Exponentiation cryptosystems on the IBM PC
IBM Systems Journal
CRYPTO '99 Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology
CHES '08 Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems
Comparative Evaluation of Rank Correlation Based DPA on an AES Prototype Chip
ISC '08 Proceedings of the 11th international conference on Information Security
Information Security and Cryptology --- ICISC 2008
Theoretical and Practical Aspects of Mutual Information Based Side Channel Analysis
ACNS '09 Proceedings of the 7th International Conference on Applied Cryptography and Network Security
Mutual Information Analysis: How, When and Why?
CHES '09 Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems
Mutual information analysis under the view of higher-order statistics
IWSEC'10 Proceedings of the 5th international conference on Advances in information and computer security
Generic side-channel distinguishers: improvements and limitations
CRYPTO'11 Proceedings of the 31st annual conference on Advances in cryptology
Analysis of nonparametric estimation methods for mutual information analysis
ICISC'10 Proceedings of the 13th international conference on Information security and cryptology
Selecting time samples for multivariate DPA attacks
CHES'12 Proceedings of the 14th international conference on Cryptographic Hardware and Embedded Systems
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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.