scholar
  14. 14.
    Pliam, J.O.: On the incomparability of entropy and marginal guesswork in brute-force attacks. In: Roy, B., Okamoto, E. (eds.) INDOCRYPT 2000. LNCS, vol. 1977, pp. 67–79. Springer, Heidelberg (2000)Google Scholar
  15. 15.
    Rényi, A.: On Measures of Entropy and Information. In: Proc. of the 4th Berkeley Symposium on Mathematics, Statistics, and Probability, pp. 547–561 (1961)Google Scholar
  16. 16.
    Smith, G.: On the foundations of quantitative information flow. In: de Alfaro, L. (ed.) FOSSACS 2009. LNCS, vol. 5504, pp. 288–302. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Zhu, Y., Bettati, R.: Anonymity vs. information leakage in anonymity systems. In: Proc. of ICDCS, pp. 514–524. IEEE, Los Alamitos (2005)Google Scholar

Copyright information

© IFIP 2010

Authors and Affiliations

  • Mário S. Alvim
    • 1
  • Miguel E. Andrés
    • 2
  • Catuscia Palamidessi
    • 1
  1. 1.INRIA and LIXÉcole PolytechniquePalaiseauFrance
  2. 2.University of NijmegenThe Netherlands

Personalised recommendations