Abstract
The Correlation Power Analysis (CPA) is probably the most used side-channel attack because it seems to fit the power model of most standard CMOS devices and is very efficiently computed. However, the Pearson correlation coefficient used in the CPA measures only linear statistical dependences where the Mutual Information (MI) takes into account both linear and nonlinear dependences. Even if there can be simultaneously large correlation coefficients quantified by the correlation coefficient and weak dependences quantified by the MI, we can expect to get a more profound understanding about interactions from an MI Analysis (MIA). We study methods that improve the non-parametric Probability Density Functions (PDF) in the estimation of the entropies and, in particular, the use of B-spline basis functions as pdf estimators. Our results indicate an improvement of two fold in the number of required samples compared to a classic MI estimation. The B-spline smoothing technique can also be applied to the rencently introduced Cramér-von-Mises test.
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Keywords
- Mutual Information
- Probability Density Function
- Entropy Estimation
- Correlation Power Analysis
- Mutual Information Analysis
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.
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Venelli, A. (2010). Efficient Entropy Estimation for Mutual Information Analysis Using B-Splines. In: Samarati, P., Tunstall, M., Posegga, J., Markantonakis, K., Sauveron, D. (eds) Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices. WISTP 2010. Lecture Notes in Computer Science, vol 6033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12368-9_2
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DOI: https://doi.org/10.1007/978-3-642-12368-9_2
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