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Data Structures with Unpredictable Timing

  • Darrell Bethea
  • Michael K. Reiter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5789)

Abstract

A range of attacks on network components, such as algorithmic denial-of-service attacks and cryptanalysis via timing attacks, are enabled by data structures for which an adversary can predict the durations of operations that he will induce on the data structure. In this paper we introduce the problem of designing data structures that confound an adversary attempting to predict the timing of future operations he induces, even if he has adaptive and exclusive access to the data structure and the timings of past operations. We also design a data structure for implementing a set (supporting membership query, insertion, and deletion) that exhibits timing unpredictability and that retains its efficiency despite adversarial attacks. To demonstrate these advantages, we develop a framework by which an adversary tracks a probability distribution on the data structure’s state based on the timings it emitted, and infers invocations to meet his attack goals.

Keywords

Binary Search Tree Average Entropy Abstract Data Type Linked List Unpredictable Timing 
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.

References

  1. 1.
    McIlroy, M.D.: A killer adversary for quicksort. Software – Practice and Experience 29, 341–344 (1999)CrossRefGoogle Scholar
  2. 2.
    Fisk, M., Varghese, G.: Fast content-based packet handling for intrusion detection. Technical Report CS2001-0670, University of California at San Diego (May 2001)Google Scholar
  3. 3.
    Crosby, S.A., Wallach, D.S.: Denial of service via algorithmic complexity attacks. In: Proceedings of the 12th USENIX Security Symposium (August 2003)Google Scholar
  4. 4.
    Kocher, P.C.: Timing attacks on implementations of diffie-hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996)Google Scholar
  5. 5.
    Brumley, D., Boneh, D.: Remote timing attacks are practical. Computer Networks: The International Journal of Computer and Telecommunications Networking 48(5), 701–716 (2005)CrossRefGoogle Scholar
  6. 6.
    Sleator, D.D., Tarjan, R.E.: Self-adjusting binary search trees. J. ACM 32(3), 652–686 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Adelson-Velskii, G., Landis, E.M.: An algorithm for the organization of information. Proceedings of the USSR Academy of Sciences 146, 263–266 (1962) (Russian); English translation by Ricci, M.J.: Soviet Math. Doklady 3, 1259–1263 (1962)Google Scholar
  8. 8.
    Seidel, R., Informatik, F., Aragon, C.R.: Randomized search trees. Algorithmica, 540–545 (1989)Google Scholar
  9. 9.
    Carter, J.L., Wegman, M.N.: Universal classes of hash functions (extended abstract). In: STOC 1977: Proceedings of the ninth annual ACM symposium on Theory of computing, pp. 106–112. ACM, New York (1977)CrossRefGoogle Scholar
  10. 10.
    Bagchi, A., Buchsbaum, A.L., Goodrich, M.T.: Biased skip lists. Algorithmica 42, 31–48 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Cho, S., Sahni, S.: Biased leftist trees and modified skip lists. Technical Report 96-002, University of Florida (1996)Google Scholar
  12. 12.
    Ergun, F., Ahinalp, S.C.S., Sinha, R.K.: Biased skip lists for highly skewed access patterns. In: Proceedings of the 3rd Workshop on Algorithm Engineering and Experiments, pp. 216–229. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Pugh, W.: A skip list cookbook. Technical Report UMIACS-TR-89-72.1, University of Maryland (1990)Google Scholar
  14. 14.
    Aspnes, J.: Skip graphs. In: Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 384–393 (2003)Google Scholar
  15. 15.
    Messeguer, X.: Skip trees, an alternative data structure to skip lists in a concurrent approach. Informatique Théorique et Applications 31(3), 251–269 (1997)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Pugh, W.: Concurrent maintenance of skip lists. Technical Report CS-TR-2222.1, University of Maryland (1989)Google Scholar
  17. 17.
    Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University Press, Cambridge (1998)zbMATHGoogle Scholar
  18. 18.
    Pugh, W.: Skip lists: a probabilistic alternative to balanced trees. Communications of the ACM 33(6), 668–676 (1990)CrossRefGoogle Scholar
  19. 19.
    Mallows, C.L.: A note on asymptotic joint normality. Annals of Mathematical Statistics 43(2), 508–515 (1972)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Elizaveta, L., Bickel, P.: The earth mover’s distance is the Mallows distance: Some insights from statistics. In: Proceedings of the 8th International Conference on Computer Vision, pp. 251–256 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Darrell Bethea
    • 1
  • Michael K. Reiter
    • 1
  1. 1.University of North CarolinaChapel HillUSA

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