Publications

2025

• NickPay, an Auditable, Privacy-Preserving, Nickname-Based Payment System
  
  • Quispe Guillaume
  • Jouvelot Pierre
  • Memmi Gerard
  , 2025. In this paper, we describe the motivation, design, security properties, and a prototype implementation of NickPay, a new privacy-preserving yet auditable payment system built on top of the Ethereum blockchain platform. NickPay offers a strong level of privacy to participants and prevents successive payment transfers from being linked to their actual owners. It is providing the transparency that blockchains ensure and at the same time, preserving the possibility for a trusted authority to access sensitive information, e.g., for audit purposes or compliance with financial regulations. NickPay builds upon the Nicknames for Group Signatures (NGS) scheme, a new signing system based on dynamic ``nicknames'' for signers that extends the schemes of group signatures and signatures with flexible public keys. NGS enables identified group members to expose their flexible public keys, thus allowing direct and natural applications such as auditable private payment systems, NickPay being a blockchain-based prototype of these. (10.1109/ICBC64466.2025.11114708)
  DOI : 10.1109/ICBC64466.2025.11114708
• EU Digital Technologies and Policy Conference (EUDTP 2025) Abstracts and Contributions
  
  • Cordero-Fuertes Juan-Antonio
  • Alam Mehwish
  • Blazy Olivier
  • Alombert Anne
  • Díaz-Rodríguez Natalia
  • Curelariu Teodora
  • Ashok Pratiksha
  • Ciuhu Calina
  • de Luca Stefano
  • Feijóo Claudio
  • Gaubiene Neringa
  • Ghaddar Bissan
  • Giglietto Fabio
  • Gomà Rafael
  • González-Fuster Gloria
  • Grumulaitis Arturas
  • Guintchev Petia
  • Jacob Romain
  • Janciute Laima
  • Kalogeiton Vicky
  • Kariniotakis Georges
  • Knaster Juan
  • Koch Luise
  • Kreer Philipp
  • Krüger Kim
  • Leblanc-Albarel Diane
  • Manner Jukka
  • Mcstay Andrew
  • Ortiz de Zúñiga María
  • Nivaggioli Patrice
  • Popovic Ivanka
  • Ramos Simona
  • Roth Markus
  • Spangenberg Jochen
  • Cripps Christopher
  , 2025.
• Une approche unifiée des activités de conception système et conception d’architecture pour intégrer la cybersécurité au tout début des phases de conception
  
  • Cincilla Pierpaolo
  • Guitton-Ouhamou Patricia
  • Guillot Bertrand
  • Barki Amira
  • Mangé Jean-Baptiste
  • Apvrille Ludovic
  • Chevalier Pascal
  MISC - Multi-System & Internet Security Cookbook, Diamond Connect, 2025, Hors-série Numéro 32 (32), pp.https://connect.ed-diamond.com/misc/mischs-032/vers-une-integration-harmonisee-des-activites-cybersecurite-dans-l-ingenierie-systeme.
• Large Language Models as Search Engines: Societal Challenges
  
  • Sadeddine Zacchary
  • Maxwell Winston
  • Varoquaux Gaël
  • Suchanek Fabian M.
  Sigir Forum, Association for Computing Machinery (ACM), 2025, 59 (1), pp.1-35. Large Language Models (LLMs) may one day replace search engines as the primary portal to information on the Web. In this article, we investigate the societal challenges that such a change could bring. We focus on the roles of LLM Providers, Content Creators, and End Users, and identify 15 types of challenges. With each, we show current mitigation strategies -both from the technical perspective and the legal perspective. We also discuss the impact of each challenge and point out future research opportunities. Large Language Models (LLMs) are increasingly used as portals to information on the Web. Google is rolling out AI overviews above its search results 1 building upon its language models 2 , Microsoft's Bing search engine 3 allows sending the query to Microsoft's Co-pilot, DuckDuckGo 4 and Brave Search 5 offer AI-assisted answers, and browsers such as Opera, Brave, and Edge have built-in AI-plugins for query answering. These developments are changing the way users access information: instead of querying the Web with a search engine, reading one or several result pages, and finding the information, people can now ask their question to the AI assistant, which will synthesize an answer for the user from Web sources. This means that LLMs have the potential to severely disrupt the search engine ecosystem, which has been comparatively stable for the last 25 years, and to completely change the way the Web is used. (10.1145/3769733.376974)
  DOI : 10.1145/3769733.376974
• A Novel Mixture Model for Characterizing Human Aiming Performance Data
  
  • Li Yanxi
  • Young Derek S
  • Rioul Olivier
  • Gori Julien
  Statistical Modelling, SAGE Publications, 2025, 25 (3), pp.236-254. Fitts’ law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered ‘in the wild’) typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movements data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. In this article, we propose a novel model with a two-component mixture structure—one Gaussian and one exponential—on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed for estimation of such a model and some properties of the algorithm are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed in this work through extensive simulations and an insightful analysis of a human aiming performance study. (10.1177/1471082X241234139)
  DOI : 10.1177/1471082X241234139
• Character recognition in Byzantine seals with deep neural networks
  
  • Rageau Théophile
  • Likforman-Sulem Laurence
  • Fiandrotti Attilio
  • Eyharabide Victoria
  • Caseau Béatrice
  • Cheynet Jean-Claude
  Digital Applications in Archaeology and Cultural Heritage, Elsevier, 2025, 37, pp.e00403-1:e00403-11. Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt towards automatic reading of inscribed text on Byzantine seal images. Byzantine seals are generally decorated with iconography on the obverse side and Greek text on the reverse side. Text may include the sender's name, position in the Byzantine aristocracy, and elements of prayers. Both text and iconography are precious literary sources that wait to be exploited electronically, so the development of computerized systems for interpreting seals images is of paramount importance. This work's contribution is hence a deep, two-stages, character reading pipeline for transcribing Byzantine seal images. A first deep convolutional neural network (CNN) detects characters in the seal (character localization). A second convolutional network reads the localized characters (character classification). Finally, a diplomatic transcription of the seal is provided by post-processing the two network outputs. We provide an experimental evaluation of each CNN in isolation and both CNNs in combination. All performances are evaluated by cross-validation. Character localization achieves a mean average precision (mAP) greater than 0.9 at the intersection of union threshold of 0.5. Classification of characters achieves an accuracy greater than 0.92. Such performance compares favorably to similar tasks such as the recognition of inscribed characters on ancient coins. At transcription level, we provide novel performance results in terms of Character Error Rate. This is novel for seal images and differs from results on isolated character recognition. (10.1016/j.daach.2025.e00403)
  DOI : 10.1016/j.daach.2025.e00403
• Exposing Go Hidden Bugs: A Novel Concolic Framework
  
  • Gorna Karolina
  • Iooss Nicolas
  • Seurin Yannick
  • Khatoun Rida
  , 2025. The widespread adoption of the Go programming language in infrastructure backends and blockchain projects has heightened the need for improved security measures. Established techniques such as unit testing, static analysis, and program fuzzing provide foundational protection mechanisms. Although symbolic execution tools have made significant contributions, opportunities remain to address the complexities of Go's runtime and concurrency model. In this work, we present Zorya, a novel methodology leveraging concrete and symbolic (concolic) execution to evaluate Go programs comprehensively. By systematically exploring execution paths to uncover vulnerabilities beyond conventional testing, symbolic execution offers distinct advantages, and coupling it with concrete execution mitigates the path explosion problem. Our solution employs Ghidra's P-Code as an intermediate representation (IR). This implementation detects runtime panics in the TinyGo compiler and supports both generic and custom invariants. Furthermore, P-Code's generic IR nature enables analysis of programs written in other languages such as C. Future enhancements may include intelligent classification of concolic execution logs to identify vulnerability patterns.
• Deliverable D2.1: Study of Timing Anomalies Documented in the Literature
  
  • Brandner Florian
  • Asăvoae Mihail
  • Bechennec Jean-Luc
  • Carle Thomas
  • Cassé Hugues
  • Faucou Sébastien
  • Rieg Lionel
  , 2025, pp.1-32. In this report, we will study a phenomenon that may have a considerable impact on the computation of the WCET of real-time tasks: Timing Anomalies (timing anomalys (TAs)). These phenomena may make WCET analysis much harder, or even impossible. Even worse they also threaten the validity of schedulability tests, which often manipulate WCET values under the hypothesis that no TAs may occur. In the following we will provide a brief introduction to relevant aspects to understand TAs, notably architecture, WCET analysis, and an intuitive definition of TAs. The remaining sections of the report detail related work on the subject of TAs, with a specific focus on formal definitions of the phenomenon.
• Efficient 5G Resource Block Scheduling Using Action Branching and Transformer Networks
  
  • Nérondat Sylvain
  • Leturc Xavier
  • Ciblat Philippe
  • Le Martret Christophe
  , 2025, pp.1-6. <div><p>This paper presents a deep reinforcement learningbased scheduling solution tailored for 5G networks. The proposed neural network architecture, utilizing an encoder-only transformer and action branching, is designed to handle large action spaces for resource block allocation in wireless environments. By training on variable number of user equipment scenarios, the solution generalizes well across different configurations. Experimental results in Nokia's wireless suite environment demonstrate superior performance in packet loss, compared to heuristics.</p></div> (10.1109/ICMLCN64995.2025.11140453)
  DOI : 10.1109/ICMLCN64995.2025.11140453
• Rate of Convergence in the Functional Central Limit Theorem for Stable Processes
  
  • Coutin Laure
  • Decreusefond Laurent
  • Huang Lorick
  Potential Analysis, Springer Verlag, 2025. In this article, we quantify the functional convergence of the rescaled random walk with heavy tails to a stable process. This generalizes the Generalized Central Limit Theorem for stable random variables in finite dimension. We show that provided we have a control between the random walk or the limiting stable process and their respective affine interpolation, we can lift the rate of convergence obtained for multivariate distributions to a rate of convergence in some functional spaces. (10.1007/s11118-025-10215-2)
  DOI : 10.1007/s11118-025-10215-2
• Railway track monitoring using distributed acoustic sensing (DAS) with standard telecom cable
  
  • Chedid Alex
  • Kabalan Ali
  • Hammi Tarik
  • Garbini Gabriel Papaiz
  • Gabet Renaud
  , 2025, 13639, pp.348. We demonstrate the ability to detect ground vibrations in a railway environment using two Distributed Acoustic Sensing (DAS) configurations. The study employs the standard deviation of the differential phase over time (STDv) as a metric to evaluate the detection capabilities and spatiotemporal localization accuracy of both systems. A demonstration of rail train tracking is presented using a standard optical fiber telecom cable sheathed PEHD, with a detection range extending up to 40 km. (10.1117/12.3062236)
  DOI : 10.1117/12.3062236
• 6G FR3 Band-limited Based DPD using Low-Resolution Σ∆ Feedback Receiver
  
  • Zeng Haoyang
  • Ghonaim Ahmed
  • Pham Dang-Kièn Germain
  • Vasilevski Michel
  • Mohellebi Reda
  • Aboushady Hassan
  • Jabbour Chadi
  , 2025, pp.1-5. This paper presents the integration of a band-limited memory polynomial (BL-MP) digital pre-distortion (DPD) model with low-resolution Σ∆-based feedback receivers, specifically targeting 6G FR3 carrier aggregation using a 400 MHz OFDM 64QAM signal. The performance is evaluated using two power amplifiers (PAs)—Doherty and Class AB—with distinct nonlinearity profiles. The study compares the Generalized Memory Polynomial (GMP) model with the BL-MP model. Significant improvements in error vector magnitude (EVM), approximately 0.7 dBm at the 3% threshold, are observed for both PAs relative to the GMP-LS model. Additionally, adjacent channel leakage ratio (ACLR) enhancements of around 7 dB are achieved, further surpassing GMP-LS performance. These findings demonstrate the adaptability and effectiveness of the BL-MP model, delivering substantial performance gains over conventional pre-distortion techniques across varied PA architectures.The results highlight the potential of employing a BL-MP DPD with a low-resolution feedback receiver, offering an optimal solution for DPD applications in 6G FR3. (10.1109/ISCAS56072.2025.11043783)
  DOI : 10.1109/ISCAS56072.2025.11043783
• The Smoothed Duality Gap as a Stopping Criterion
  
  • Walwil Iyad
  • Fercoq Olivier
  Mathematical Programming Computation, Springer, 2025. We optimize the running time of the primal-dual algorithms by optimizing their stopping criteria for solving convex optimization problems under affine equality constraints, which means terminating the algorithm earlier with fewer iterations. We study the relations between four stopping criteria and show under which conditions they are accurate to detect optimal solutions. The uncomputable one: "Optimality gap and Feasibility error", and the computable ones: the "Karush-Kuhn-Tucker error", the "Projected Duality Gap", and the "Smoothed Duality Gap". Assuming metric sub-regularity or quadratic error bound, we establish that all of the computable criteria provide practical upper bounds for the optimality gap, and approximate it effectively. Furthermore, we establish comparability between some of the computable criteria under certain conditions. Numerical experiments on basis pursuit, and quadratic programs with(out) non-negative weights corroborate these findings and show the superior stability of the smoothed duality gap over the rest. (10.1007/s12532-025-00284-0)
  DOI : 10.1007/s12532-025-00284-0
• Efficient adaptation of deep neural networks for semantic segmentation in space applications
  
  • Olivi Leonardo
  • Santero Mormile Edoardo
  • Tartaglione Enzo
  Scientific Reports, Nature Publishing Group, 2025, 15 (1), pp.18046 (1-14). In recent years, the application of Deep Learning techniques has shown remarkable success in various computer vision tasks, paving the way for their deployment in extraterrestrial exploration. Transfer learning has emerged as a powerful strategy for addressing the scarcity of labeled data in these novel environments. This paper represents one of the first efforts in evaluating the feasibility of employing adapters toward efficient transfer learning for rock segmentation in extraterrestrial landscapes, mainly focusing on lunar and martian terrains. Our work suggests that the use of adapters, strategically integrated into a pre-trained backbone model, can be successful in reducing both bandwidth and memory requirements for the target extraterrestrial device. In this study, we considered two memory-saving strategies: layer fusion (to reduce to zero the inference overhead) and an “adapter ranking” (to also reduce the transmission cost). Finally, we evaluate these results in terms of task performance, memory, and computation on embedded devices, evidencing trade-offs that open the road to more research in the field. The code will be open-sourced upon acceptance of the article. (10.1038/s41598-025-99192-5)
  DOI : 10.1038/s41598-025-99192-5
• Two Means to an End Goal": Connecting Explainability and Contestability in the Regulation of Public Sector AI
  
  • Schmude Timothée
  • Yurrita Mireia
  • Alfrink Kars
  • Le Goff Thomas
  • Viard Tiphaine
  , 2025. Explainability and its emerging counterpart contestability have become important normative and design principles for the trustworthy use of AI as they enable users and subjects to understand and challenge AI decisions. However, the regulation of AI systems spans technical, legal, and organizational dimensions, producing a multiplicity in meaning that complicates the implementation of explainability and contestability due to the difficulty of defining them. Resolving this conceptual ambiguity requires specifying and comparing the meaning of both principles across regulation dimensions, disciplines, and actors. This process, here defined as translation, is essential to provide guidance on the principles' realization. To this end, we present the findings of a semi-structured interview study with 14 interdisciplinary AI regulation experts. We report on the experts' understanding of the intersection between explainability and contestability in public AI regulation, their advice for a decision subject and a public agency in a welfare allocation AI use case, and their perspectives on the connections and gaps within the research landscape. We provide differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We further outline three main translation processes pertaining to the alignment of top-down and bottom-up regulation, the assignment of responsibility for interpreting regulations, and the establishment of interdisciplinary collaboration. Our contributions include an empirically grounded conceptualization of the intersection between explainability and contestability and recommendations on implementing these principles in public institutions. We believe our contributions can inform policy-making and regulation of these core principles and enable more effective and equitable design, development, and deployment of trustworthy public AI systems.
• Towards a More Reliable and Efficient Development Workflow: A framework for Transforming Textual Requirements into Formal Models
  
  • Touam Mohamed-Achraf
  • Ameur-Boulifa Rabéa
  • Patricia Guitton-Ouhamou
  , 2025. In the development of software-driven vehicles, ensuring safety and reliability requires rigorous validation and verification (V\&V). Early-stage verification, starting from textual requirements, is crucial for detecting errors early. However, transforming textual requirements into models for simulation and formal verification is often time-consuming and error-prone. This paper proposes a structured methodology to improve this workflow, enabling the extraction, refinement, and transformation of textual requirements into formal models. This methodology also integrates automation techniques, including large language models (LLMs) for preprocessing. The effectiveness of this method is demonstrated on a real-world automotive use case.
• A gem5-Based Framework for RISC-V Security Analysis
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>1] cheng2024,Evict+Spec+Time: Exploiting Out-of-Order Execution to Improve Cache Attacks Enhanced variant of Evict+Time attack combining eviction, speculation, and timing originally tested for x86. We tested it on RISC-V architecture.</p></div>
• Annealed Winner-Takes-All for Motion Forecasting
  
  • Xu Yihong
  • Letzelter Victor
  • Chen Mickaël
  • Zablocki Éloi
  • Cord Matthieu
  , 2025. In autonomous driving, motion prediction aims at forecasting the future trajectories of nearby agents, helping the ego vehicle to anticipate behaviors and drive safely. A key challenge is generating a diverse set of future predictions, commonly addressed using data-driven models with Multiple Choice Learning (MCL) architectures and Winner-Takes-All (WTA) training objectives. However, these methods face initialization sensitivity and training instabilities. Additionally, to compensate for limited performance, some approaches rely on training with a large set of hypotheses, requiring a post-selection step during inference to significantly reduce the number of predictions. To tackle these issues, we take inspiration from annealed MCL, a recently introduced technique that improves the convergence properties of MCL methods through an annealed Winner-Takes-All loss (aWTA). In this paper, we demonstrate how the aWTA loss can be integrated with state-of-the-art motion forecasting models to enhance their performance using only a minimal set of hypotheses, eliminating the need for the cumbersome post-selection step. Our approach can be easily incorporated into any trajectory prediction model normally trained using WTA and yields significant improvements. To facilitate the application of our approach to future motion forecasting models, the code is made publicly available: https://github.com/valeoai/MF_aWTA.
• From trustworthy AI to technical standards - The distinctive European approach to artificial intelligence regulation
  
  • Gornet Mélanie
  , 2025. Europe has been at the forefront of Artificial Intelligence (AI) ethics, developing non-binding charters and principles on "trustworthy'' AI. The term "trustworthiness'' is used by Europe to designate AI systems that are "ethical'', "legal'' and "technically robust''. Europe has supplemented these non-binding principles with a binding regulation on AI, known as the AI Act. The AI Act is one of the world's first comprehensive frameworks for regulating AI systems across different industries and use cases, focusing on safety and protection of fundamental rights. The AI Act relies, for operational questions, mostly on technical standards that are in the course of development. The European approach thus combines three layers of regulatory instruments: AI ethics charters, the AI Act and technical standards.The standardisation approach is traditional in product safety, but under the AI Act, standards are also expected to address fundamental rights concerns. To avoid making hard normative choices, standardisation organisations are playing it safe, developing standards which remain at a high-level. Moreover, under the AI Act, the responsibility for developing technical standards is delegated to private standardisation bodies, where large multinational companies are over-represented and hold significant influence. These standards are also often locked behind paywalls, although the situation may evolve in the coming years after a recent case law from the Court of Justice of the European Union. Standardisation experts therefore face pressures to deliver standards on time and of good quality. (10.70675/0cb526b5z9f0cz491ezbf98z1427db363cbb)
  DOI : 10.70675/0cb526b5z9f0cz491ezbf98z1427db363cbb
• Equivariant Denoisers for Image Restoration
  
  • Renaud Marien
  • Leclaire Arthur
  • Papadakis Nicolas
  , 2025, pp.227 - 240. One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and stochastic optimization. We analyze the convergence of this algorithm and discuss its practical benefit. (10.1007/978-3-031-92366-1_18)
  DOI : 10.1007/978-3-031-92366-1_18
• Generation of frequency entanglement with an effective quantum dot-waveguide two-photon quadratic interaction
  
  • Meguebel Mohamed
  • Federico Maxime
  • Felicetti Simone
  • Belabas Nadia
  • Fabre Nicolas
  , 2025. Light–matter interactions with quantum dots have been extensively studied to harness key quantum properties of photons, such as indistinguishability and entanglement. In this theoretical work, we exploit the atomic-like four-level structure of a quantum dot coupled to a waveguide to model a shaping frequency entangling gate (ShaFrEnGa) for single photons. Our approach is based on the identification of input frequencies and an atomic level structure for which frequency-dependent one-photon transitions are adiabatically eliminated, while frequency-dependent two-photon transitions are resonantly enhanced. The frequency entanglement performance of the gate are analyzed using a Schmidt decomposition for continuous variables, revealing a trade-off between entanglement generation efficiency and entanglement quality. We further demonstrate the use of the ShaFrEnGa for the generation of entangled frequency qudit states.
• Computer vision-based foot contact detection for long jump using a monocular normal-speed camera
  
  • Fang Yangtao
  • Gan Qi
  • Nguyen Sao Mai
  , 2025.
• Correct-by-construction requirement decomposition
  
  • Sun Minghui
  • Bakirtzis Georgios
  • Jafarzadeh Hassan
  • Fleming Cody
  Software and Systems Modeling, Springer Verlag, 2025, pp.1-16. In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down methodologies for effectively breaking down complex requirements into precise, actionable sub-requirements are scarce, especially compared to the wealth of bottom-up verification techniques. Addressing this gap, we introduce a formal decomposition for contract-based design that guarantees the correctness of decomposed requirements if specific conditions are met. Our (semi-)automated methodology augments contract-based design with reachability analysis and constraint programming to systematically identify, verify, and validate sub-requirements representable by continuous bounded sets---continuous relations between real-valued inputs and outputs. We demonstrate the efficacy and practicality of a correct-by-construction approach through a comprehensive case study on a cruise control system, highlighting how our methodology improves the interpretability, tractability, and verifiability of system requirements. (10.1007/s10270-025-01291-4)
  DOI : 10.1007/s10270-025-01291-4
• Interactive Sketch-based Modeling of Braided Hair
  
  • Jetti Hari Hara Gowtham
  • Parakkat Amal Dev
  , 2025, pp.1-2. Hair braids are widely used in various games and animated movies, thanks to their simplified representation and ease of animation. However, the existing research on modeling braids often relies on a limited dictionary of commonly seen hair braid patterns, constraining artists' ability to experiment by creating imaginary or creative hair braids. In this paper, we introduce a simple sketch-based interface for creating arbitrary hair braids. Our method employs a two-stage framework that first interprets a user-drawn sketch to extract the braid pattern. To accommodate arbitrarily drawn sketches, we then use a physics-inspired simulation to generate visually pleasing braids. In addition to automatically generating braids, our system allows users to interactively refine the braid pattern to create braids that match the user's imagination, facilitating experimentation and exploration of different braid structures. (10.2312/egp.20251027)
  DOI : 10.2312/egp.20251027
• Multi-client Functional Encryption with Public Inputs and Strong Security
  
  • Nguyen Ky
  • Phan Duong Hieu
  • Pointcheval David
  , 2025, 15676, pp.68-101. Recent years have witnessed a significant development for functional encryption (FE) in the multi-user setting, particularly with multi-client functional encryption (MCFE). The challenge becomes more important when combined with access control, such as attribute-based encryption (ABE), which was actually not covered syntactically by the public-key FE nor semantically by the secret-key MCFE frameworks. On the other hand, as for complex primitives, many works have studied the admissibility of adversaries to ensure that the security model encompasses all real threats of attacks. 1. At a conceptual level, by adding a public input to FE/MCFE, we cover many previous primitives, notably attribute-based function classes. Furthermore, with the strongest admissibility for inner-product functionality, our framework is quite versatile, as it encrypts multiple sub-vectors, allows repetitions and corruptions, and eventually also encompasses public-key FE and classical ABE, bridging the private setting of MCFE with the public setting of FE and ABE. 2. Finally, we propose an MCFE with public inputs with the class of functions that combines inner-products (on private inputs) and attribute-based access-control (on public inputs) for LSSS policies. We achieve the first AB-MCFE for inner products with strong admissibility (from Nguyen et al., ACNS’23) and with adaptive security. In the end, our concrete MCFE leads to MIFE for inner products, public-key single-input inner- product FE with LSSS key-policy, and KP-ABE for LSSS, with adaptive security. Previous AB-MCFE constructions are either restricted in terms of weaker admissibility (Nguyen et al., ASIACRYPT’22) or considers a slightly larger functionality of attribute-weighted sum but with only selective security (Agrawal et al., CRYPTO’23). (10.1007/978-3-031-91826-1_3)
  DOI : 10.1007/978-3-031-91826-1_3