Success through Confidence: Evaluating the Effectiveness of a Side-Channel Attack

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8086)


Side-channel attacks usually apply a divide-and-conquer strategy, separately recovering different parts of the secret. Their efficiency in practice relies on the adversary ability to precisely assess the success or unsuccess of each of these recoveries. This makes the study of the attack success rate a central problem in side channel analysis. In this paper we tackle this issue in two different settings for the most popular attack, namely the Correlation Power Analysis (CPA). In the first setting, we assume that the targeted subkey is known and we compare the state of the art formulae expressing the success rate as a function of the leakage noise and the algebraic properties of the cryptographic primitive. We also make the link between these formulae and the recent work of Fei et al. at CHES 2012. In the second setting, the subkey is no longer assumed to be known and we introduce the notion of confidence level in an attack result, allowing for the study of different heuristics. Through experiments, we show that the rank evolution of a subkey hypothesis can be exploited to compute a better confidence than considering only the final result.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Doget, J., Prouff, E., Rivain, M., Standaert, F.-X.: Univariate Side Channel Attacks and Leakage Modeling. Journal of Cryptographic Engineering 1(2), 123–144 (2011)CrossRefGoogle Scholar
  2. 2.
    Fei, Y., Luo, Q., Ding, A.A.: A Statistical Model for DPA with Novel Algorithmic Confusion Analysis. In: Prouff, E., Schaumont, P. (eds.) CHES 2012. LNCS, vol. 7428, pp. 233–250. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Fisher, R.A.: On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society (1922)Google Scholar
  4. 4.
    Genz, A., Shing Kwong, K.: Numerical evaluation of singular multivariate normal distributions. Journal of Statistical Computation and Simulation 68, 1–21 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Harpes, C.: Cryptanalysis of iterated block ciphers. ETH Series in Information Processing, vol. 7. Hartung-Gorre Verlag (1996)Google Scholar
  6. 6.
    Mangard, S.: Hardware Countermeasures against DPA – A Statistical Analysis of Their Effectiveness. In: Okamoto, T. (ed.) CT-RSA 2004. LNCS, vol. 2964, pp. 222–235. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Nassar, M., Souissi, Y., Guilley, S., Danger, J.-L.: “Rank Correction”: A New Side-Channel Approach for Secret Key Recovery. In: Joye, M., Mukhopadhyay, D., Tunstall, M. (eds.) InfoSecHiComNet 2011. LNCS, vol. 7011, pp. 128–143. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Neyman, J., Pearson, E.S.: On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 231, 289–337 (1933)zbMATHGoogle Scholar
  9. 9.
    Rivain, M.: On the Exact Success Rate of Side Channel Analysis in the Gaussian Model. In: Avanzi, R.M., Keliher, L., Sica, F. (eds.) SAC 2008. LNCS, vol. 5381, pp. 165–183. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Standaert, F.-X., Peeters, E., Rouvroy, G., Quisquater, J.-J.: An overview of power analysis attacks against field programmable gate arrays. IEEE 94(2), 383–394 (2006)CrossRefGoogle Scholar
  11. 11.
    Whitnall, C., Oswald, E.: A Comprehensive Evaluation of Mutual Information Analysis Using a Fair Evaluation Framework. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 316–334. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© International Association for Cryptologic Research 2013

Authors and Affiliations

  1. 1.ANSSIParis 07France

Personalised recommendations