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International audience; Block ciphers are protected against side-channel attacks by masking. On one hand, when the leakage model is unknown, second-order correlation attacks are typically used. On the other hand, when the leakage model can be profiled, template attacks are prescribed. But what if the profiled model does not exactly match that of the attacked device? One solution consists in regressing on-the-fly the scaling parameters from the model. In this paper, we leverage an Expectation-Maximization (EM) algorithm to implement such an attack. The resulting unprofiled EM attack, termed U-EM, is shown to be both efficient (in terms of number of traces) and effective (computationally speaking). Based on synthetic and real traces, we introduce variants of our U-EM attack to optimize its performance, depending on trade-offs between model complexity and epistemic noise. We show that the approach is flexible, in that it can easily be adapted to refinements such as different points of interest and number of parameters in the leakage model.

International audience; The kissing number of a code is the average number of pairs of codewords at minimum distance from each other. It has fundamental applications in determining codes performances. Besides, a recent interest has arisen from the field of side- channel analysis of algorithms handling sensitive information (e.g., cryptographic keys). Namely, when code-base masking protections are applied, their performance in terms of attacker’s signal-to-noise ratio or mutual information is proportional to the kissing number of the masking code. Therefore the kissing number is also a security metric for a given minimum distance in side-channel protected implementation, as it is in codes performance evaluation. It is known exactly for some classical families of codes. To estimate it in general, two types of bounds are given. Linear programming, either numerically or by the polynomial method is the most versatile and the more precise. Spectral graph theory provides bounds on the multiplicity of the subdominant eigenvalue that are easier to state.

Ce document a pour objectif de rassembler les connaissances actuelles liées aux impacts environnementaux de la 5G.Il s’articule autour de questions liées à ces impacts. Pour cela, il est organisé en trois partie. La première présentée icidonne les bases nécessaires à la compréhension de la technologie elle-même. La deuxième détaillera les services renduspossibles par la 5G. La dernière se penchera enfin sur les impacts environnementaux du déploiement de la 5G.

International audience; Claude Shannon, who laid the foundations of the celebrated information theory in 1948, preferred the term “communication theory”. In a 1953 paper, he proposed a new approach to information and its lattice structure. Surprisingly, this remained relatively unknown. We briefly recall the features of this theory and the characteristics that could explain its poor impact; finally we consider possible interesting developments.; Claude Shannon, qui a posé les bases de la célèbre théorie de l’information en 1948, préférait parler de théorie de la communication. Dans un article de 1953, il a proposé une nouvelle approche de l’information et de sa structure en treillis. Cela n’a étonnamment eu qu’un très faible écho. Nous rappelons brièvement les traits principaux de cette théorie et les caractéristiques qui pourraient en expliquer l’oubli, et nous envisageons enfin les développements intéressants qu’elle pourrait susciter.

International audience; We address the estimation of the Ricean K factor when the available complex channel samples are noisy and subject to Nakagami-m shadowing, i.e., the line-of-sight component is modeled as a Nakagami-m random variable. We propose two estimators: one based on the expectation-maximization (EM) procedure, and a second one based on the method of moment (MoM). The MoM estimator can be used to initialize the EM procedure. We show by simulations that the two proposed estimators outperform the existing ones.

International audience; This paper proposes an extended coded caching scheme based on piggyback coding for single-server multi-user networks with decentralized caching. The proposed scheme is obtained by adapting Polar codes and extending the original coded caching scheme, which is based on index coding and a data assignment that can be implemented via minimum graph-colouring. Polar codes are adapted so that users can apply parts of their cache contents as the frozen bits for Polar decoding, and the coded caching is adapted so as to account for different user coding rates and to combine transmissions to cache-aided and cache-free users. Numerical simulations prove that our piggyback-coding based scheme achieves higher rates than previous schemes also in the finite block-length regime. Finally, real testbed measurements are presented, which validate the practical implementation.

We propose a method to estimate the amplitude of an unexpected power loss which, leveraging on a calibration, enables the real-time monitoring of a network link. It is based on an existing fiber-longitudinal power profile evaluation technique. The reliability of the method is assessed experimentally. When the anomaly is located at 0 km from the beginning of the span, the estimation bias is smaller than 0.2 dB for losses up to 10 dB. When the anomaly is located at 25 km from the beginning of the span, the same estimation bias is observed but for losses up to 5 dB. In both cases, the standard deviation of the estimation is smaller than 0.2 dB.

International audience; This paper analyses the multiplexing gain (MG) achievable over Wyner's symmetric network with random user activity and random arrival of mixed-delay traffic. The mixeddelay traffic is composed of delay-tolerant traffic and delaysensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. We derive inner and outer bounds on the MG region. In the limit as D → ∞, the bounds coincide and the results show that transmitting delaysensitive messages does not cause any penalty on the sum MG. For finite D our bounds are still close and prove that the penalty caused by delay-sensitive transmissions is small.

International audience; We use linear programming (LP) to derive upper and lower bounds on the "kissing number" A d of any q-ary code C with distance distribution frequencies Ai, in terms of the given parameters (n, M, d). In particular, a polynomial method gives explicit analytic bounds in a certain range of parameters, which are sharp for some low-rate codes like the first-order Reed-Muller codes. The general LP bounds are more suited to numerical estimates. Besides the classical estimation of the probability of decoding error and of undetected error, we outline recent applications in hardware protection against side-channel attacks using code-based masking countermeasures, where the protection is all the more efficient as the kissing number is low.

While machine learning applications are getting mainstream owing to a demonstrated efficiency in solving complex problems, they suffer from inherent vulnerability to adversarial attacks. Adversarial attacks consist of additive noise to an input which can fool a detector. Recently, successful real-world printable adversarial patches were proven efficient against state-of-the-art neural networks. In the transition from digital noise based attacks to real-world physical attacks, the myriad of factors affecting object detection will also affect adversarial patches. Among these factors, view angle is one of the most influential, yet under-explored. In this paper, we study the effect of view angle on the effectiveness of an adversarial patch. To this aim, we propose the first approach that considers a multi-view context by combining existing adversarial patches with a perspective geometric transformation in order to simulate the effect of view angle changes. Our approach has been evaluated on two datasets: the first dataset which contains most real world constraints of a multi-view context, and the second dataset which empirically isolates the effect of view angle. The experiments show that view angle significantly affects the performance of adversarial patches, where in some cases the patch loses most of its effectiveness. We believe that these results motivate taking into account the effect of view angles in future adversarial attacks, and open up new opportunities for adversarial defenses., To appear in the 20th CyberWorlds Conference

Side-channel attacks aim at extracting secret keys from cryptographic devices. Ran- domly masking the implementation is a provable way to protect the secrets against this threat. Recently, various masking schemes have converged to the “code-based masking” philosophy. In code-based masking, different codes allow for different levels of side-channel security. In practice, for a given leakage function, it is important to select the code which enables the best resistance, i.e., which forces the attacker to capture and analyze the largest number of side-channel traces.This paper is a first attempt to address the constructive selection of the optimal codes in the context of side-channel countermeasures, in particular for code-based masking when the device leaks information in the Hamming weight leakage model. We show that the problem is related to the weight enumeration of the extended dual of the masking code. We first present mathematical tools to study those weight enumeration polynomials, and then provide an efficient method to search for good codes, based on a lexicographic sorting of the weight enumeration polynomial from lowest to highest degrees.

International audience

Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset 1 with several classes representing realistic intrusions, and a normal class-a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learningbased sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting invehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type., Les systèmes de détection d'intrusion sont largement utilisés pour détecter les cyberattaques, en particulier sur les protocoles vulnérables aux attaques de piratage tels que SOME/IP. Dans cet article, nous présentons un modèle séquentiel basé sur l'apprentissage profond pour la détection d'intrusion hors ligne sur le protocole de couche d'application SOME/IP. Pour évaluer notre système de détection d'intrusion, nous avons généré et étiqueté un ensemble de données 1 avec plusieurs classes représentant des intrusions réalistes, et une classe normale - une contribution significative en raison de l'absence de tels ensembles de données accessibles au public. En outre, nous proposons également un réseau de neurones récurrents (RNN), en tant qu'instance de modèle séquentiel basé sur l'apprentissage en profondeur, que nous appliquons à notre ensemble de données généré. Les résultats numériques montrent que RNN excelle dans la prédiction des intrusions dans les véhicules, avec des scores F1 et des valeurs AUC supérieures à 0,8 en fonction de chaque type d'intrusion.

International audience; What is Shannon’s information theory (IT)? Despite its continued impact on our digital society, Claude Shannon’s life and work is still unknown to numerous people. In this tutorial, we review many aspects of the concept of entropy and information from a historical and mathematical point of view. The text is structured into small, mostly independent sections, each covering a particular topic. For simplicity we restrict our attention to one-dimensional variables and use logarithm and exponential notations log and exp without specifying the base. We culminate with a simple exposition of a recent proof (2017) of the entropy power inequality (EPI), one of the most fascinating inequalities in the theory.

International audience; We study challenge codes for physically unclonable functions (PUFs). Starting from the classical Hadamard challenge code, we augment it by one vector. Numerical values suggest that the optimal choice of this vector for maximizing the entropy is to pick a vector the farthest away from the code formed by the challenges and their binary complements. This leads us to study the covering radius of Hadamard codes. A notion of bent sequence that generalizes the classical notion from Hadamard matrices of Sylvester type to general Hadamard matrices is given. Lower bounds for Paley-type Hadamard matrices are given.

International audience; The information leakage of a cryptographic implementation with a given degree of protection is evaluated in a typical situation when the signal-to-noise ratio is small. This is solved by expanding Kullback-Leibler divergence, entropy, and mutual information in terms of moments/cumulants.

International audience; Nous proposons une méthode pour estimer l'amplitude d'une chute de puissance dans un réseau fibré en utilisant un estimateur de profils longitudinaux de puissance basé sur l'analyse de signal reçu. Celle-ci s'appuie notamment sur une calibration et un formalisme simple. Nous analysons expérimentalement la précision de l'estimation en fonction de la distance à l'amplificateur précédent.

International audience; This paper presents a unified approach to quantifying the information leakages in the most general code-based masking schemes. Specifically, by utilizing a uniform representation, we highlight first that all code-based masking schemes’ side-channel resistance can be quantified by an all-in-one framework consisting of two easy-tocompute parameters (the dual distance and the number of conditioned codewords) from a coding-theoretic perspective. In particular, we use signal-to-noise ratio (SNR) and mutual information (MI) as two complementary metrics, where a closed-form expression of SNR and an approximation of MI are proposed by connecting both metrics to the two coding-theoretic parameters. Secondly, considering the connection between Reed-Solomon code and SSS (Shamir’s Secret Sharing) scheme, the SSS-based masking is viewed as a particular case of generalized code-based masking. Hence as a straightforward application, we evaluate the impact of public points on the side-channel security of SSS-based masking schemes, namely the polynomial masking, and enhance the SSS-based masking by choosing optimal public points for it. Interestingly, we show that given a specific security order, more shares in SSS-based masking leak more information on secrets in an information-theoretic sense. Finally, our approach provides a systematic method for optimizing the side-channel resistance of every code-based masking. More precisely, this approach enables us to select optimal linear codes (parameters) for the generalized code-based masking by choosing appropriate codes according to the two coding-theoretic parameters. Summing up, we provide a best-practice guideline for the application of code-based masking to protect cryptographic implementations.

A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When applying the proximal gradient algorithm to solve this problem, there exist quite affordable methods to implement the proximity operator (backward step) in the special case where the graph is a simple path without loops. In this paper, an algorithm, referred to as "Snake", is proposed to solve such regularized problems over general graphs, by taking benefit of these fast methods. The algorithm consists in properly selecting random simple paths in the graph and performing the proximal gradient algorithm over these simple paths. This algorithm is an instance of a new general stochastic proximal gradient algorithm, whose convergence is proven. Applications to trend filtering and graph inpainting are provided among others. Numerical experiments are conducted over large graphs.

Using information-theoretic tools, this paper establishes a mathematical link between the probability of success of a side-channel attack and the minimum number of queries to reach a given success rate, valid for any possible distinguishing rule and with the best possible knowledge on the attacker’s side. This link is a lower bound on the number of queries highly depends on Shannon’s mutual information between the traces and the secret key. This leads us to derive upper bounds on the mutual information that are as tight as possible and can be easily calculated. It turns out that, in the case of an additive white Gaussian noise, the bound on the probability of success of any attack is directly related to the signal to noise ratio. This leads to very easy computations and predictions of the success rate in any leakage model.

Neural belief propagation decoders were recently introduced by Nachmani et al. as a way to improve the decoding performance of belief propagation iterative algorithm for short to medium length linear block codes. The main idea behind these decoders is to represent belief propagation as a neural network, enabling adaptive weighting of the decoding process. In the present paper an efficient recurrent neural network architecture, based on gating and weights sharing mechanisms, is proposed to perform blind neural belief propagation decoding without prior knowledge of the coding scheme used by the encoder. The proposed architecture is able to learn to decode BCH (15,11) and BCH (15,7) codes at least at the level of performance of a standard belief propagation algorithm and even to outperform it in the case of BCH (15,11) code thanks to NBP approach. A particular emphasis is given to the interpretability and complexity of the proposed model to ensure scalability to larger codes.

International audience

International audience

To enhance the security of digital circuits, there is often a desire to dynamically generate, rather than statically store, random values used for identification and authentication purposes. Physically Unclonable Functions (PUFs) provide the means to realize this feature in an efficient and reliable way by utilizing commonly overlooked process variations that unintentionally occur during the manufacturing of integrated circuits (ICs) due to the imperfection of fabrication process. When given a challenge, PUFs produce a unique response. However, PUFs have been found to be vulnerable to modeling attacks where by using a set of collected challenge response pairs (CRPs) and training a machine learning model, the response can be predicted for unseen challenges. To combat this vulnerability, researchers have proposed techniques such as Challenge Obfuscation. However, as shown in this paper, this technique can be compromised via modeling the PUF's power side-channel. We first show the vulnerability of a state-of-the-art Challenge Obfuscated PUF (CO-PUF) against power analysis attacks by presenting our attack results on the targeted CO-PUF. Then we propose two countermeasures, as well as their hybrid version, that when applied to the CO-PUFs make them resilient against power side-channel based modeling attacks. We also provide some insights on the proper design metrics required to be taken when implementing these mitigations. Our simulation results show the high success of our attack in compromising the original Challenge Obfuscated PUFs (success rate > 98%) as well as the significant improvement on resilience of the obfuscated PUFs against power side-channel based modeling when equipped with our countermeasures.

In 1994, Jim Massey proposed the guessing entropy as a measure of the difficulty that an attacker has to guess a secret used in a cryptographic system, and established a well-known inequality between entropy and guessing entropy. Over 15 years before, in an unpublished work, he also established a well-known inequality for the entropy of an integer-valued random variable of given variance. In this paper, we establish a link between the two works by Massey in the more general framework of the relationship between discrete (absolute) entropy and continuous (differential) entropy. Two approaches are given in which the discrete entropy (or R\'enyi entropy) of an integer-valued variable can be upper bounded using the differential (R\'enyi) entropy of some suitably chosen continuous random variable. As an application, lower bounds on guessing entropy and guessing moments are derived in terms of entropy or R\'enyi entropy (without side information) and conditional entropy or Arimoto conditional entropy (when side information is available)., Comment: 16 pages, 4 figures

International audience; Graph neural networks (GNN) have been recognized as powerful tools for learning representations in graph structured data. The key idea is to propagate and aggregate information along edges of the given graph. However, little work has been done to analyze the effect of noise on their performance. By conducting a number of simulations, we show that GNN are very sensitive to the graph noise. We propose a graphassisted Bayesian node classifier which takes into account the degree of impurity of the graph, and show that it consistently outperforms GNN based classifiers on benchmark datasets, particularly when the degree of impurity is moderate to high.

A finite metric space is called here distance degree regular if its distance degree sequence is the same for every vertex. A notion of designs in such spaces is introduced that generalizes that of designs in $Q$-polynomial distance-regular graphs. An approximation of their cumulative distribution function, based on the notion of Christoffel function in approximation theory is given. As an application we derive limit laws on the weight distributions of binary orthogonal arrays of strength going to infinity. An analogous result for combinatorial designs of strength going to infinity is given., Comment: 18 pages

International audience; In this paper, we address power allocation for an uplink multiband single-beam satellite system taking into account on-board nonlinearities. These nonlinearities are generated by the high power amplifier. Based on the closed-form expression of the capacity associated with the optimal receiver exploiting the structure of the nonlinear effects, three power allocations are studied: maximization of the sum-rate, maximization of the minimum of the rates, and minimization of the sum of the powers. The related optimization problems boil down nonconvex problems that can be cast and solved by using signomial programming. We propose practical and scalable algorithms for fixing these power allocation problems.

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache's downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations., To appear ITW2020

International audience; Persistent fault analysis (PFA) consists in guessing block cipher secret keys by biasing their substitution box. This paper improves the original attack of Zhang et al. on AES-128 presented at CHES 2018. By a thorough analysis, the exact probability distribution of the ciphertext (under a uniformly distributed plaintext) is derived, and the maximum likelihood key recovery estimator is computed exactly. Its expression is turned into an attack algorithm, which is shown to be twice more efficient in terms of number of required encryptions than the original attack of Zhang et al. This algorithm is also optimized from a computational complexity standpoint. In addition, our optimal attack is naturally amenable to key enumeration, which expedites full 16-bytes key extraction. Various tradeoffs between data and computational complexities are investigated.

International audience; A compact and closed-form expression of capacity is derived for a uplink multiband satellite system in the presence of nonlinear interference. The nonlinear effect comes from the satellite high-power amplifier modeled by a Volterra series expansion. The derivations reveal that the nonlinear interference can provide a constructive power contribution that could be used to increase the transmission rate. Consequently, decoders designed by viewing this interference as only an additional noise are suboptimal. Numerical results confirm this claim and also shows that an appropriate power allocation amongst the subbands may be of interest.

A data representation technique dubbed Kolmogorov model (KM), has been applied to the beam alignment problem in large-dimensional antenna systems. The previous learning-based beam alignment solely focused on utilizing the predictive power of KM, i.e., the capability of predicting the outcome of random variables that are outside the training set, to reduce the beam training overhead. However, a distinctive feature of KM, namely, the interpretability which enables the capability of extracting additional information hidden inside the data, has not yet been exploited. Moreover, the prohibitively high computational complexity of the existing KM learning algorithm offsets the benefits brought by KM and hampers its application to large-scale problems. In this paper, we propose a joint beam alignment and tracking framework by incorporating the predictability and interpretability of KM. Especially, our proposed scheme enables a novel interpretable beam tracking that reveals insights on relations among the sounded observations to alleviate the beam sounding overhead after the initial beam alignment. To reduce the computational complexity of KM learning, two enhancement approaches, based on discrete monotonic optimization (DMO) and dual optimization, respectively, are proffered. Numerical results demonstrate that the proposed methods can reduce the computational cost of the existing KM learning algorithm by up to three orders of magnitude. Furthermore, the proposed methods show superior beam alignment and tracking performance over other state-of-the-art techniques, notably in the low signal-to-noise ratio (SNR) regime.

The paper presents upper and lower bounds on the per-sector degrees of freedom (DoF) of a sectorized hexagonal cellular model when neighboring base stations (BSs) can cooperate during at most $\Delta $ interaction rounds over rate-limited backhaul links. The lower bound is based on practically implementable beamforming and adapts the way BSs cooperate to the sectorization of the cells. It improves over the naive approach that ignores this sectorization in terms of the sum-rate, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. For moderate SNR, the new scheme improves also over an opportunistic cooperation strategy where each message is decoded based on the signals received at the three adjacent sectors with the best SNR. The upper bound is information-theoretic and holds for all possible coding schemes, including for example interference alignment with unlimited symbol extensions whose practical implementation currently seems out of reach. Lower and upper bounds show that the complexity constraint, imposed by limiting the number of interaction rounds $\Delta $ , indeed limits the largest achievable sum-rate and DoF. In particular, irrespective of the backhaul capacity $\mu $ , the per-sector DoF cannot exceed a threshold which depends on $\Delta $ .

International audience

International audience; We give an informative review of the notions of Rényi's αentropy and α-divergence, Arimoto's conditional α-entropy, and Sibson's α-information, with emphasis on the various relations between them. All these generalize Shannon's classical information measures corresponding to α = 1. We present results on data processing inequalities and provide some new generalizations of the classical Fano's inequality for any α > 0. This enables one to α-information as a information theoretic metric of leakage in secrecy systems. Such metric can bound the gain of an adversary in guessing some secret (any potentially random function of some sensitive dataset) from disclosed measurements, compared with the adversary's prior belief (without access to measurements).

Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, the Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally. In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, the eigenvalue decomposition (EVD) elimination strategy and an approximate EVD algorithm. Furthermore, a thresholding technique by exploiting the error bound analysis and leveraging the normalized Minkowski $\ell_1$-norm, is provided for the selection of the number of iterations of the approximate EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds $80\%$., Published in the IEEE Transactions on Signal Processing (15 pages, 11 figures, and 6 tables)

International audience; This chapter discusses a perspective on designing interaction by quantifying information that reduces the computer's uncertainty about the user's goal. We begin with how to quantify uncertainty and information using Shannon's information-theoretic terms and how to optimize decisions under uncertainty using an expected utility function with Bayesian Experimental Design. We then describe the BIG framework-Bayesian Information Gain-where the computer "runs experiments" on the user by sending feedback that maximizes the expected gain of information by the computer, and uses the users' subsequent input to update its knowledge as interaction progresses. We demonstrate a BIG application to multiscale navigation, discuss some limitations of the BIG framework and conclude with future possibilities.

International audience; In any side-channel attack, it is desirable to exploit all the available leakage data to compute the distinguisher’s values. The profiling phase is essential to obtain an accurate leakage model, yet it may not be exhaustive. As a result, information theoretic distinguishers may come up on previously unseen data, a phenomenon yielding empty bins. A strict application of the maximum likelihood method yields a distinguisher that is not even sound. Ignoring empty bins reestablishes soundness, but seriously limits its performance in terms of success rate. The purpose of this paper is to remedy this situation. In this research, we propose six different techniques to improve the performance of information theoretic distinguishers. We study them thoroughly by applying them to timing attacks, both with synthetic and real leakages. Namely, we compare them in terms of success rate, and show that their performance depends on the amount of profiling, and can be explained by a bias-variance analysis. The result of our work is that there exist use-cases, especially when measurements are noisy, where our novel information theoretic distinguishers (typically the soft-drop distinguisher) perform the best compared to known side-channel distinguishers, despite the empty bin situation.

International audience; Trajectories in human aimed movements are inherently variable. Using the concept of positional variance profiles, such trajectories are shown to be decomposable into two phases: In a first phase, the variance of the limb position over many trajectories increases rapidly; in a second phase, it then decreases steadily. A new theoretical model, where the aiming task is seen as a Shannon-like communication problem, is developed to describe the second phase: Information is transmitted from a "source" (determined by the position at the end of the first phase), to a "destination" (the movement's end-point) over a "channel" perturbed by Gaussian noise, with the presence of a noiseless feedback link. Information-theoretic considerations show that the positional variance decreases exponentially with a rate equal to the channel capacity C. Two existing datasets for simple pointing tasks are re-analyzed and observations on real data confirm our model. The first phase has constant duration and C is found constant across instructions and task parameters, which thus characterizes the participant's performance. Our model provides a clear understanding of the speed-accuracy tradeoff in aimed movements: Since the participant's capacity is fixed, a higher prescribed accuracy necessarily requires a longer second phase resulting in an increased overall movement time. The well-known Fitts' law is also recovered using this approach.

International audience; A framework for deriving Rényi entropy-power inequalities (REPIs) is presented that uses linearization and an inequality of Dembo, Cover, and Thomas. Simple arguments are given to recover the previously known Rényi EPIs and derive new ones, by unifying a multiplicative form with constant c and a modification with exponent α of previous works. An information-theoretic proof of the Dembo-Cover-Thomas inequality-equivalent to Young's convolutional inequality with optimal constants-is provided, based on properties of Rényi conditional and relative entropies and using transportation arguments from Gaussian densities. For log-concave densities, a transportation proof of a sharp varentropy bound is presented. This work was partially presented at the 2019 Information Theory and Applications Workshop, San Diego, CA.

In this paper we consider the downlink precoding for massive multi-user multiple-input multiple-output (MU-MIMO) antenna systems. We address the problem of optimizing the channel state information (CSI) process timeline. So we propose a new feedback quantity, referred to as channel state information accuracy indicator (CSIAI), which will be transmitted by the user equipment (UE) to the base station (BS). The design of an appropriate precoding matrix based on the CSIAI reporting has the advantage of enhancing the overall system performance. It was shown that an improvement in the average bit error rate (BER) performance is achieved.

International audience; Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.

International audience; Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.

International audience; Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.

International audience; Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.

International audience; Quand les sciences s'invitent dans l'atelier du sculpteur... Deuxième art après l'architecture mais avant la peinture, la sculpture désigne étymologiquement l'art de tailler la pierre. Restrictive, cette définition a depuis évolué, incluant des techniques aussi variées que sont le modelage, l'assemblage, l'embossage ou la soudure. Ce faisant, elle s'ouvrait à d'autres matériaux : argile, plâtre, cire, métal, bois, verre, résines. Aujourd'hui tout à la fois plasticien, paysagiste, menuisier, potier, verrier, forgeron, le sculpteur se joue de la matière. Dans ses trois états il l'étire, la chauffe, l'ajoute ou la retire. Tout en faisant des sciences. En témoignent les outils et les procédés dont quelques observations, de simples activités récréatives ou une sortie au musée nous révèlent combien les mathématiques y sont esthétiques et indispensables.

We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity in the lattice dimension and a quasi-linear complexity in the list-size. The error rate is shown to be close to the universal lower bound in dimensions 64 and 128., Presented at ISIT 2020

International audience; Hick's law is a key quantitative law in Psychology that relates reaction time to the logarithm of the number of stimulus-response alternatives in a task. Its application to HCI is controversial: Some believe that the law does not apply to HCI tasks, others regard it as the cornerstone of interface design. The law, however, is often misunderstood. We review the choice-reaction time literature and argue that: (1) Hick's law speaks against, not for, the popular principle that 'less is better'; (2) logarithmic growth of observed temporal data is not necessarily interpretable in terms of Hick's law; (3) the stimulus-response paradigm is rarely relevant to HCI tasks, where choice-reaction time can often be assumed to be constant; and (4) for user interface design, a detailed examination of the effects on choice-reaction time of psychological processes such as visual search and decision making is more fruitful than a mere reference to Hick's law.

International audience

This paper analyzes the multiplexing gains (MG) achievable over Wyner&rsquo, s soft-handoff model under mixed-delay constraints, that is, when delay-sensitive and delay-tolerant data are simultaneously transmitted over the network. In the considered model, delay-sensitive data cannot participate or profit in any ways from transmitter or receiver cooperation, but delay-tolerant data can. Cooperation for delay-tolerant data takes place over rate-limited links and is limited to a fixed number of cooperation rounds. For the described setup, inner and outer bounds are derived on the set of MG pairs that are simultaneously achievable for delay-sensitive and delay-tolerant data. The bounds are tight in special cases and allow us to obtain the following conclusions. For large cooperation rates, and when both transmitters and receivers can cooperate, it is possible to simultaneously attain maximum MG for delay-sensitive messages and maximum sum MG for all messages. For comparison, in scheduling schemes (also called time-sharing schemes), the largest achievable sum MG decreases linearly with the MG of delay-sensitive messages. A similar linear decrease is proved for any coding scheme, not only for scheduling schemes, if only transmitters or only receivers can cooperate (but not both) and if delay-sensitive messages have moderate MG. In contrast, if the MG of delay-sensitive messages is small, the maximum sum MG can be achieved even with only transmitter or only receiver cooperation. To summarise, when cooperation rates are high and both transmitters and receivers can cooperate or when delay-sensitive messages have small MG, then transmitting delay-sensitive messages causes no penalty on the sum-MG. In other regimes, this penalty increases proportionally to the delay-tolerant MG in the sense that increasing the delay-sensitive MG by &Delta, penalises the largest achievable delay-tolerant MG by 2 &Delta, and thus the sum MG by &Delta