Higher-Order Glitches Free Implementation of the AES using Secure Multi-Party Computation Protocols - Extended Version

Higher-order side channel attacks (HO-SCA) is a powerful technique against cryptographic implementations and the design of appropriate countermeasures is nowadays an important topic. In parallel, another class of attacks, called glitches attacks, have been investigated which exploit the hardware glitches phenomena occurring during the physical execution of algorithms. Some solutions have been proposed to counteract HO-SCA at any order or to defeat glitches attacks, but no work has until now focussed on the definition of a sound countermeasure thwarting both attacks. We introduce in this paper a circuit model in which side-channel resistance in presence of glitches effects can be characterized. This allows us to construct the first glitches free HO-SCA countermeasure. The new construction can be built from any Secure Multi-Party Computation protocol and, as an illustration, we propose to apply the protocol introduced by Ben-Or et al. at STOC in 1988. The adaptation of the latter protocol to the context of side-channel analysis results in a completely new higher-order masking scheme, particularly interesting when addressing resistance in the presence of glitches. An application of our scheme to the AES block cipher is detailed, as well as an information theoretic evaluation of the new masking function that we call polynomial masking

First-Order Side-Channel Attacks on the Permutation Tables Countermeasure –Extended Version–

The use of random permutation tables as a side-channel attack countermeasure was recently proposed by Coron [6]. The countermeasure operates by ensuring that during the execution of an algorithm, each intermediate variable that is handled is in a permuted form described by the random permutation tables. In this paper, we examine the application of this countermeasure to the AES algorithm as described in [6], and show that certain operations admit first-order side-channel leakage. New side-channel attacks are developed to exploit these flaws, using correlation-based and mutual information-based methods. The attacks have been verified in simulation, and in practice on a smart card

Breaking Cryptographic Implementations Using Deep Learning Techniques

Template attack is the most common and powerful profiled side channel attack. It relies on a realistic assumption regarding the noise of the device under attack: the probability density function of the data is a multivariate Gaussian distribution. To relax this assumption, a recent line of research has investigated new profiling approaches mainly by applying machine learning techniques. The obtained results are commensurate, and in some particular cases better, compared to template attack. In this work, we propose to continue this recent line of research by applying more sophisticated profiling techniques based on deep learning. Our experimental results confirm the overwhelming advantages of the resulting new attacks when targeting both unprotected and protected cryptographic implementations

Linear Repairing Codes and Side-Channel Attacks

International audienceTo strengthen the resistance of countermeasures based on secret sharing, several works have suggested to use the scheme introduced by Shamir in 1978, which proposes to use the evaluation of a random d-degree polynomial into n d+1 public points to share the sensitive data. Applying the same principles used against the classical Boolean sharing, all these works have assumed that the most efficient attack strategy was to exploit the minimum number of shares required to rebuild the sensitive value; which is d + 1 if the reconstruction is made with Lagrange's interpolation. In this paper, we highlight first an important difference between Boolean and Shamir's sharings which implies that, for some signal-to-noise ratio, it is more advantageous for the adversary to observe strictly more than d + 1 shares. We argue that this difference is related to the existence of so-called exact linear repairing codes, which themselves come with reconstruction formulae that need (much) less information (counted in bits) than Lagrange's interpolation. In particular, this result implies that, contrary to what was believed, the choice of the public points in Shamir's sharing has an impact on the countermeasure strength. As another contribution, we exhibit a positive impact of the existence of linear exact repairing schemes; we indeed propose to use them to improve the state-of-the-art multiplication algorithms dedicated to Shamir's sharing. We argue that the improvement can be effective when the multiplication operation in the base field is at least two times smaller than in its sub-fields

Higher-order Masking and Shuffling for Software Implementations of Block Ciphers

Differential Power Analysis (DPA) is a powerful side channel key recovery attack that efficiently breaks block ciphers implementations. In software, two main techniques are usually applied to thwart them: masking and operations shuffling. To benefit from the advantages of the two techniques, recent works have proposed to combine them. However, the schemes which have been designed until now only provide limited resistance levels and some advanced DPA attacks have turned out to break them. In this paper, we investigate the combination of masking and shuffling. We moreover extend the approach with the use of higher-order masking and we show that it enables to significantly improve the security level of such a scheme. We first conduct a theoretical analysis in which the efficiency of advanced DPA attacks targeting masking and shuffling is quantified. Based on this analysis, we design a generic scheme combining higher-order masking and shuffling. This scheme is scalable and its security parameters can be chosen according to any desired resistance level. As an illustration, we apply it to protect a software implementation of AES for which we give several security/efficiency trade-offs

Algebraic Decomposition for Probing Security

The probing security model is very popular to prove the side-channel security of cryptographic implementations protected by masking. A common approach to secure nonlinear functions in this model is to represent them as polynomials over a binary field and to secure their nonlinear multiplications thanks to a method introduced by Ishai, Sahai and Wagner at Crypto 2003. Several schemes based on this approach have been published, leading to the recent proposal of Coron, Roy and Vivek which is currently the best known method when no particular assumption is made on the algebraic structure of the function. In the present paper, we revisit this idea by trading nonlinear multiplications for low-degree functions. Specifically, we introduce an algebraic decomposition approach in which a nonlinear function is represented as a sequence of functions with low algebraic degrees. We therefore focus on the probing-secure evaluation of such low-degree functions and we introduce three novel methods to tackle this particular issue. The paper concludes with a comparative analysis of the proposals, which shows that our algebraic decomposition method outperforms the method of Coron, Roy and Vivek in several realistic contexts

Affine Masking against Higher-Order Side Channel Analysis

In the last decade, an effort has been made by the research community to find efficient ways to thwart side channel analysis (SCA) against physical implementations of cryptographic algorithms. A common countermeasure for implementations of block ciphers is Boolean masking which randomizes by the bitwise addition of one or several random value(s) to the variables to be protected. However, advanced techniques called higher-order SCA attacks exist that overcome such a countermeasure. These attacks are greatly favored by the very nature of Boolean masking. In this paper, we revisit the affine masking initially introduced by Von Willich in 2001 as an alternative to Boolean masking. We show how to apply it to AES at the cost of a small timing overhead compared to Boolean masking. We then conduct an in-depth analysis pinpointing the leakage reduction implied by affine masking. Our results clearly show that the proposed scheme provides an excellent performance-security trade-off to protect AES against higher-order SCA

Modeling extreme values resulting from compromising electromagnetic emanations generated by an information system

International audienceElectromagnetic intelligence and attacks pose unacceptable risks for the security and safety of critical networks and more specifically the power network. In this paper, it is pointed out how the use of the excess model allows one to extrapolate the very high level of spurious compromising emanations induced by an information system in realistic power network models. It is shown that the design of appropriate protections and risk management methodologies can be enhanced thanks to the extreme value statistics

Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database

To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantage compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address theses limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we clarify the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electro-magnetic measurements exploited in our benchmarks. This open database, named ASCAD, has been specified to serve as a common basis for further works on this subject. Our work confirms the conclusions made by Cagli et al. at CHES 2017 about the high potential of convolutional neural networks. Interestingly, it shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis