Publications

2025

• Automatic Classification of Software Repositories: a Systematic Mapping Study
  
  • Balla Stefano
  • Degueule Thomas
  • Robbes Romain
  • Falleri Jean-Rémy
  • Zacchiroli Stefano
  , 2025. The rapid growth of software repositories on development platforms such as GitHub, as well as archives like Software Heritage, prompts the need for better repository classification. Machine learning is increasingly used to automate this classification, but there are no secondary studies analyzing this research landscape. We present a systematic mapping study of 43 primary sources published between 2002 and 2023, where we examine the goals, inputs, outputs, training, and evaluation processes involved in automatic repository classification. Our findings reveal a growing interest in automatic classification, particularly to enhance the discoverability and recommendation of relevant repositories. Other applications, such as classification for mining studies, were surprisingly underrepresented. We also observe that a lack of standardized datasets, classification tasks, and evaluation metrics makes it difficult to compare the performance of different techniques.
• Modeling Musical Genre Trajectories through Pathlet Learning
  
  • Marey Lilian
  • Laclau Charlotte
  • Sguerra Bruno
  • Viard Tiphaine
  • Moussallam Manuel
  , 2025, pp.202-210. <div><p>The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user preferences remains a complex challenge, particularly as their musical tastes change over time. This paper uses the dictionary learning paradigm to model user trajectories across different musical genres. We define a new framework that captures recurring patterns in genre trajectories, called pathlets, enabling the creation of comprehensible trajectory embeddings. We show that pathlet learning reveals relevant listening patterns that can be analyzed both qualitatively and quantitatively. This work improves our understanding of users' interactions with music and opens up avenues of research into user behavior and fostering diversity in recommender systems. A dataset of 2000 user histories tagged by genre over 17 months, supplied by Deezer (a leading music streaming company), is also released with the code.</p></div> (10.1145/3708319.3733695)
  DOI : 10.1145/3708319.3733695
• Can AI expose tax loopholes? Towards a new generation of legal policy assistants
  
  • Fratrič Peter
  • Holzenberger Nils
  • Amariles David Restrepo
  , 2025. The legislative process is the backbone of a state built on solid institutions. Yet, due to the complexity of laws -- particularly tax law -- policies may lead to inequality and social tensions. In this study, we introduce a novel prototype system designed to address the issues of tax loopholes and tax avoidance. Our hybrid solution integrates a natural language interface with a domain-specific language tailored for planning. We demonstrate on a case study how tax loopholes and avoidance schemes can be exposed. We conclude that our prototype can help enhance social welfare by systematically identifying and addressing tax gaps stemming from loopholes.
• A Quantitative Approach to the GDPR’s Anonymization and Pseudonymization Tests
  
  • Holzenberger Nils
  • Maxwell Winston
  , 2025. This article examines two tests from the European General Data Protection Regulation (GDPR): (1) the test for full anonymisation (the "anonymisation test"), and (2) the test for applying "appropriate technical measures" to protect personal data when full anonymisation is not achieved (the "pseudonymisation test"). Both tests depend on vague legal standards and have given rise to legal disputes and differing interpretations among data protection authorities and courts, including in the context of machine learning. Under the anonymisation test, data are sufficiently anonymised when they are immune from re-identification by an attacker using "all means reasonably likely to be used". Under the pseudonymisation test, technical measures to protect personal data that are not anonymised must be "appropriate" with regard to the risks of data loss. Here, we use methods from law and economics to transform these qualitative tests into quantitative tests: we take a risk-management approach and put forward a mathematical formalization of the GDPR's criteria, to supplement existing qualitative approaches. We chart different attack efforts and re-identification probabilities, and propose this as a methodology to help stakeholders discuss whether data are sufficiently anonymised to satisfy the GDPR anonymisation test, or alternatively, whether pseudonymisation efforts are "appropriate" under the GDPR. The resulting graphs can help stakeholders decide whether the anonymisation test is fulfilled, and discuss the use of Privacy-Enhancing Technologies necessary to pass the pseudonymisation test. We apply our proposed framework to several scenarios, applying the anonymisation test to a Large Language Model, and the pseudonymisation test to a database protected with differential privacy. (10.2139/ssrn.5162461)
  DOI : 10.2139/ssrn.5162461
• A Transductive and Inductive GNNs for Physical Moving Objects Detection in Surface Scenes for Digital Twins
  
  • Prummel Wieke
  • Giraldo Jhony
  • Subudhi Badri
  • Zakharova Anastasia
  • Bouwmans Thierry
  , 2025 (1), pp.133-149. Computer vision applications using static or moving cameras are often required in digital twins generation. More specifically, the detection of moving objects is essential to provide a virtual representation of an environment in order to reflect physical moving objects accurately. To this end, background subtraction (BGS) is then applied to separate the background (BG) and the foreground (FG) from videos. Numerous publications employ mathematical, machine learning, and signal processing models to be more robust to the open challenges presented in videos. Recently, many methods using graph neural networks for BGS have been reported, with very promising outcomes. This chapter provides a survey of transductive and inductive Graph Neural Networks (GNNs) for moving objects detection (MOD) comparing their architectures. After analysis of their strategies and limitations, a comparative evaluation of the large-scale CDnet2014 dataset is provided. Finally, we conclude with some potential future research directions. (10.1201/9781003582489-10)
  DOI : 10.1201/9781003582489-10
• CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
  
  • Hou Abe Bohan
  • Weller Orion
  • Qin Guanghui
  • Yang Eugene
  • Lawrie Dawn
  • Holzenberger Nils
  • Blair-Stanek Andrew
  • van Durme Benjamin
  , 2025. Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writing such documents provide great benefits but are challenging to design. Such systems need to help locate, summarize, and reason over salient precedents in order to be useful. To enable systems for such tasks, we work with legal professionals to transform a large open-source legal corpus into a dataset supporting two important backbone tasks: information retrieval (IR) and retrieval-augmented generation (RAG). This dataset CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal. We benchmark state-of-the-art models on CLERC, showing that current approaches still struggle: GPT-4o generates analyses with the highest ROUGE F-scores but hallucinates the most, while zero-shot IR models only achieve 48.3% recall@1000.
• Categorical semantics of compositional reinforcement learning
  
  • Bakirtzis Georgios
  • Savvas Michail
  • Topcu Ufuk
  Journal of Machine Learning Research, Microtome Publishing, 2025, 26 (130), pp.1-37. Compositional knowledge representations in reinforcement learning (RL) facilitate modular, interpretable, and safe task specifications. However, generating compositional models requires the characterization of minimal assumptions for the robustness of the compositionality feature, especially in the case of functional decompositions. Using a categorical point of view, we develop a knowledge representation framework for a compositional theory of RL. Our approach relies on the theoretical study of the category MDP, whose objects are Markov decision processes (MDPs) acting as models of tasks. The categorical semantics models the compositionality of tasks through the application of pushout operations akin to combining puzzle pieces. As a practical application of these pushout operations, we introduce zig-zag diagrams that rely on the compositional guarantees engendered by the category MDP. We further prove that properties of the category MDP unify concepts, such as enforcing safety requirements and exploiting symmetries, generalizing previous abstraction theories for RL.
• From information leakage to rank statistics in side-channel attacks
  
  • Béguinot Julien
  • Rioul Olivier
  , 2025. In practical side-channel analysis, evaluators can estimate some key hypothesis rank statistics used as security indicators—e.g., arithmetic or geo- metric mean, median, α-marginal guesswork, or enumeration success rate. Yet, a direct estimation becomes time-consuming as security levels increase. We provide new bounds on these figures of merit in terms of the mutual in- formation between the secret and its side-channel leakages. These bounds provide theoretical insights on the evolution of the figures of merit in terms of noise level, computational complexity, and data complexity. Our results enable fast shortcut formulas for the certification laboratories, potentially enabling them to speed up the security evaluation process.
• Masked Vector Sampling for HQC
  
  • Spyropoulos Maxime
  • Vigilant David
  • Perion Fabrice
  • Pacalet Renaud
  • Sauvage Laurent
  , 2025, 1, pp.750-758. Anticipating the advent of large quantum computers, NIST started a worldwide competition in 2016 aiming to define the next cryptographic standards. HQC is one of these post-quantum schemes selected for standardization. In 2022, Guo et al. introduced a timing attack that exploited a weakness in HQC rejection sampling function to recover its secret key in 866,000 calls to an oracle. The authors of HQC updated its specification by applying an algorithm to sample vectors in constant time. A masked implementation of this function was later proposed for BIKE but it is not directly applicable to HQC. In this paper we propose a specification-compliant masked version of the HQC vector sampling which relies, to our knowledge, on the first masked implementation of the Barrett reduction. (10.5220/0013637400003979)
  DOI : 10.5220/0013637400003979
• Assessing Security RISC: Analyzing Flush+Fault Attack on RISC-V using gem5 Simulator
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025, Assessing Security RISC: Analyzing Flush+Fault Attack on RISC-V using gem5 Simulator. <div><p>Microarchitectural side-channel attacks exploit vulnerabilities such as cache behavior to leak sensitive data. These attacks have been extensively studied on x86 architectures but they remain less explored on RISC-V systems. A recent paper (Gerlach et al., 2023) demonstrated existing and novel microarchitectural attacks on RISC-V hardware platforms (C906, U74, C910, C908). This hardware-based analysis, while realistic, lacks the flexibility and detailed behavioral insights needed to fully understand these attacks. Simulation environments like gem5 (Lowe-Power, 2024) provide fine-grained control and diverse metrics to overcome this limitation and observe the attack in detail. In this paper, gem5 is used to explore Flush+Fault (Gerlach et al., 2023) side-channel attack on RISC-V architecture which was originally tested on RISC-V hardware. Through gem5, we analyze detailed insights of attack such as cache patterns, and timing behaviors. Our results demonstrate the gem5's potential for advancing the understanding of RISC-V microarchitectural vulnerabilities and eventually for developing effective countermeasures.</p></div>
• Assessing Security RISC: Analyzing Flush+Fault Attack on RISC-V using gem5 Simulator
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Microarchitectural timing side-channel attacks exploit variations in execution times caused by the underlying hardware to extract sensitive information. These attacks leverage architectural features like caches, branch predictors, and speculative execution. For thorough analysis, we use gem5 simulations to analyze Flush+Fault attack behavior on RISC-V.</p></div>
• Cheat-proof random numbers generated from quantum entanglement
  
  • Brown Peter
  Nature, Nature Publishing Group, 2025, 642 (8069), pp.875-876. A quantum random-number generator has been developed that uses classical cryptography to certify that its output was produced by a quantum process. NEWS AND VIEWS 11 June 2025 Cheat-proof random numbers generated from quantum entanglement A quantum random-number generator has been developed that uses classical cryptography to certify that its output was produced by a quantum process. By Peter Brown Twitter Facebook Email Random-number generators are used to pick juries, select samples for financial audits and assign participants in clinical trials to treatment or placebo groups. The unpredictability of random numbers ensures that such selections are not biased. However, for these systems to be secure and fair, we need a way to verify that a random-number generator has not been tampered with. Writing in Nature, Kavuri et al.1 report a random-number generator based on the quantum phenomenon of entanglement and underpinned by classical cryptography. Their protocol marks an important step forward, because it doesn’t require users to take the randomness of the output on trust: every part of the protocol is publicly recorded, and the randomness of the numbers produced can be verified by anyone. (10.1038/d41586-025-01451-y)
  DOI : 10.1038/d41586-025-01451-y
• Récupération d'énergie efficace à Métasurface pour applications IoT
  
  • Sharifi Raziyeh
  , 2025. Les communications sans fil et l'Internet des objets (IoT) deviennent des éléments incontournables de la vie moderne. En évitant l'utilisation de batteries et en réduisant ainsi les coûts, la taille et le poids des appareils, la récupération d'énergie ambiante représente une alternative prometteuse pour l'alimentation électrique.L'énergie ambiante peut être captée à partir de diverses sources telles que l'énergie solaire, éolienne ou les signaux radiofréquences (RF). Alors que les énergies solaire et éolienne offrent une densité de puissance élevée, elles ne sont pas toujours disponibles. À l'inverse, les signaux RF sont omniprésents, mais leur densité de puissance est relativement faible.Plusieurs solutions permettent de collecter l'énergie électromagnétique, notamment les rectennas et les métasurfaces pour la récupération d'énergie. Pour utiliser des absorbants dans les dispositifs de récupération d'énergie, le défi principal est de maximiser l'énergie collectée et ainsi de minimiser les pertes dans les diélectriques. À cet égard, les absorbants à base de métasurface, grâce à leur faible épaisseur et leurs caractéristiques d'absorption constituent des solutions prometteuses si on utilise un substrat à faibles pertes.En général, les dispositifs de récupération d'énergie basés sur des métasurfaces se présentent sous forme de structures multicouches ou planaires. L'inconvénient majeur des structures multicouches réside dans leur complexité de fabrication. Les conceptions planaires permettent de surmonter ce problème. De plus, étant donné que l'énergie RF ambiante est généralement faible, il est essentiel d'en capter un maximum en optimisant les performances du dispositif récupérateur d'énergie.Dans cette thèse, des récupérateurs d'énergie à base de métasurface compacts et efficaces sont proposés : une structure monobande fonctionnant à 2,45 GHz et une bi-bande fonctionnant à 2,45 GHz et 5,2 GHz.Dans un premier temps, les métasurfaces ont été conçues. Étant donné les faibles niveaux d'énergie RF ambiante, maximiser la puissance collectée est une priorité. Pour relever ce défi, une étape intermédiaire dans le processus de conception a été introduite afin d'améliorer l'efficacité de capture. Cette étape supplémentaire a été appliquée aux deux structures, monobande et bi-bande. Un réseau fini de 5×4 cellules est développé pour chaque structure. L'efficacité de capture des rangées centrales du réseau fini simulé atteint 90 % à 2,54 GHz pour la version monobande. Pour la conception bi-bande, l'efficacité de capture des rangées centrales est de 74 % à 2,5 GHz et de 30 % à 5,09 GHz en simulation.Dans un second temps, un circuit redresseur mono-bande à base de diodes Schottky est proposé pour être intégré à la métasurface mono-bande, afin de convertir l'énergie RF captée en courant continu. L'efficacité de redressement à 2,49 GHz pour une puissance d'entrée de -2,7 dBm atteint 58 %.Les métasurfaces mono-bande et bi-bande ainsi que le redresseur ont été analysés indépendamment. Ensuite, tous les dispositifs ont été fabriqués et mesurés afin de vérifier leurs performances.
• Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI
  
  • Bakirtzis Georgios
  • Aler Tubella Andrea
  • Theodorou Andreas
  • Danks David
  • Topcu Ufuk
  , 2025. Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static---or slower-paced---technologies, find themselves at a crossroads when faced with the fluid and evolving nature of AI systems. Moreover, typical problems in AI, for example, the frequent opacity and unpredictability of the behaviour of the systems, add additional sociotechnical challenges. To address these interconnected issues, we introduce the concept of dynamic certification, an adaptive regulatory framework specifically crafted to keep pace with the continuous evolution of AI systems. The complexity of these challenges requires common progress in multiple domains: technical, socio-governmental, and regulatory. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems, aligning them bidirectionally with the real-world contexts in which they operate. By doing so, we aim to bridge the gap between rapid technological advancement and effective regulatory oversight, ensuring that AI systems not only achieve their intended goals but also adhere to ethical standards and societal values. (10.1016/B978-0-44-340553-2.00019-8)
  DOI : 10.1016/B978-0-44-340553-2.00019-8
• Assessing the Vulnerabilities of RISC-V
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Emerging RISC-V processors require rigorous security evaluation to address microarchitectural vulnerabilities inherent in their rapidly evolving ecosystem. A recent paper [1] implemented both known and novel side-channel attacks targeting commercial RISC-V CPUs (U74 and C906). While this hardware-based research confirmed vulnerabilities, it could not provide detailed insights into attack dynamics. We bridge this gap using the gem5 simulation framework to systematically analyze side-channel attacks on RISC-V architectures. Our paper focuses on the access-retired attack, which exploits the unprivileged rdinstret instruction to infer protected filesystem data. By tracking retired instruction counts, attackers detect microarchitectural state differences caused by directory access checks. We utilize the gem5 simulator in full-system (FS) mode to capture kernel-level behaviors, allowing us to analyze critical performance metrics including instruction retirement, cache performance, and branch prediction statistics. This detailed simulation-based analysis is essential for understanding the behavior of the attack and for developing effective countermeasures. Advancing RISC-V security research with simulation tools like gem5 is thus a promising direction for mitigating future side-channel vulnerabilities.</p></div>
• Polar coordinate-based 2D pose prior with neural distance field
  
  • Gan Qi
  • Nguyen Sao Mai
  • Fenaux Eric
  • Clémençon Stéphan
  • El Yacoubi Mounim
  , 2025, pp.6144-6152. Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB videos have achieved impressive performance on public datasets, their effectiveness in real-world sports scenarios is often hindered by motion blur, occlusions, and domain shifts across different pose representations. Fine-tuning these models can partially alleviate such challenges but typically requires large-scale annotated data and still struggles to generalize across diverse sports environments. To address these limitations, we propose a 2D pose prior-guided refinement approach based on Neural Distance Fields (NDF). Unlike existing approaches that rely solely on angular representations of human poses, we introduce a polar coordinate-based representation that explicitly incorporates joint connection lengths, enabling a more accurate correction of erroneous pose estimations. Additionally, we define a novel non-geodesic distance metric that separates angular and radial discrepancies, which we demonstrate is better suited for polar representations than traditional geodesic distances. To mitigate data scarcity, we develop a gradient-based batch-projection augmentation strategy, which synthesizes realistic pose samples through iterative refinement. Our method is evaluated on a long jump dataset, demonstrating its ability to improve 2D pose estimation across multiple pose representations, making it robust across different domains. Experimental results show that our approach enhances pose plausibility while requiring only limited training data. Code is available at: https://github.com/QGAN2019/polar-NDF. (10.1109/CVPRW67362.2025.00612)
  DOI : 10.1109/CVPRW67362.2025.00612
• From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
  
  • Bouniot Quentin
  • Redko Ievgen
  • Mallasto Anton
  • Laclau Charlotte
  • Struckmeier Oliver
  • Arndt Karol
  • Heinonen Markus
  • Kyrki Ville
  • Kaski Samuel
  , 2025. In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -common factors associated with their expressive power -may exhibit a drastically different performance even when trained on the same dataset. In this paper, we introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for approximately measuring the non-linearity of deep neural networks. Built upon a score derived from closed-form optimal transport mappings, this signature provides a better understanding of the inner workings of a wide range of DNN architectures and learning paradigms, with a particular emphasis on the computer vision task. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature and its potential for long-reaching implications. The code for our work is available at https: //github.com/qbouniot/AffScoreDeep.
• Auto-Reg: A Dynamic AutoML Framework for Streaming Regression
  
  • Verma Nilesh
  • Bifet Albert
  • Pfahringer Bernhard
  • Bahri Maroua
  , 2025, 15873, pp.245-256. <div><p>Automated Machine Learning (AutoML) has transformed the development of machine learning pipelines. However, its application to data streams presents unique challenges, such as adapting to evolving data distributions and ensuring real-time performance. While substantial progress has been made in streaming classification, advancements in streaming regression remain limited. To address this gap, we propose Auto-Reg, an AutoML framework tailored specifically for data stream regression. Auto-Reg introduces two key components: a dynamic budget adjustment mechanism for efficient resource allocation and a Probability-Weighted Hyperparameter Search (PWHS) strategy that balances exploration and exploitation. Comprehensive experiments on both real-world and synthetic datasets, supported by theoretical and empirical evaluations, demonstrate that Auto-Reg consistently outperforms state-of-theart data stream regression models in terms of predictive accuracy.</p></div> (10.1007/978-981-96-8183-9_20)
  DOI : 10.1007/978-981-96-8183-9_20
• Multi-bit Quantizer Design for Distributed Parameter Estimation
  
  • Bi Yue
  • Ciblat Philippe
  • Wu Yue
  • Hua Cunqing
  , 2025. <div><p>We consider sensors deployed in diverse locations measuring a common parameter through noisy observations. These observations are quantized to be sent to a fusion center doing the estimation of the common parameter. We design these quantizers to minimize the worst-case mean square error for common parameter estimation. Relying on an asymptotic regime in terms of sensors' number and on random multi-bit quantizer per sensor, we provide a relevant continuous distribution for the thresholds of these quantizers via signomial programming. Through numerical simulations, we show that the proposed quantizers outperform the uniformly-distributed one and some deterministic ones even when the number of sensors is limited.</p></div>
• Stochastic Activation based Broadcast Push-Sum for Distributed Estimation
  
  • Bi Yue
  • Ciblat Philippe
  • Wu Yue
  • Hua Cunqing
  , 2025, pp.1-5. <div><p>Distributed estimation systems enable nodes to estimate a target parameter in a collaborative manner. These systems are useful in sensor networks or distributed machine learning. Here, we explore distributed estimation in graph-connected networks without a fusion center, where nodes exchange information with neighbors to estimate this target parameter synchronously. Due to packet collision, there is a tradeoff between the number of exchanges and the quality of these exchanges. To fix this issue, we propose to activate the nodes randomly. The main contribution of the paper is to determine an activation rate offering a good target estimation quality as fast as possible.</p></div> (10.1109/SSP64130.2025.11073203)
  DOI : 10.1109/SSP64130.2025.11073203
• InteractOR: Interacting with Images during Surgery in Mixed Reality through Real-time Instrument Segmentation
  
  • Karoui Nour
  • Bloch Isabelle
  • Avellino Ignacio
  , 2025.
• Massive parallelization of projection-based depths
  
  • Leone Leonardo
  • Mozharovskyi Pavlo
  • Bounie David
  , 2025. This article introduces a novel methodology for the massive parallelization of projection-based depths, addressing the computational challenges of data depth in high-dimensional spaces. We propose an algorithmic framework based on Refined Random Search (RRS) and demonstrate significant speedup (up to 7,000 times faster) on GPUs. Empirical results on synthetic data show improved precision and reduced runtime, making the method suitable for large-scale applications. The RRS algorithm (and other depth functions) are available in the Python-library data-depth (https://data-depth.github.io/) with ready-to-use tools to implement and to build upon this work. (10.48550/arXiv.2506.08262)
  DOI : 10.48550/arXiv.2506.08262
• Co-investment under uncertainty: coalitional game formulation and application to edge computing
  
  • Sakr Amal
  • Araldo Andrea
  • Chahed Tijani
  • Patanè Rosario
  • Kofman Daniel
  , 2025. <div><p>We focus on the deployment of large scale Edge Computing (EC) infrastructure, which requires substantial capital and operational costs. The Infrastructure Providers (InPs) are reluctant to make these investments, as the revenues from edge services, e.g., augmented reality, may be mainly captured by the respective Service Providers (SPs), and not the InPs. This is the main reason for the limited deployment of EC. We tackle this economic aspect by proposing a co-investment strategy, in which all players, i.e., one InP and multiple SPs, form a coalition to deploy an optimal amount of resources, dynamically allocate them over an investment period, and fairly share costs and revenues. The major challenge is to ensure that the coalition is stable, i.e., the co-investment is profitable for all players under uncertainty of revenues, which comes from the uncertainty of future user demand. We address this challenge with a novel stochastic coalitional game formulation, which allows us to analytically compute a lower bound on the probability that the grand coalition is stable. Numerical results show a large value for the lower bound, even with high levels of uncertainty, provided that the number of SPs is relatively small, their loads are comparable, and the investment period is long enough.</p></div>
• Performance Analysis of Network Sensing in the Distributed MIMO Radar System
  
  • Song Yi
  • Zhi Kangda
  • Yi Shuangyang
  • Ciblat Philippe
  • Caire Guiseppe
  , 2025. <div><p>This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of ´1, and with the TX/RX densities to the power of ´γ{2, where γ is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.</p></div>
• On the Average Random Probing Model
  
  • Béguinot Julien
  • Masure Loïc
  IACR Transactions on Cryptographic Hardware and Embedded Systems, IACR, 2025, 2025 (3), pp.32-55. Masking is one of the main countermeasures against side-channel analysis since it relies on provable security. In this context, “provable” means that a security bound can be exhibited for the masked implementation through a theoretical analysis in a given threat model. The main goal in this line of research is therefore to provide the tightest security bound, in the most realistic model, in the most generic way. Yet, all of these objectives cannot be reached together. That is why the masking literature has introduced a large spectrum of threat models and reductions between them, depending on the desired trade-off with respect to these three goals. In this paper, we focus on three threat models, namely the noisy-leakage model (realistic yet hard to work with), the random probing (unrealistic yet easy to work with), and more particularly a third intermediate model called average random probing. Average random probing has been introduced by Dziembowski et al. at Eurocrypt 2015, in order to exhibit a tight reduction between noisy-leakage and random probing models, recently proven by Brian et al. at Eurocrypt 2024. This milestone has strong practical consequences, since otherwise the reduction from the noisy leakage model to the random probing model introduces a prohibitively high constant factor in the security bound, preventing security evaluators to use it in practice. However, we exhibit a gap between the average random probing definitions of Dziembowski et al. (denoted hereafter by DFS-ARP) and Brian et al. (simply denoted by ARP). Whereas any noisy leakage can be tightly reduced to DFS-ARP, we show in this paper that it cannot be tightly reduced to ARP, unless requiring extra assumptions, e.g., if the noisy leakage is deterministic. Our proof techniques do not involve more tools than the one used so far in such reductions, namely basic probability facts, and known properties of the total variation distance. As a consequence, the reduction from the noisy leakage to the random probing — without high constant factor — remains unproven. This stresses the need to clarify the practical relevance of analyzing the security of masking in the random probing model since most of the current efforts towards improving the constructions and their security proofs in the random probing model might be hindered by potentially unavoidable loss in the reduction from more realistic but currently less investigated leakage models. (10.46586/tches.v2025.i3.32-55)
  DOI : 10.46586/tches.v2025.i3.32-55