Publications

2021

• An Anonymous Trace-and-Revoke Broadcast Encryption Scheme
  
  • Blazy Olivier
  • Mukherjee Sayantan
  • Nguyen Huyen
  • Hieu Phan Duong
  • Stehlé Damien
  , 2021, 13083, pp.214-233. Broadcast Encryption is a fundamental cryptographic primitive, that gives the ability to send a secure message to any chosen target set among registered users. In this work, we investigate broadcast encryption with anonymous revocation, in which ciphertexts do not reveal any information on which users have been revoked. We provide a scheme whose ciphertext size grows linearly with the number of revoked users. Moreover, our system also achieves traceability in the black-box confirmation model. Technically, our contribution is threefold. First, we develop a generic transformation of linear functional encryption toward trace-and-revoke systems. It is inspired from the transformation by Agrawal et al. (CCS’17) with the novelty of achieving anonymity. Our second contribution is to instantiate the underlying linear functional encryptions from standard assumptions. We propose a DDH-based construction which does no longer require discrete logarithm evaluation during the decryption and thus significantly improves the performance compared to the DDH-based construction of Agrawal et al.. In the LWE-based setting, we tried to instantiate our construction by relying on the scheme from Wang et al. (PKC’19) but finally found an attack to this scheme. Our third contribution is to extend the 1-bit encryption from the generic transformation to n-bit encryption. By introducing matrix multiplication functional encryption, which essentially performs a fixed number of parallel calls on functional encryptions with the same randomness, we can prove the security of the final scheme with a tight reduction that does not depend on n, in contrast to employing the hybrid argument. (10.1007/978-3-030-90567-5_11)
  DOI : 10.1007/978-3-030-90567-5_11
• Le bon virage
  
  • Zayana Karim
  • Lusseau Cédric
  • Pantaloni Vincent
  • Rabiet Victor
  CultureMath, ENS, 2021.
• Metasurface de Huygens multibande avec dépointage du faisceau dans la bande 5G 3.4–3.8 GHz
  
  • Gonçalves Licursi de Mello Rafael
  • Lepage Anne Claire
  • Begaud Xavier
  , 2021. Les métasurfaces de Huygens sont des outils puissants pour façonner les fronts d'ondes électromagnétiques. Dans ce travail, nous présentons une cellule multibande et reconfigurable d’une métasurface de Huygens qui permet le dépointage du faisceau dans la bande 5G (3.4-3.8 GHz) sans perturber la phase et la transmission dans les bandes 4G (2.5–2.7 GHz) et Wi-Fi (2.4–2.5 GHz, 5.17–5.83 GHz, 5.93–6.45 GHz). Il est ainsi possible d’obtenir une variation de phase allant jusqu’à 114° dans la bande 5G avec un impact négligeable dans les autres bandes.
• Artificial Neural Network-Based Uplink Power Prediction From Multi-Floor Indoor Measurement Campaigns in 4G Networks
  
  • Mazloum Taghrid
  • Wang Shanshan
  • Hamdi Maryem
  • Ashenafi Mulugeta Biruk
  • Wiart Joe
  Frontiers in Public Health, Frontiers Media S.A., 2021, 9, pp.777798-1:777798-8. Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in the number of user equipment has strengthened public concerns on human exposure to radio-frequency electromagnetic fields (RF EMFs). This requires an assessment and monitoring of RF EMF exposure, in an almost continuous way. Particular interest goes to the uplink (UL) exposure, assessed through the transmission power of the mobile phone, due to its close proximity to the human body. However, the UL transmit (TX) power is not provided by the off-the-shelf modem and RF devices. In this context, we first conduct measurement campaigns in a multi-floor indoor environment using a drive test solution to record both downlink (DL) and UL connection parameters for Long Term Evolution (LTE) networks. Several usage services (including WhatsApp voice calls, WhatsApp video calls, and file uploading) are investigated in the measurement campaigns. Then, we propose an artificial neural network (ANN) model to estimate the UL TX power, by exploiting easily available parameters such as the DL connection indicators and the information related to an indoor environment. With those easy-accessed input features, the proposed ANN model is able to obtain an accurate estimation of UL TX power with a mean absolute error (MAE) of 1.487 dB. (10.3389/fpubh.2021.777798)
  DOI : 10.3389/fpubh.2021.777798
• Combinaison spatiale de puissance 2-18 GHz pour application radar
  
  • Spillebout Théo
  • Bergeault Eric
  • Geslin Florian
  • Lepage Anne Claire
  • Begaud Xavier
  • Belluot James
  • Di Paolo Franco
  • Leggieri Alberto
  , 2021.
• Analog Duty Cycle Controller Using Backgate Body Biasing For 5G Millimeter Wave Applications
  
  • Beauquier Clément
  • Duperray David
  • Jabbour Chadi
  • Desgreys Patricia
  • Frappé Antoine
  • Kaiser Andreas
  , 2021. This work presents the first 21-43 GHz CMOS analog Duty Cycle Controller (DCC) implemented in 28 nm FDSOI. The main application is millimeter wave mixers with CMOS digital signals. The proposed circuit corrects the input duty cycle with a negative feedback analog loop. Observability of the duty cycle is made through a passive low pass filter and the control is achieved by modifying the rise and fall time of the input clock signal, via backgate biasing of an inverter chain. The circuit has been validated by post layout, Monte-Carlo and corner simulations. At 28 GHz, the duty cycle correction range varies from 40 % to 55 %, and the additional power consumption introduced by the correction loop is frequency independent and is equal to 0.6 mW. (10.1109/ICECS53924.2021.9665600)
  DOI : 10.1109/ICECS53924.2021.9665600
• Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
  
  • Chehreghani Mostafa Haghir
  • Bifet Albert
  • Abdessalem Talel
  Fundamenta Informaticae, Polskie Towarzystwo Matematyczne, 2021, 182 (3), pp.219--242. (10.3233/FI-2021-2071)
  DOI : 10.3233/FI-2021-2071
• Pertes de transmission pour différents matériaux Indoor entre 2 et 170 GHz
  
  • Conrat Jean-Marc
  • Aliouane Mohamed
  • Begaud Xavier
  • Cousin Jean-Christophe
  , 2021.
• Design Method of Analog Sigmoid Function and its Approximate Derivative
  
  • Chabane Lylia Thiziri
  • Pham Germain
  • Chollet Paul
  • Desgreys Patricia
  , 2021. In this paper, we propose to implement the sigmoid function, which will serve as an activation function of the neurons of a Multi Layer Perceptron (MLP) network, as well as its approximate derivative using an analog circuit. Several implementations have already been proposed in the literature, in particular, by Lu et al. (2000), which offers both a configurable and simple circuit realized in 1.2 µm technology. In this paper we demonstrate the circuit design of a sigmoid function based on Lu et al. using 65 nm technology in order to reduce energy consumption and circuit area. The design is based on an indepth theoretical analysis of the circuit and validated by circuit level simulations. The main contributions of the paper are a modification of topology of the circuit in order to meet the required nonlinear response of the circuit and the extraction of the DC power consumption of the resulting circuit.
• Consensus Byzantin et blockchain : Modèles unifiés et nouveaux protocoles
  
  • Durand Antoine
  , 2021. Any distributed application makes use of agreement protocols in order to maintain a consistent state across multiple machines in a network. With the recent advent of Bitcoin and blockchain-based algorithms, there has been a renewed interest around such agreement protocols, especially regarding their ability to scale and tolerate malicious participants. However this attention has been a source of misunderstanding, flooding an already large and complex subject with vague claims and different terminology.In this thesis, we make a unifying view of the blockchain landscape, by proposing a formulation capturing a wide range of accepted models for agreement protocols. We use this formalism to describe the specifications of various agreement protocols of interest for blockchain. We reframe and make precise the theorems that describe the conditions under which a protocol is possible or not. We also use our framework to describe the model of several prominent blockchains despite their fundamental differences, and we are able to make a fine-grained assessment and comparison of their performance characteristics.Then, we make a proposal for a scalable blockchain, StakeCube. StakeCube's security is based on the Proof-of-Stake model, and its scalability relies on the sharding paradigm, implemented through a distributed hash table.We also implemented (a restricted version of) StakeCube and evaluated its performance, thus validating its scalability property.Notably, because StakeCube trades Proof-of-Work for Proof-of-Stake without sacrificing scalability, it is particularly well suited for IoT applications. To further demonstrate this aspect, we implemented an energy marketplace IoT application in StakeCube and were able to successfully test its viability.
• Water- PUF: An Insider Threat Resistant PUF Enrollment Protocol Based on Machine Learning Watermarking
  
  • Khalfaoui Sameh
  • Leneutre Jean
  • Villard Arthur
  • Gazeau Ivan
  • Ma Jingxuan
  • Danger Jean-Luc
  • Urien Pascal
  , 2021, pp.1-10. The demand for Internet of Things services is increasing exponentially, and consequently a big number of devices are being deployed. To efficiently authenticate these services, the use of Physical Unclonable Functions (PUF) has been introduced as a promising solution that is suitable for the resource-constraint nature of these devices. A growing number of PUF architectures has been demonstrated mathematically clonable through Machine Learning (ML) modeling techniques. The use of ML PUF models has been recently proposed to authenticate the IoT objects. This procedure facilitates the scalability of the authentication process by reducing the storage space required for each device. Nonetheless, the leakage scenario of the PUF model to an adversary due to an insider threat within the organization is not supported by the existing solutions. Hence, the security of these PUF model-based enrollment proposals can be compromised. In this paper, we propose an enrollment solution that exploits a ML PUF model in the authentication process, called Water-PUF. Our enrollment scheme is based on a specifically designed black-box watermarking technique for PUF models with a binary output response. This procedure prevents an adversary from relying on the watermarked model in question or another derivative model to bypass the authentication. Therefore, any leakage of the watermarked PUF model that is used for the enrollment does not affect the correctness of the protocol. The Water- PUF design is validated by a number of simulations against numerous watermark suppression attacks to assess the robustness of our proposal (10.1109/NCA53618.2021.9685239)
  DOI : 10.1109/NCA53618.2021.9685239
• Sliced-Wasserstein distance for large-scale machine learning : theory, methodology and extensions
  
  • Nadjahi Kimia
  , 2021. Many methods for statistical inference and generative modeling rely on a probability divergence to effectively compare two probability distributions. The Wasserstein distance, which emerges from optimal transport, has been an interesting choice, but suffers from computational and statistical limitations on large-scale settings. Several alternatives have then been proposed, including the Sliced-Wasserstein distance (SW), a metric that has been increasingly used in practice due to its computational benefits. However, there is little work regarding its theoretical properties. This thesis further explores the use of SW in modern statistical and machine learning problems, with a twofold objective: 1) provide new theoretical insights to understand in depth SW-based algorithms, and 2) design novel tools inspired by SW to improve its applicability and scalability. We first prove a set of asymptotic properties on the estimators obtained by minimizing SW, as well as a central limit theorem whose convergence rate is dimension-free. We also design a novel likelihood-free approximate inference method based on SW, which is theoretically grounded and scales well with the data size and dimension. Given that SW is commonly estimated with a simple Monte Carlo scheme, we then propose two approaches to alleviate the inefficiencies caused by the induced approximation error: on the one hand, we extend the definition of SW to introduce the Generalized Sliced-Wasserstein distances, and illustrate their advantages on generative modeling applications; on the other hand, we leverage concentration of measure results to formulate a new deterministic approximation for SW, which is computationally more efficient than the usual Monte Carlo technique and has nonasymptotical guarantees under a weak dependence condition. Finally, we define the general class of sliced probability divergences and investigate their topological and statistical properties; in particular, we establish that the sample complexity of any sliced divergence does not depend on the problem dimension.
• Cache-Aided Polar Coding: From Theory to Implementation
  
  • Fadlallah Yasser
  • Oubejja Othmane
  • Kamel Sarah
  • Ciblat Philippe
  • Wigger Michele
  • Gorce Jean-Marie
  IEEE Journal on Selected Areas in Information Theory, IEEE, 2021, 2 (4), pp.1206 - 1223. 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. (10.1109/JSAIT.2021.3128232)
  DOI : 10.1109/JSAIT.2021.3128232
• Leveraging lyrics from audio for MIR
  
  • Vaglio Andrea
  , 2021. Lyrics provide a lot of information about music since they encapsulate a lot of the semantics of songs. Such information could help users navigate easily through a large collection of songs and to recommend new music to them. However, this information is often unavailable in its textual form. To get around this problem, singing voice recognition systems could be used to obtain transcripts directly from the audio. These approaches are generally adapted from the speech recognition ones. Speech transcription is a decades-old domain that has lately seen significant advancements due to developments in machine learning techniques. When applied to the singing voice, however, these algorithms provide poor results. For a number of reasons, the process of lyrics transcription remains difficult. In this thesis, we investigate several scientifically and industrially difficult ’Music Information Retrieval’ problems by utilizing lyrics information generated straight from audio. The emphasis is on making approaches as relevant in real-world settings as possible. This entails testing them on vast and diverse datasets and investigating their scalability. To do so, a huge publicly available annotated lyrics dataset is used, and several state-of-the-art lyrics recognition algorithms are successfully adapted. We notably present, for the first time, a system that detects explicit content directly from audio. The first research on the creation of a multilingual lyrics-toaudio system are as well described. The lyrics-toaudio alignment task is further studied in two experiments quantifying the perception of audio and lyrics synchronization. A novel phonotactic method for language identification is also presented. Finally, we provide the first cover song detection algorithm that makes explicit use of lyrics information extracted from audio.
• Deep learning methods for music style transfer
  
  • Cífka Ondřej
  , 2021. Recently, deep learning methods have enabled transforming musical material in a data-driven manner. The focus of this thesis is on a family of tasks which we refer to as (one-shot) music style transfer, where the goal is to transfer the style of one musical piece or fragment onto another.In the first part of this work, we focus on supervised methods for symbolic music accompaniment style transfer, aiming to transform a given piece by generating a new accompaniment for it in the style of another piece. The method we have developed is based on supervised sequence-to-sequence learning using recurrent neural networks (RNNs) and leverages a synthetic parallel (pairwise aligned) dataset generated for this purpose using existing accompaniment generation software. We propose a set of objective metrics to evaluate the performance on this new task and we show that the system is successful in generating an accompaniment in the desired style while following the harmonic structure of the input.In the second part, we investigate a more basic question: the role of positional encodings (PE) in music generation using Transformers. In particular, we propose stochastic positional encoding (SPE), a novel form of PE capturing relative positions while being compatible with a recently proposed family of efficient Transformers.We demonstrate that SPE allows for better extrapolation beyond the training sequence length than the commonly used absolute PE.Finally, in the third part, we turn from symbolic music to audio and address the problem of timbre transfer. Specifically, we are interested in transferring the timbre of an audio recording of a single musical instrument onto another such recording while preserving the pitch content of the latter. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. As in the first part, we design a set of objective metrics for the task. We show that the proposed method is able to outperform existing ones.
• Apprendre à représenter et à générer du texte en utilisant des mesures d'information
  
  • Colombo Pierre
  , 2021. Natural language processing (NLP) allows for the automatic understanding and generation of natural language. NLP has recently received growing interest from both industry and researchers as deep learning (DL) has leveraged the staggering amount of available text (e.g web, youtube, social media) and reached human-like performance in several tasks (e.g translation, text classification). Besides, Information theory (IT) and DL have developed a long-lasting partnership. Indeed, IT has fueled the adoption of deep neural networks with famous principles such as Minimum Description Length (MDL), Information Bottleneck (IB) or the celebrated InfoMax principle. In all these principles, different measures of information (e.g entropy, MI, divergences) are one of the core concepts. In this thesis, we address the interplay between NLP and measures of information. Our contributions focus on two types of NLP problems : natural language understanding (NLU) and natural language generation (NLG). NLU aims at automatically understand and extract semantic information from an input text where NLG aims at producing natural language that is both well-formed (i.e grammatically correct, coherent) and informative. Building spoken conversational agents is a challenging issue and dealing with spoken conversational data remains a difficult and overlooked problem. Thus, our first contributions, are turned towards NLU and we focus on learning transcript representations. Our contribution focuses on learning better transcript representations that include two important characteristics of spoken human conversations : namely the conversational and the multi-modal dimension. To do so, we rely on various measures of information and leverage the mutual information maximization principle. The second group of contributions addresses problems related to NLG. This thesis specifically focuses on two core problems. First, we propose a new upper bound on mutual information to tackle the problem of controlled generation via the learning of disentangled representation (i.e style transfer and conditional sentence generation). Secondly, we address the problem of automatic evaluation of generated texts by developing a new family of metrics using various measuresof information.
• Does the Goal Matter? Emotion Recognition Tasks Can Change the Social Value of Facial Mimicry towards Artificial Agents
  
  • Perugia Giulia
  • Paetzel-Prüssman Maike
  • Hupont Isabelle
  • Varni Giovanna
  • Chetouani Mohamed
  • Peters Christopher Edward
  • Castellano Ginevra
  Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8. In this paper, we present a study aimed at understanding whether the embodiment and humanlikeness of an artificial agent can affect people's spontaneous and instructed mimicry of its facial expressions. The study followed a mixed experimental design and revolved around an emotion recognition task. Participants were randomly assigned to one level of humanlikeness (between-subject variable: humanlike, characterlike, or morph facial texture of the artificial agents) and observed the facial expressions displayed by a human (control) and three artificial agents differing in embodiment (within-subject variable: video-recorded robot, physical robot, and virtual agent). To study both spontaneous and instructed facial mimicry, we divided the experimental sessions into two phases. In the first phase, we asked participants to observe and recognize the emotions displayed by the agents. In the second phase, we asked them to look at the agents' facial expressions, replicate their dynamics as closely as possible, and then identify the observed emotions. In both cases, we assessed participants' facial expressions with an automated Action Unit (AU) intensity detector. Contrary to our hypotheses, our results disclose that the agent that was perceived as the least uncanny, and most anthropomorphic, likable, and co-present, was the one spontaneously mimicked the least. Moreover, they show that instructed facial mimicry negatively predicts spontaneous facial mimicry. Further exploratory analyses revealed that spontaneous facial mimicry appeared when participants were less certain of the emotion they recognized. Hence, we postulate that an emotion recognition goal can flip the social value of facial mimicry as it transforms a likable artificial agent into a distractor. (10.3389/frobt.2021.699090)
  DOI : 10.3389/frobt.2021.699090
• Receiver-Based Experimental Estimation of Power Losses in Optical Networks
  
  • May A.
  • Boitier F.
  • Awwad Elie
  • Ramantanis P.
  • Lonardi M.
  • Ciblat P.
  IEEE Photonics Technology Letters, Institute of Electrical and Electronics Engineers, 2021, 33 (22), pp.1238-1241. (10.1109/LPT.2021.3115627)
  DOI : 10.1109/LPT.2021.3115627
• Dual Polarized Self-Complementary Connected Array Antenna Concept
  
  • Lepage Anne Claire
  • Begaud Xavier
  • Varault Stefan
  • Soiron Michel
  • Barka Andre
  , 2021. In this communication, we present the results obtained within the framework of the Astrid project (DGA-ANR) SAFAS (Self-complementary Surface with Low Signature). The antenna has been designed and optimized using an analytical model of the complete structure. The antenna is able to maintain frequency bandwidth ratios of 4.6: 1 for a scanning capability of up to 60 °. These results were obtained at receiving and the aim of the article is to present the latest advances to take into account the future feeding system.
• From local hesitations to global impressions of the listener
  
  • Dinkar Tanvi
  • Biancardi Beatrice
  • Clavel Chloé
  , 2021.
• Power Allocation for Multibeam Satellite Communications with Nonlinear Impairments
  
  • Louchart Arthur
  • Ciblat Philippe
  • Poulliat Charly
  , 2021. In the context of multibeam satellite uplink communications, we derive a closed-form expression for the sum rate when nonlinearity related to the high power amplifier is taken into account. We then propose a per-user power allocation for maximizing this sum rate. We resort to Signomial Programming. We show significant performance robustness compared to the allocation done by using linear regime only.
• Constraint programming for design space exploration of dataflow applications on multi-bus architectures
  
  • Gharbi Amna
  , 2021. This thesis is part of a collaboration between Télécom Paris and Nokia Bell Labs France. In this context, we focus on the system-level Design Space Exploration of embedded systems for the execution of signal processing applications. In the system we target, the design space exploration process intends to identify the allocation and scheduling of both application tasks and data transfers between these tasks: this identification plays a key role in the overall performance (e.g. end-to-end latency) of these systems. While there are already multiple works for diverse communication architectures, this thesis focuses on multi-bus architectures that are particularly well-suited for computation platforms of signal processing applications. For these platforms, we show that only limited contributions have already been proposed. Three contributions are proposed to tackle the above mentioned problem. 1) A satisfiability modulo theories (SMT) formulation which allows to explore mapping and scheduling decisions on multi-bus architectures for latency optimization; We demonstrate its ability to produce a solution for well-known applications. Yet, 2) to mitigate the scalability limitations for the optimal solution search of this first contribution, we propose a technique to prune the design space of searched solutions. Evaluations we provide demonstrate a better scalability. Last, 3) communication allocation is enhanced with power consumption, and we show how to jointly optimize latency and power consumption. Our evaluation is again applied to a set of well-known signal processing applications and demonstrates how different trade-offs between latency and power consumption can be studied.Our contributions are integrated into a state-of-the-art modeling and verification tool for the system-level design of embedded systems (TTool). Perspectives are articulated in mainly two axes. 1) Extending the current formulation to account for new design aspects (e.g., shared memory, throughput). 2) Further improving the scalability of the optimal search.
• Caregiver development of activity-supporting services for smart homes
  
  • Belloum Rafik
  • Yaddaden Amel
  • Lussier Maxime
  • Bier Nathalie
  • Consel Charles
  JAISE - Journal of Ambient Intelligence and Smart Environments, IOS Press, 2021, pp.1-19. Older adults often need some level of assistance in performing daily living activities. Even though these activities are common to the vast majority of individuals (e.g., eating, bathing, dressing), the way they are performed varies across individuals. Supporting older people in performing their everyday activities is a major avenue of research in smart homes. However, because of its early stage, this line of work has paid little attention on customizing assistive computing support with respect to the specific needs of each older adult towards improving its effectiveness and acceptability. We propose a tool-based approach to allowing caregivers to define services in the area of home daily living, leveraging their knowledge and expertise on the older adult they care for. This approach consists of two stages: 1) a wizard allows caregivers to define an assistive service, which supports aspects of a daily activity that are specific to an older adult; 2) the wizard-generated service is uploaded in an existing smart home platform and interpreted by a dedicated component, carrying out the caregiver-defined service. Our approach has been implemented. Our wizard has been successfully used to define existing manually-programmed, activity-supporting services. The resulting services have been deployed and executed by an existing assisted living platform deployed in the home of community-dwelling individuals. They have been shown to be equivalent to their manually-programmed counterparts. We also conducted an ergonomics study involving five occupational therapists, who tested our wizard with clinical vignettes describing fictitious patients. Participants were able to successfully define services while revealing an ease of use of our wizard. (10.3233/AIS-210616)
  DOI : 10.3233/AIS-210616
• Towards a Black-Box Security Evaluation Framework
  
  • Ahmed Mosabbah Mushir
  • Souissi Youssef
  • Trabelsi Oualid
  • Guilley Sylvain
  • Bouvet Antoine
  • Takarabt Sofiane
  , 2021, 1497, pp.79-92. Injection of faults has been studied in various research works since last decades. Several hardware targets have been studied with respect to the efficiency of fault injections. In this paper we address the security evaluation of embedded systems in constrained environments called black-box analyses. This is not considered by standards of evaluation as they require conducting the analysis in the most relaxed conditions, often called white-box analysis which focuses on specific security modules provided that the finer details are available. However, black-box analysis has a much larger view by focusing on all the system as potential target. It is closer to a real world attacker. This allows measuring the impact of real attack scenarios, and therefore thinking and building the most adequate protections. We put forward a six steps evaluation methodology along with a practical use-case on a real end-user device. This shall give a better understanding and also an evaluation framework of black-box analysis. (10.1007/978-3-030-90553-8_6)
  DOI : 10.1007/978-3-030-90553-8_6
• First-Order Side-Channel Leakage Analysis of Masked but Asynchronous AES
  
  • Bouvet Antoine
  • Guilley Sylvain
  • Vlasak Lukas
  , 2021, 1497, pp.16-29. Masking schemes are classical countermeasures against Side-Channel Attacks on cryptographic implementations. This paper investigates the eectiveness of masking when the code does not run in constant time. We prove that in this case, a rst-order Correlation Power Analysis can break an otherwise perfect masking scheme. Furthermore, with an in-depth leakage analysis on traces generated at a pre-silicon stage, we pinpoint the leaking instructions and recover a complex leakage model. (10.1007/978-3-030-90553-8_2)
  DOI : 10.1007/978-3-030-90553-8_2