Ibtisam A. Taqi^*, Sarab M. Hameed

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1653-1674, 2025, DOI:10.32604/cmc.2025.067524 - 29 August 2025

Abstract The exponential growth of audio data shared over the internet and communication channels has raised significant concerns about the security and privacy of transmitted information. Due to high processing requirements, traditional encryption algorithms demand considerable computational effort for real-time audio encryption. To address these challenges, this paper presents a permutation for secure audio encryption using a combination of Tent and 1D logistic maps. The audio data is first shuffled using Tent map for the random permutation. The high random secret key with a length equal to the size of the audio data is then generated… More >

Ahmed I. Alutaibi^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 875-903, 2025, DOI:10.32604/cmc.2025.067078 - 29 August 2025

Abstract The rapid evolution of quantum computing poses significant threats to traditional cryptographic schemes, particularly in Decentralized Finance (DeFi) systems that rely on legacy mechanisms like RSA and ECDSA for digital identity verification. This paper proposes a quantum-resilient, blockchain-based identity verification framework designed to address critical challenges in privacy preservation, scalability, and post-quantum security. The proposed model integrates Post-quantum Cryptography (PQC), specifically lattice-based cryptographic primitives, with Decentralized Identifiers (DIDs) and Zero-knowledge Proofs (ZKPs) to ensure verifiability, anonymity, and resistance to quantum attacks. A dual-layer architecture is introduced, comprising an identity layer for credential generation and validation,… More >

Lingling Wu¹, Xu An Wang^1,2,*, Jiasen Liu¹, Yunxuan Su¹, Zheng Tu¹, Wenhao Liu¹, Haibo Lei¹, Dianhua Tang³, Yunfei Cao³, Jianping Zhang³

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 89-119, 2025, DOI:10.32604/cmc.2025.064346 - 29 August 2025

Abstract Due to the rapid advancement of information technology, data has emerged as the core resource driving decision-making and innovation across all industries. As the foundation of artificial intelligence, machine learning(ML) has expanded its applications into intelligent recommendation systems, autonomous driving, medical diagnosis, and financial risk assessment. However, it relies on massive datasets, which contain sensitive personal information. Consequently, Privacy-Preserving Machine Learning (PPML) has become a critical research direction. To address the challenges of efficiency and accuracy in encrypted data computation within PPML, Homomorphic Encryption (HE) technology is a crucial solution, owing to its capability to… More >

Michael Juma Ayuma¹, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka^2,*

Journal on Artificial Intelligence, Vol.7, pp. 205-254, 2025, DOI:10.32604/jai.2025.067361 - 19 August 2025

Abstract The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges, particularly in securing token-based communication protocols used for authentication and authorization. This survey systematically reviews the vulnerabilities in token transmission within IoT environments, focusing on various sophisticated attack vectors such as replay attacks, token hijacking, man-in-the-middle (MITM) attacks, token injection, and eavesdropping among others. These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth, JSON Web Tokens (JWT), and bearer tokens, which are widely used in IoT ecosystems for managing device interactions and access control. The impact of such attacks… More >

Alexej Schelle^1,2,*

Journal of Information Hiding and Privacy Protection, Vol.7, pp. 33-44, 2025, DOI:10.32604/jihpp.2025.067510 - 31 July 2025

Abstract This work presents an encryption model based on Generative Adversarial Networks (GANs). Encryption of RTF-8 data is realized by dynamically generating decimal numbers that lead to the encryption and decryption of alphabetic strings in integer representation by simple addition rules, the modulus of the dimension of the considered alphabet. The binary numbers for the private dynamic keys correspond to the binary numbers of public reference keys, as defined by a specific GAN configuration. For reversible encryption with a bijective mapping between dynamic and reference keys, as defined by the GAN encryptor, secure text encryption can… More >

Gang Liu¹, Guosheng Xu^1,*, Chenyu Wang¹, Guoai Xu²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4851-4868, 2025, DOI:10.32604/cmc.2025.066331 - 30 July 2025

Abstract In wireless communication transmission, image encryption plays a key role in protecting data privacy against unauthorized access. However, conventional encryption methods often face challenges in key space security, particularly when relying on chaotic sequences, which may exhibit vulnerabilities to brute-force and predictability-based attacks. To address the limitations, this paper presents a robust and efficient encryption scheme that combines iterative hyper-chaotic systems and Convolutional Neural Networks (CNNs). Firstly, a novel two-dimensional iterative hyper-chaotic system is proposed because of its complex dynamic behavior and expanded parameter space, which can enhance the key space complexity and randomness, ensuring… More >

Chenxin Lin¹, Qun Zhou¹, Zhan Wang^2,*, Ximing Fan², Yaochang Xu², Yijia Xu²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5859-5877, 2025, DOI:10.32604/cmc.2025.065496 - 30 July 2025

Abstract False Data Injection Attack (FDIA), a disruptive cyber threat, is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems, leading to system unreliability, data integrity loss and operational vulnerability exposure. Given its widespread harm and impact, conducting in-depth research on FDIA detection is vitally important. This paper innovatively introduces a FDIA detection scheme: A Protected Federated Deep Learning (ProFed), which leverages Federated Averaging algorithm (FedAvg) as a foundational framework to fortify data security, harnesses pre-trained enhanced spatial-temporal graph neural networks (STGNN) to perform localized model training and More >

Rim Amdouni^1,2,*, Mahdi Madani³, Mohamed Ali Hajjaji^1,4, El Bay Bourennane³, Mohamed Atri⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5527-5553, 2025, DOI:10.32604/cmc.2025.065356 - 30 July 2025

Abstract Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector. In the context of security, this paper proposes a novel encryption algorithm that integrates Blockchain technology, aiming to improve the security and privacy of transmitted data. The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps: The logistic Map, the Tent Map, and the Henon Map used to generate three encryption keys. The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.… More >

Peizheng Lai¹, Minqing Zhang^1,2,*, Yixin Tang¹, Ya Yue¹, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >

Dain Lee^1,#, Hojune Shin^1,#, Jihyeon Choi¹, Younho Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3673-3705, 2025, DOI:10.32604/cmc.2025.064161 - 03 July 2025

Abstract In this study, we investigated privacy-preserving ID3 Decision Tree (PPID3) training and inference based on fully homomorphic encryption (FHE), which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree. We propose HEaaN-ID3, a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song (CKKS) scheme. HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption, which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed. To enhance computational efficiency, we adopt a… More >