Be My Guess: Guessing Entropy vs. Success Rate for Evaluating Side-Channel Attacks of Secure Chips

Abstract

In a theoretical context of side-channel attacks, optimal bounds between success rate and guessing entropy are derived with a simple majorization (Schur-concavity) argument. They are further theoretically refined for different versions of the classical Hamming weight leakage model, in particular assuming a priori equiprobable secret keys and additive white Gaussian measurement noise. Closed-form expressions and numerical computation are given. A study of the impact of the choice of the substitution box with respect to side-channel resistance reveals that its nonlinearity tends to homogenize the expressivity of success rate and guessing entropy. The intriguing approximate relation GE = 1/SR is observed in the case of 8-bit bytes and low noise.

The Interplay between Error, Total Variation, Alpha-Entropy and Guessing: Fano and Pinsker Direct and Reverse Inequalities

“…Taking the minimum over such sequences gives P m e (X|Y) ≤ P m e (X), which is Axiom 2. The case m = 1 was already shown, e.g., in [27]. Again, the result is quite intuitive: any side information Y can only improve the estimation of X.…”

“…The case ρ = 1 was already shown in [27]. The result is quite intuitive: any side information Y can only improve the guess of X.…”

“…For entropy H, the property H(X|Y) ≤ H(X|Z) amounts to I(X; Z) ≤ I(X; Y), i.e., (post-)processing in the Markov chain X − Y − Z can never increase information § 2.8 in [2]. The data processing inequality for P e and G was already shown in [27].…”

Be My Guesses: The interplay between side-channel leakage metrics

“…Obviously there is no one-to-one relation between them, but we show that one metric can be lower and upper bounded as a function of the other, which can be optimally determined for a given leakage model. This extended version complements the conference version [1] with results from Rioul [2][3] applied for the statistical 2 distance that we discuss in the side-channel context.…”

SCA-CGAN:A New Side-Channel Attack Method for Imbalanced Small Samples

“…(2) Guessing Entropy (GE) [23] GE is one of the common SCA metrics. In the implementation of DLSCA, assuming that the model to be evaluated is M, a score matrix on M can be calculated based on the power consumption dataset and the corresponding plaintext matrix as follows:…”