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Статті в журналах з теми "INVERSION-ENCRYPTION METHOD"

Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 31 липня 2025

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1

Khan, Sajid, Lansheng Han, Ghulam Mudassir, Bachira Guehguih, and Hidayat Ullah. "3C3R, an Image Encryption Algorithm Based on BBI, 2D-CA, and SM-DNA." Entropy 21, no. 11 (2019): 1075. http://dx.doi.org/10.3390/e21111075.

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Анотація:

Color image encryption has enticed a lot of attention in recent years. Many authors proposed a chaotic system-based encryption algorithms for that purpose. However, due to the shortcomings of the low dimensional chaotic systems, similar rule structure for RGB channels, and the small keyspace, many of those were cryptanalyzed by chosen-plaintext or other well-known attacks. A Security vulnerability exists because of the same method being applied over the RGB channels. This paper aims to introduce a new three-channel three rules (3C3R) image encryption algorithm along with two novel mathematical

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2

Cheng, Peng, and Huai Xun Zhao. "A Novel Image Encryption Algorithm Based on Cellular Automata and Chaotic System." Advanced Materials Research 998-999 (July 2014): 797–801. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.797.

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Анотація:

This paper introduces a novel image encryption scheme based on chaotic maps and toggle cellular automata (TCA). In confusion stage, the proposed scheme utilizes logistic map to construct a nonlinear sequence for scrambling the plain-image. Then in diffusion stage, TCA is constructed by setting up the inversion rule and the image which has been processed by chaotic sequence is encryption again by using the TCA iteration method. Theoretical analysis and experimental results demonstrate the proposed algorithm achieves a high security level and processes good performance against common attacks lik

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3

A., Shanooja M., and Anil Kumar M. N. "A Technique for Image Encryption Using the Modular Multiplicative Inverse Property of Mersenne Primes." Symmetry 17, no. 2 (2025): 166. https://doi.org/10.3390/sym17020166.

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Анотація:

Mersenne prime numbers, expressed in the form (2n − 1), have long captivated researchers due to their unique properties. The presented work aims to develop a symmetric cryptographic algorithm using a novel technique based on the logical properties of Mersenne primes. Existing encryption algorithms exhibit certain challenges, such as scalability and design complexity. The proposed novel modular multiplicative inverse property over Mersenne primes simplifies the encryption/decryption process. The simplification is achieved by computing the multiplicative inverse using cyclic bit shift operation.

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4

Lokesh Reddy, B., and Anith Nelleri. "Single-Pixel Compressive Digital Holographic Encryption System Based on Circular Harmonic Key and Parallel Phase Shifting Digital Holography." International Journal of Optics 2022 (July 14, 2022): 1–20. http://dx.doi.org/10.1155/2022/6298010.

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An encryption system that combines compressive sensing (CS) and two-step parallel phase shifting digital holography (PPSDH) using double random phase encoding (DRPE) is presented in this paper. The two-step PPSDH is a linear inline holographic scheme and is much suitable for encrypting the 2D/3D information in a single exposure. The distribution of random phase mask (RPM) in the DRPE is implemented using circular harmonic key which increases the security of the encryption process. In this system, the keys used to encrypt are spatial positions of the planes, wavelength, and rotation of the circ

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5

LI, Huixian, Fulei WANG, Chun SHEN, Shiyuan LIU, and Liaojun PANG. "Small interval interpolation fitting bootstrapping method based on residue number system." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 42, no. 5 (2024): 969–78. https://doi.org/10.1051/jnwpu/20244250969.

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Анотація:

Aiming at the problem that the bootstrapping time of approximate homomorphic encryption scheme is too long, a small interval interpolation fitting method based on residue system is proposed. In this paper, the sinusoidal function by using interpolating and fitting method between the multiple cells to avoid the increase in bootstrapping time or decrease in calculation accuracy caused by the high degree of fitting polynomial is calculated. And the efficiency of modular multiplication and modular inversion in the calculation process is improved by combining the residual system. Lagrange interpola

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6

Liao, Jia, Baihui Jiang, Peng Zhao, Lei Ning, and Liming Chen. "Unmanned Aerial Vehicle-Assisted Federated Learning Method Based on a Trusted Execution Environment." Electronics 12, no. 18 (2023): 3938. http://dx.doi.org/10.3390/electronics12183938.

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In the face of increasing concerns around privacy and security in the use of unmanned aerial vehicles (UAVs) for mobile edge computing (MEC), this study proposes a novel approach to secure UAV-assisted federated learning. This research integrates a trusted execution environment (TEE) into UAV-assisted federated learning and proposes a robust aggregation algorithm based on cosine distance, denoted as CosAvg. This study further designs and evaluates a TEE-based federated learning model, comparing its resource overhead with other secure aggregation frameworks, like homomorphic encryption (HE) and

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7

Sun, Zhe, Weiping Li, Junxi Liang, et al. "A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network." Mathematics 12, no. 5 (2024): 718. http://dx.doi.org/10.3390/math12050718.

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Анотація:

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In

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8

Xi, Yuzhou, Yu Ning, Jie Jin, and Fei Yu. "A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption." Mathematics 12, no. 24 (2024): 3948. https://doi.org/10.3390/math12243948.

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Анотація:

Cryptography is one of the most important branches of information security. Cryptography ensures secure communication and data privacy, and it has been increasingly applied in healthcare and related areas. As a significant cryptographic method, the Hill cipher has attracted significant attention from experts and scholars. To enhance the security of the traditional Hill cipher (THC) and expand its application in medical image encryption, a novel dynamic Hill cipher with Arnold scrambling technique (DHCAST) is proposed in this work. Unlike the THC, the proposed DHCAST uses a time-varying matrix

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9

Naseer, Yasir, Tariq Shah, Dawood Shah, and Sadam Hussain. "A Novel Algorithm of Constructing Highly Nonlinear S-p-boxes." Cryptography 3, no. 1 (2019): 6. http://dx.doi.org/10.3390/cryptography3010006.

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Анотація:

The role of substitution boxes is very important in block ciphers. Substitution boxes are utilized to create confusion in the cryptosystem. However, to create both confusion and diffusion in any cryptosystem p-boxes and chaos base substitution boxes are designed. In this work, a simple method is presented that serves both ways. This method is based on composition of the action of symmetric group on Galois field and inversion map. This construction method provides a large number of highly non-linear substitution permutation boxes having the property of confusion as well as diffusion. These subs

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10

Li, Jiakun, and Wei Gao. "Hardware Optimization and System Design of Elliptic Curve Encryption Algorithm Based on FPGA." Journal of Sensors 2022 (October 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/9074524.

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Анотація:

Since entering the era of big data, the degree of information sharing is getting higher and higher; the information exchange is becoming more and more convenient, but at the same time, personal information is also easy to be exposed to the network environment, if it is used by criminals to lead to information leakage, and then bring certain risks. Therefore, it is in the information age and do a good job of network information security and confidentiality. At present, the security and secrecy of network information are mainly realized by cryptography. Public key cryptography can encrypt inform

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11

Akinyemi, Lateef A., and Bukola H. Akinwole. "Performance Evaluation of Laguerre Transform and Neural Network-based Cryptographic Techniques for Network Security." International Journal of Intelligent Systems and Applications 16, no. 3 (2024): 1–17. http://dx.doi.org/10.5815/ijisa.2024.03.01.

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Анотація:

As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided compa

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12

Ibrahim, Atef, and Fayez Gebali. "Optimizing Security of Radio Frequency Identification Systems in Assistive Devices: A Novel Unidirectional Systolic Design for Dickson-Based Field Multiplier." Systems 13, no. 3 (2025): 154. https://doi.org/10.3390/systems13030154.

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Анотація:

The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic

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13

Aaruni, P. M., Baby Aleena, Antony Angel, Benny Binsha, and Gopinath Anjaly. "Substitution Box Optimization." Journal of Electronics and Communication Systems 4, no. 3 (2019): 21–24. https://doi.org/10.5281/zenodo.3531510.

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Анотація:

<em>The basic principle of encryption is to transform plain data into unintelligible data via a series of steps referred to as an encryption algorithm. The input to this algorithm is called plaintext, and its output encoded version is called as cipher text. Even though there are various algorithms, Advanced Encryption Standard (AES) is adopted due to the availability of optimized hardware components for efficient implementation. The S-Box is one of the critical operations in AES algorithm and it consists of two sub-modules, namely the multiplicative inversion sub-module in GF(2^8) and the Affi

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14

Kareem, Suhad Muhajer, Ayad Al-Adhami, and Abdul Monem S. Rahma. "An improvement for CAST-128 encryption based on magic square and matrix inversion." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 377–87. http://dx.doi.org/10.11591/eei.v13i1.5340.

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Анотація:

This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information entropy,

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15

Zhang, Xing, Xiaoyu Jiang, Zhaolin Jiang, and Heejung Byun. "Algorithms for solving a class of real quasi-symmetric Toeplitz linear systems and its applications." Electronic Research Archive 31, no. 4 (2023): 1966–81. http://dx.doi.org/10.3934/era.2023101.

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Анотація:

<abstract><p>In this paper, fast numerical methods for solving the real quasi-symmetric Toeplitz linear system are studied in two stages. First, based on an order-reduction algorithm and the factorization of Toeplitz matrix inversion, a sequence of linear systems with a constant symmetric Toeplitz matrix are solved. Second, two new fast algorithms are employed to solve the real quasi-symmetric Toeplitz linear system. Furthermore, we show a fast algorithm for quasi-symmetric Toeplitz matrix-vector multiplication. In addition, the stability analysis of the splitting symmetric Toeplit

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16

Ogunmolu, Akinde Michael. "Enhancing Data Security in Artificial Intelligence Systems: A Cybersecurity and Information Governance Approach." Journal of Engineering Research and Reports 27, no. 5 (2025): 154–72. https://doi.org/10.9734/jerr/2025/v27i51500.

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Анотація:

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in

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17

Guo, Pengxin, Shuang Zeng, Wenhao Chen, et al. "A New Federated Learning Framework Against Gradient Inversion Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16969–77. https://doi.org/10.1609/aaai.v39i16.33865.

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Анотація:

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility tr

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18

Mohandas, Dr R., Dr S. Veena, G. Kirubasri, I. Thusnavis Bella Mary, and Dr R. Udayakumar. "Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data." Indian Journal of Information Sources and Services 14, no. 2 (2024): 17–23. http://dx.doi.org/10.51983/ijiss-2024.14.2.03.

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Анотація:

Federated Learning (FL) is a machine learning methodology that allows remote devices to collectively train a learning system without sharing their data. FL-based methods provide enhanced secure privacy by transmitting only localized model variables, learned with local information, from dispersed devices to a centralized controller. There is a potential for a centralized server or malicious individuals to deduce or get sensitive private data by analyzing the structure and variables of regional learning networks. This study incorporates the FL process into the deep learning process of medical pr

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19

Kumar, Rahul, Chin-Shiuh Shieh, Prasun Chakrabarti, Ashok Kumar, Jhankar Moolchandani, and Raj Sinha. "Privacy-Preserving Federated Learning in Healthcare, E-Commerce, and Finance: A Taxonomy of Security Threats and Mitigation Strategies." EPJ Web of Conferences 328 (2025): 01066. https://doi.org/10.1051/epjconf/202532801066.

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Анотація:

Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating eff

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20

Ezeogu, Favour Lewechi, Chidiebube Nelson Ozioko, Ihuoma Remita Uchenna, Innocent Junior Opara, and Salvation Ifechukwude Atalor. "Securing AI-Powered Healthcare Decision Support Systems: A Comprehensive Review of Attack Vectors and Defensive Strategies." Asian Journal of Advanced Research and Reports 19, no. 6 (2025): 1–11. https://doi.org/10.9734/ajarr/2025/v19i61037.

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Анотація:

Background: Artificial intelligence (AI) is emerging as a transformative technology in healthcare, enabling the development of AI-powered clinical decision support systems (CDSS). These systems leverage large-scale data and advanced computational algorithms to assist in diagnosis, treatment planning, and patient management. However, the integration of AI into clinical practice faces critical challenges, particularly related to cybersecurity and system vulnerability. Objectives: This review aims to evaluate the security vulnerabilities of AI-powered healthcare decision support systems by identi

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21

Ghilom, Milkias, and Shahram Latifi. "The Role of Machine Learning in Advanced Biometric Systems." Electronics 13, no. 13 (2024): 2667. http://dx.doi.org/10.3390/electronics13132667.

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Анотація:

Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users’ changing biometric patterns, ML algorithms can improve accuracy and performance ov

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22

Ratan, Ram, and Arvind Yadav. "Key Independent Image Deciphering using Neighbourhood Similarity Characteristics and Divide-And-Conquer Attack." Recent Patents on Engineering 14 (July 19, 2020). http://dx.doi.org/10.2174/1872212114999200719144548.

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Анотація:

Aim: The aim of the paper is to analyse the security strength of image encryption schemes which are based on pixel rotation and inversion functions. The key independent image decryption methodologies are presented to obtain original images with intelligible contents from encrypted images using neighbourhood similarity characteristics and divide-and-conquer attack. Background: The efficiency and security strength of secure communication of sensitive data depends on the computing resources and cryptographic strength of encryption schemes. An encryption scheme is cryptographically strong if it do

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23

Wu, Ximei, Chen Zhong, Bofeng Long, Tongzhe Liu, Chenchen He, and Lujie Wang. "Chaotic image encryption algorithm based on dynamic Hachimoji DNA coding and computing." Physica Scripta, March 11, 2024. http://dx.doi.org/10.1088/1402-4896/ad3245.

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Анотація:

Abstract With the increasing awareness of privacy protection, people pay more and more attention to strengthening the security of image data transmitted over the network. Therefore, this paper designs a chaotic image encrypting algorithm based on dynamic Hachimoji DNA coding and computing to protect images. The Hachimoji DNA coding method provides richer coding rules to dynamically encode images than the traditional DNA coding method, improving the complexity and security of the encryption algorithm. First, the original image is rearranged and encoded with the dynamic Hachimoji DNA coding meth

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24

Amudha, P., and J. Jayapriya. "An Application for Pair Sum Modulo Labeling in Cryptography." Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 41, no. 1 (2025). https://doi.org/10.23967/j.rimni.2025.10.63629.

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Анотація:

In the dynamic realm of cybersecurity, it is important to create a strong cryptographic technique to protect sensitive data against advanced threats. This paper introduces an innovative encryption and decryption technique leveraging graph theory and matrix algebra, especially through the use of pair sum modulo (PSM) labeling of graphs, adjacency matrices, and self-invertible matrices. The PSM labeling for a simple undirected graph G(VG, EG)with |VG| = p and |EG| = q, is an 1-1 map FG:VG → {±1,±2,...,±p}, and there is an induced edge labeling bijective function gG:EG → {0,1,2,...,(q − 1)} such

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25

Jhansi Rani Ganapa, Poonam Joshi, T Amitha, et al. "Security and Privacy Challenges in Deep Learning Models Hosted on Cloud Platforms." International Journal of Computational and Experimental Science and Engineering 11, no. 3 (2025). https://doi.org/10.22399/ijcesen.3235.

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Анотація:

Deep learning's fast integration into cloud computing services enables businesses to perform scalable AI model training and real-time analysis in diverse sectors. The combination of deep learning with cloud platforms results in important security vulnerabilities that stem from adversarial threats and data breaches as well as model inversion vulnerabilities and unauthorized system intrusions. Data infringement combined with weakened model reliability and non-compliance requirements require cloud AI systems to adopt more robust security controls. Experts analyze security issues facing deep learn

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26

Li, Zhenqiang, Fei Gao, Sujuan Qin, and Qiaoyan Wen. "New record in the number of qubits for a quantum implementation of AES." Frontiers in Physics 11 (March 24, 2023). http://dx.doi.org/10.3389/fphy.2023.1171753.

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Анотація:

Optimizing the quantum circuit for implementing Advanced Encryption Standard (AES) is crucial for estimating the necessary resources in attacking AES by the Grover algorithm. Previous studies have reduced the number of qubits required for the quantum circuits of AES-128/-192/-256 from 984/1112/1336 to 270/334/398, which is close to the optimal value of 256/320/384. It becomes a challenging task to further optimize them. AimTaking aim at this task, we find a method for how the quantum circuit of AES S-box can be designed with the help of the automation tool LIGHTER-R. Particularly, the multipli

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27

БЕЛОВ, С. А. "Метод построения полинома одной переменной над конечным полем раундовой функции блочных шифров". Международный научный журнал "Современные информационные технологии и ИТ-образование" 14, № 3 (2018). https://doi.org/10.25559/sitito.14.201803.586-593.

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Анотація:

В работе предлагается метод построения раундовой функции в виде полинома одной переменной над конечным полем. Предложенный метод основан на вычислении исходного криптографического преобразования в специальных точках конечного поля и последующем обращении матрицы Вандермонда. Для этого класса матриц существуют алгоритмы вычисления обратной матрицы, которые значительно эффективнее стандартного алгоритма обращения с помощью метода Гаусса. В работе был использован алгоритм Трауба, вычислительная сложность которого пропорциональна квадрату размера заданной матрицы. Метод применим для блочных итерат

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28

Petrean, Diana-Elena, and Rodica Potolea. "Random forest evaluation using multi-key homomorphic encryption and lookup tables." International Journal of Information Security, March 14, 2024. http://dx.doi.org/10.1007/s10207-024-00823-1.

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Анотація:

AbstractIn recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent

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29

Sielaff, Lennard, Ruben Hetfleisch, and Michael Rader. "Evaluation Framework for the Use of Privacy Preserving Technologies for Production Data." Proceedings of the International Conference on Advanced Technologies, August 19, 2023. http://dx.doi.org/10.58190/icat.2023.33.

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Анотація:

To prevent unplanned machine downtime in production, machine conditions can be monitored and even predicted using condition and failure models based on current machine and process data. As most of these models are data-intensive, machine users often do not have enough data to develop these models themselves and want to collaborate with other companies. Since these models often require critical and classified machine and process data, which could be extracted from the models using attacks such as model inversion, sharing existing models between companies is not an option as it leaves one party

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30

Shadab, Mohd, Md Saquib Jawed, and Mohammad Sajid. "Substitution Box Construction Using Transfer‐Function Assisted Metaheuristic and Booster Algorithm: A Hybrid Approach." SECURITY AND PRIVACY, September 26, 2024. http://dx.doi.org/10.1002/spy2.462.

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Анотація:

ABSTRACTS‐box strengthens the encryption and decryption process by introducing nonlinearity and protecting the encrypted data against various differential and linear cryptanalytic attacks. Generating a highly nonlinear S‐box with maximal nonlinearity is computationally impractical due to the expansive search space, classifying it as an NP‐hard problem. This paper proposes the Hawkboost algorithm, a novel hybrid method merging the Harris Hawks optimization algorithm (HHO) and the Booster algorithm for generating highly nonlinear S‐boxes with low computational efforts. The HHO algorithm is utili

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31

Klamti, Jean, M. Hasan, and Koray Karabina. "Efficient Methods for Simultaneous Homomorphic Inversion." IACR Communications in Cryptology 2, no. 1 (2025). https://doi.org/10.62056/abe0iv7sf.

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Анотація:

Efficient implementation of some privacy-preserving algorithms and applications rely on efficient implementation of homomorphic inversion. For example, a recently proposed homomorphic image filtering algorithm and the privacy-preserving body mass index (BMI) calculations repetitively use homomorphic inversion. In this paper, inspired by Montgomery's trick to perform simultaneous plaintext inversion, we tackle the simultaneous homomorphic inversion problem to compute s inverses simultaneously over ciphertexts. The advantage of Montgomery's trick for plaintext arithmetic is well-known. We first

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32

Alaverdyan, Y. Ts, and E. G. Satimova. "Fully Homomorphic Cipher Based on Finite Algebraic Structures." Earthline Journal of Mathematical Sciences, January 18, 2019, 97–103. http://dx.doi.org/10.34198/ejms.1119.97103.

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Анотація:

A way to increase the robustness of a cryptographic algorithm toward unauthorized inversion can be obtained through application of non-commutative or non-associative algebraic structures. In this regard, data security became a great issue in adaptation of cloud computing over Internet. While in the traditional encryption methods, security to data in storage state and transmission state is provided, in cloud data processing state, decryption of data is assumed, data being available to cloud provider. In this paper, we propose a special homomorphism between self-distributed and non-associative a

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33

Suhad, Muhajer Kareem, Al-Adhami Ayad, and Monem S. Rahma Abdul. "An improvement for CAST-128 encryption based on magic square and matrix inversion." Bulletin of Electrical Engineering and Informatics (BEEI) 13, no. 1 (2024). https://doi.org/10.11591/eei.v13i1.5340.

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Анотація:

This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information en

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34

Pasunuru, Sreekanth, and Anil Kumar Malipeddi. "Securing AI Models: Cryptographic Approaches to Protect AI Algorithms and Data." Journal of Artificial Intelligence & Cloud Computing, August 31, 2024, 1–3. https://doi.org/10.47363/jaicc/2024(3)e247.

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Анотація:

This paper addresses the growing necessity of protecting AI models and their underlying data using cryptographic techniques. As AI continues to integrate into critical industries such as healthcare, finance, and autonomous systems, unique vulnerabilities arise that expose models to threats like data poisoning, adversarial attacks, and model inversion. By applying cryptographic methods like homomorphic encryption, differential privacy, and secure multiparty computation, organizations can safeguard both the integrity and confidentiality of AI models and training data. This white paper provides i

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35

KHOWAJA, SUNDER A., PARUS KHUWAJA, KAPAL DEV, et al. "Block Encryption LAyer (BELA): Zero-Trust Defense Against Model Inversion Attacks for Federated Learning in 5G/6G Systems." January 6, 2025. https://doi.org/10.1109/OJCOMS.2025.3526768.

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Анотація:

Federated Learning (FL) paradigm has been very popular in the implementation of 5Gand beyond communication systems as it provides necessary security for the users in terms of data.However, the FL paradigm is still vulnerable to model inversion attacks, which allow malicious attackersto reconstruct data by using the trained model gradients. Such attacks can be carried out using generativeadversarial networks (GANs), generative models, or by backtracking the model gradients. A zero-trustmechanism involves securing access and interactions with model gradients under the principle of “nevertr

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36

Khowaja, Sunder A., Parus Khuwaja, Kapal Dev, et al. "Block Encryption LAyer (BELA): Zero-Trust Defense Against Model Inversion Attacks for Federated Learning in 5G/6G Systems." January 6, 2025. https://doi.org/10.1109/OJCOMS.2024.011100.

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Анотація:

Federated Learning (FL) paradigm has been very popular in the implementation of 5G and beyond communication systems as it provides necessary security for the users in terms of data. However, the FL paradigm is still vulnerable to model inversion attacks, which allow malicious attackers to reconstruct data by using the trained model gradients. Such attacks can be carried out using generative adversarial networks (GANs), generative models, or by backtracking the model gradients. A zero-trust mechanism involves securing access and interactions with model gradients under the principle of “ne

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37

Chen, Fan, Lingfeng Qu, Hadi Amirpour, Christian Timmerer, and Hongjie He. "Counterfeiting Attacks on a RDH-EI scheme based on block-permutation and Co-XOR." ACM Transactions on Multimedia Computing, Communications, and Applications, February 25, 2025. https://doi.org/10.1145/3719294.

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Анотація:

Reversible data hiding in encrypted images (RDH-EI) has gained widespread attention due to its potential applications in secure cloud storage. However, the security challenges of RDH-EI in cloud storage scenarios remain largely unexplored. In this paper, we present a counterfeiting attack on RDH-EI schemes that utilize block-permutation and Co-XOR (BPCX) encryption. We demonstrate that ciphertext images generated by BPCX-based RDH-EI are easily tampered with to produce a counterfeit decrypted image with different contents imperceptible to the human eye. This vulnerability is mainly because the

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