Academic literature on the topic 'Code-based masking'
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Journal articles on the topic "Code-based masking":
Xiao, Yisheng, Ruiyang Xu, Lijun Wu, Juntao Li, Tao Qin, Tie-Yan Liu, and Min Zhang. "AMOM: Adaptive Masking over Masking for Conditional Masked Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13789–97. http://dx.doi.org/10.1609/aaai.v37i11.26615.
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 (December 4, 2023): 398–432. http://dx.doi.org/10.46586/tches.v2024.i1.398-432.
Levina, Alla, and Gleb Ryaskin. "Robust Code Constructions Based on Bent Functions and Spline Wavelet Decomposition." Mathematics 10, no. 18 (September 12, 2022): 3305. http://dx.doi.org/10.3390/math10183305.
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 (November 2019): 3069–82. http://dx.doi.org/10.1109/tifs.2019.2912549.
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 (March 12, 2024): 64–87. http://dx.doi.org/10.46586/tches.v2024.i2.64-87.
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 (June 26, 2023): 4792–800. http://dx.doi.org/10.1609/aaai.v37i4.25604.
Burke, Colin J., Patrick D. Aleo, Yu-Ching Chen, Xin Liu, John R. Peterson, Glenn H. Sembroski, and Joshua Yao-Yu Lin. "Deblending and classifying astronomical sources with Mask R-CNN deep learning." Monthly Notices of the Royal Astronomical Society 490, no. 3 (October 10, 2019): 3952–65. http://dx.doi.org/10.1093/mnras/stz2845.
Zhu, Fengmin, Michael Sammler, Rodolphe Lepigre, Derek Dreyer, and Deepak Garg. "BFF: foundational and automated verification of bitfield-manipulating programs." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (October 31, 2022): 1613–38. http://dx.doi.org/10.1145/3563345.
Chen, Ying, Rebekah Wu, James Felton, David M. Rocke, and Anu Chakicherla. "A Method to Detect Differential Gene Expression in Cross-Species Hybridization Experiments at Gene and Probe Level." Biomedical Informatics Insights 3 (January 2010): BII.S3846. http://dx.doi.org/10.4137/bii.s3846.
Harrington, J. Patrick. "Polarized Continuum Radiation from Stellar Atmospheres." Proceedings of the International Astronomical Union 10, S305 (December 2014): 395–400. http://dx.doi.org/10.1017/s1743921315005116.
Dissertations / Theses on the topic "Code-based masking":
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.
Cryptographic algorithms are nowadays prevalent in establishing secure connectivity in our digital society. Such computations handle sensitive information like encryption keys, which are usually very exposed during manipulation, resulting in a huge threat to the security of the sensitive information concealed in cryptographic components. In the field of embedded systems security, side-channel analysis is one of the most powerful techniques against cryptographic implementations. The main subject of this thesis is the measurable side-channel security of cryptographic implementations, particularly in the presence of random masking. Overall, this thesis consists of two topics. One is the leakage quantification of the most general form of masking equipped with the linear codes, so-called code-based masking; the other one is exploration of applying more generic information measures in a context of side-channel analysis. Two topics are inherently connected to each other in assessing and enhancing the practical security of cryptographic implementations .Regarding the former, we propose a unified coding-theoretic framework for measuring the information leakage in code-based masking. Specifically, our framework builds formal connections between coding properties and leakage metrics in side-channel analysis. Those formal connections enable us to push forward the quantitative evaluation on how the linear codes can affect the concrete security of all code-based masking schemes. Moreover, relying on our framework, we consolidate code-based masking by providing the optimal linear codes in the sense of maximizing the side-channel resistance of the corresponding masking scheme. Our framework is finally verified by attack-based evaluation, where the attacks utilize maximum-likelihood based distinguishers and are therefore optimal. Regarding the latter, we present a full spectrum of application of alpha-information, a generalization of (Shannon) mutual information, for assessing side-channel security. In this thesis, we propose to utilize a more general information-theoretic measure, namely alpha-information (alpha-information) of order alpha. The new measure also gives the upper bound on success rate and the lower bound on the number of measurements. More importantly, with proper choices of alpha, alpha-information provides very tight bounds, in particular, when alpha approaches to positive infinity, the bounds will be exact. As a matter of fact, maximum-likelihood based distinguishers will converge to the bounds. Therefore, we demonstrate how the two world, information-theoretic measures (bounds) and maximum-likelihood based side-channel attacks, are seamlessly connected in side-channel analysis .In summary, our study in this thesis pushes forward the evaluation and consolidation of side-channel security of cryptographic implementations. From a protection perspective, we provide a best-practice guideline for the application of code-based masking. From an evaluation perspective, the application of alpha-information enables practical evaluators and designers to have a more accurate (or even exact) estimation of concrete side-channel security level of their cryptographic chips
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.
Book chapters on the topic "Code-based masking":
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.
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, 384–409. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch015.
Conference papers on the topic "Code-based masking":
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.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/68.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/102.
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}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/692.