Relevant bibliographies by topics / Code-based masking

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Code-based masking'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Code-based masking"

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Tian, Qi, Hao Cheng, Chun Guo, Daniel Page, Meiqin Wang, and Weija Wang. "A Code-Based ISE to Protect Boolean Masking in Software." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 2 (2025): 293–332. https://doi.org/10.46586/tches.v2025.i2.293-332.

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Side-Channel Attacks (SCAs) pose a significant threat to data security in embedded environments. To counteract the power-based SCAs, masking is a widely used defense technique, that introduces randomness to obscure the sidechannel information generated during the processing of secret data. However, in practice, some challenges exist when implementing masking schemes. For example, in the implementation of Boolean masking, they may refer to low noise level and implementation flaws. To address the said implementation challenges, we present an effective and efficient solution that incorporates the
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Gaspoz, John, and Siemen Dhooghe. "Code-based Masking: From Fields to Bits Bitsliced Higher-Order Masked SKINNY." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 3 (2025): 212–37. https://doi.org/10.46586/tches.v2025.i3.212-237.

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Masking is one of the most prevalent and investigated countermeasures against side-channel analysis. As an alternative to the simple (e.g., additive) encoding function of Boolean masking, a collection of more algebraically complex masking types has emerged. Recently, inner product masking and the more generic codebased masking have proven to enable higher theoretical security properties than Boolean masking. In CARDIS 2017, Poussier et al. connected this “security order amplification” effect to the bit-probing model, demonstrating that for the same shared size, sharings from more complex encod
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Xiao, Yisheng, Ruiyang Xu, Lijun Wu, et al. "AMOM: Adaptive Masking over Masking for Conditional Masked Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13789–97. http://dx.doi.org/10.1609/aaai.v37i11.26615.

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Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To speed up the inference stage, many non-autoregressive (NAR) strategies have been proposed in the past few years. Among them, the conditional masked language model (CMLM) is one of the most versatile frameworks, as it can support many different sequence generation scenarios and achieve very competitive performance on these tasks. In this paper, we furt
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Carlet, Claude, Abderrahman Daif, Sylvain Guilley, and Cédric Tavernier. "Quasi-linear masking against SCA and FIA, with cost amortization." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 1 (2023): 398–432. http://dx.doi.org/10.46586/tches.v2024.i1.398-432.

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The implementation of cryptographic algorithms must be protected against physical attacks. Side-channel and fault injection analyses are two prominent such implementation-level attacks. Protections against either do exist. Against sidechannel attacks, they are characterized by SNI security orders: the higher the order, the more difficult the attack.In this paper, we leverage fast discrete Fourier transform to reduce the complexity of high-order masking. The security paradigm is that of code-based masking. Coding theory is amenable both to mask material at a prescribed order, by mixing the info
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Usman, Usman Bukar, Kabir Umar, and Aliyu Isah Agaie. "CodeELECTRA: An ELECTRA-based approach for improved vulnerability detection in blockchain smart contracts." Dutse Journal of Pure and Applied Sciences 10, no. 3b (2024): 95–105. http://dx.doi.org/10.4314/dujopas.v10i3b.11.

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Blockchain technology has gained significant traction due to its core features of immutability, transparency, and decentralization. Smart contracts, self-executing programs stored on blockchains, play a vital role in enabling secure and automated transactions. Secure and automated transactions are made possible by self-executing programs and smart contracts that are kept on blockchains. The rapid progress of blockchain technology has been linked to an increase in security concerns targeting smart contracts. In comparison to traditional approaches, deep learning and transformer-based approaches
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Levina, Alla, and Gleb Ryaskin. "Robust Code Constructions Based on Bent Functions and Spline Wavelet Decomposition." Mathematics 10, no. 18 (2022): 3305. http://dx.doi.org/10.3390/math10183305.

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The paper investigates new robust code constructions based on bent functions and spline–wavelet transformation. Implementation of bent functions in code construction increases the probability of error detection in the data channel and cryptographic devices. Meanwhile, the use of spline wavelet theory for constructing the codes gives the possibility to increase system security from the actions of an attacker. Presented constructions combine spline-wavelets functions and bent functions. Developed robust codes, compared to existing ones, have a higher parameter of the maximum error masking probab
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Mehboob, R., S. Shabbir, and A. Javed. "A Technique of Code Clone Detection based on Defined Mechanism for Threshold Calculation." Nucleus 54, no. 4 (2018): 197–204. https://doi.org/10.71330/thenucleus.2017.258.

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Over the past few years the revolution in the technology and use of programming languages for product development has made code reusability a common practice. Consequently the problem of code cloning is also increasing leading to redundancy and increased maintenance cost. The real motivation of the proposed research work is to identify code clones from pair of codes that are going to be utilized for a project under consideration. Existing practices such as control flow graphs (CFGs) and abstract syntax tree (AST) promote a high level of abstraction by masking the inner details of the code. The
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Wang, Weijia, Yu Yu, and Francois-Xavier Standaert. "Provable Order Amplification for Code-Based Masking: How to Avoid Non-Linear Leakages Due to Masked Operations." IEEE Transactions on Information Forensics and Security 14, no. 11 (2019): 3069–82. http://dx.doi.org/10.1109/tifs.2019.2912549.

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Goy, Guillaume, Julien Maillard, Philippe Gaborit, and Antoine Loiseau. "Single trace HQC shared key recovery with SASCA." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 2 (2024): 64–87. http://dx.doi.org/10.46586/tches.v2024.i2.64-87.

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This paper presents practicable single trace attacks against the Hamming Quasi-Cyclic (HQC) Key Encapsulation Mechanism. These attacks are the first Soft Analytical Side-Channel Attacks (SASCA) against code-based cryptography. We mount SASCA based on Belief Propagation (BP) on several steps of HQC’s decapsulation process. Firstly, we target the Reed-Solomon (RS) decoder involved in the HQC publicly known code. We perform simulated attacks under Hamming weight leakage model, and reach excellent accuracies (superior to 0.9) up to a high noise level (σ = 3), thanks to a re-decoding strategy. In a
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Yao, Xincheng, Chongyang Zhang, Ruoqi Li, Jun Sun, and Zhenyu Liu. "One-for-All: Proposal Masked Cross-Class Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4792–800. http://dx.doi.org/10.1609/aaai.v37i4.25604.

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One of the most challenges for anomaly detection (AD) is how to learn one unified and generalizable model to adapt to multi-class especially cross-class settings: the model is trained with normal samples from seen classes with the objective to detect anomalies from both seen and unseen classes. In this work, we propose a novel Proposal Masked Anomaly Detection (PMAD) approach for such challenging multi- and cross-class anomaly detection. The proposed PMAD can be adapted to seen and unseen classes by two key designs: MAE-based patch-level reconstruction and prototype-guided proposal masking. Fi
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Dissertations / Theses on the topic "Code-based masking"

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Cheng, Wei. "What can information guess ? : Towards information leakage quantification in side-channel analysis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT044.

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Les algorithmes cryptographiques jouent un rôle prédominant pour établir une connectivité sécurisée dans notre société numérique actuelle. Ces calculs traitent des informations sensibles telles que des clés de chiffrement, qui sont généralement très exposées lors de la manipulation, ce qui représente une menace énorme pour la sécurité des informations sensibles dans les composants cryptographiques et l'ensemble des systèmes connectés. Dans le domaine de la sécurité des systèmes embarqués, l'analyse des canaux auxiliaires est l'une des techniques les plus puissantes contre les implémentations c
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Yang, Jheng Jhe, and 楊政哲. "Constructions of QC-LDPC Code Based on Masking and PDF." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/af2wa6.

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Book chapters on the topic "Code-based masking"

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Norga, Quinten, Suparna Kundu, Uttam Kumar Ojha, Anindya Ganguly, Angshuman Karmakar, and Ingrid Verbauwhede. "Masking Gaussian Elimination at Arbitrary Order with Application to Multivariate-and Code-Based PQC." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88661-4_11.

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Khurana, Mridul, Arka Daw, M. Maruf, et al. "Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73024-5_9.

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AbstractA central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical E
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Jarcă, Andrei, Florinel-Alin Croitoru, and Radu Tudor Ionescu. "CBM: Curriculum by Masking." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240503.

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We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning strategy that effectively creates an easy-to-hard training schedule via patch (token) masking, offering significant accuracy improvements over the conventional training regime and previous curriculum learning (CL) methods. CBM leverages gradient magnitudes to prioritize the masking of salient image regions via a novel masking algorithm and a novel masking block. Our approach enables controlling sample difficulty via the patch masking ratio, generating an effective easy-to-hard curriculum by gradually introduci
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Sim, Minho, Young-Jun Lee, Dongkun Lee, Jongwhoa Lee, and Ho-Jin Choi. "A Simple Debiasing Framework for Out-of-Distribution Detection in Human Action Recognition." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230511.

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In real-world scenarios, detecting out-of-distribution (OOD) action is important when deploying a deep learning-based human action recognition (HAR) model. However, HAR models are easily biased to static information in the video (e.g., background), which can lead to performance degradation of OOD detection methods. In this paper, we propose a simple debiasing framework for out-of-distribution detection in human action recognition. Specifically, our framework eliminates patches with static bias in video using attention maps extracted from the video vision transformer model. Experimental results
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Xu, Gejing. "The Application and Evaluation of AI-Assisted Automatic Code Generation in Programming Instruction." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250366.

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In order to solve the problem of low accuracy and robustness of existing automatic code generation methods, AI-assisted automatic code generation in programming teaching is proposed for application and evaluation. The mechanism consists of 2 parts: node screening strategy and de-redundant generation strategy. The node filtering strategy introduces masking variables based on syntactic information to filter invalid nodes, thus reducing the learning cost of complex syntax for pointer networks. The de-redundancy generation strategy dynamically adjusts the node probability based on the time window,
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Patel, Advait, and Pravin Pandey. "Safeguarding Sensitive Data in the DevSecOps Pipelines." In Data Governance, DevSecOps, and Advancements in Modern Software. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0365-9.ch011.

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This chapter focuses on ways, practices, and resources available to protect data in DevSecOps processes, an essential element of application development. It focuses on software security in the Software Development Life Cycle (SDLC) context, including secure code, data-covering techniques like masking and encryption, and role-based access. Innovative examples of appropriate organizations like Netflix or Capital One show the necessity of performing preventive management of data breaches. Another interesting trend the chapter discusses is zero trust and AI in cybersecurity. Thus, improving DevSec
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Santos, Ricardo Jorge, Jorge Bernardino, and Marco Vieira. "Using Data Masking for Balancing Security and Performance in Data Warehousing." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch015.

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Data Warehouses (DWs) are the core of sensitive business information, which makes them an appealing target. Encryption solutions are accepted as the best way to ensure strong security in data confidentiality while keeping high database performance. However, this work shows that they introduce massive storage space and performance overheads to a magnitude that makes them unfeasible for DWs. This work proposes a data masking technique for protecting sensitive business data in DWs which balances security strength with database performance, using a Formula based on the mathematical modular operato
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Conference papers on the topic "Code-based masking"

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Reefat, Hasin Ishraq, Hossein Pourmehrani, Wei Cheng, et al. "CBM-TI: Code-Based Masking against Glitches by Hybridization with Threshold Implementation." In 2025 IEEE 43rd VLSI Test Symposium (VTS). IEEE, 2025. https://doi.org/10.1109/vts65138.2025.11022813.

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Zhang, Jiahe, Duo Li, Jun Jia, Wenjie Sun, and Guangtao Zhai. "Protection and Hiding Algorithm of QR Code Based on Multi-channel Visual Masking." In 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019. http://dx.doi.org/10.1109/vcip47243.2019.8966044.

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Chen, Yinda, Wei Huang, Shenglong Zhou, Qi Chen, and Zhiwei Xiong. "Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/68.

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The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron microscopy (EM) data. By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, man
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Niu, Runliang, Zhepei Wei, Yan Wang, and Qi Wang. "AttExplainer: Explain Transformer via Attention by Reinforcement Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/102.

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Transformer and its variants, built based on attention mechanisms, have recently achieved remarkable performance in many NLP tasks. Most existing works on Transformer explanation tend to reveal and utilize the attention matrix with human subjective intuitions in a qualitative manner. However, the huge size of dimensions directly challenges these methods to quantitatively analyze the attention matrix. Therefore, in this paper, we propose a novel reinforcement learning (RL) based framework for Transformer explanation via attention matrix, namely AttExplainer. The RL agent learns to perform step-
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Zhou, Min, Chenchen Xu, Ye Ma, Tiezheng Ge, Yuning Jiang, and Weiwei Xu. "Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/692.

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In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking d
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