Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079018 - 08 May 2026

Abstract In today’s digitally connected world, where cyber threats are becoming increasingly complex, finding modern and secure text encryption solutions that maintain maximum runtime performance while offering high-level protection is more crucial. The deployment of sophisticated security paradigms is often accompanied by a significant escalation in computational overhead. Thus, the fundamental objective resides in the mitigation of computational overhead while maintaining an uncompromising security posture. Internet of Things (IoT) devices require strong security measures for data transmission. Also, protecting communication channels against illegal access and eavesdropping has become crucial due to the exponential expansion of the… More >

Tao Bai¹, Yang Tang², Kuan Shao³, Zhenyong Zhang^3,*, Yuanteng Liu⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078473 - 08 May 2026

Abstract In recent years, the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service (MLaaS). Users can upload their requirements through front-end applications, and the server provides model inference services after receiving the user input. However, MLaaS may lead to serious privacy breaches. Large language model services are typical representatives of MLaaS, and the Transformer is a typical structure in large language models. Therefore, this paper proposes a privacy-protected Transformer inference scheme based on the CKKS fully homomorphic encryption scheme to optimize computational and communication efficiency. Firstly, this paper… More >

Walaa Alayed¹, Asad Ur Rehman², M.Awais Ehsan³, Waqar Ul Hassan⁴, Ahmed Zeeshan^5,6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078078 - 08 May 2026

Abstract The rapid growth in the field of data and cloud computing has made it essential to ensure information security. Encryption consists of multiple layers, among which a critical component is the Substitution box (S-box). The S-box provides nonlinearity and confusion between the original and cipher forms, and its performance directly determines the security of the cipher against cryptanalysis. Chaotic systems have been widely used for image encryption, however, they suffer from well known limitations such as deterministic periodicity and reduced unpredictability in finite field digital environments. To address these issues, we propose a new S-box… More >

Tianhua Lin¹, Sijiang Xie^1,*, Yalong Yan², Jianguo Xie², Ang Liu²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075573 - 08 May 2026

Abstract Quantum key distribution (QKD) provides unconditional security but relies on repeaters to extend coverage, thereby introducing repeater trust risks—compromised repeaters may leak keys. Brakerski/Fan-Vercauteren scheme (BFV)-based QKD addresses this issue through key encryption and quantum attack resistance. However, Fast Fully Homomorphic Encryption over the Torus (TFHE) outperforms BFV in encryption/decryption speed for single-qubit homomorphic XOR operations, which is critical for the real-time requirements of QKD. We propose TFHE-based QKD (TQKD), a quantum key distribution protocol based on public-key TFHE. During key forwarding, it leverages the “usable-but-unobservable” property of homomorphic encryption to prevent key exposure. A… More >

Zhu Fang^1,2,*, Zhengquan Xu^1,2, Weizhen He³, Bohao Xu³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076043 - 09 April 2026

Abstract In view of the problem that the IP address jump law is easy to predict in the current mobile target defense, this paper proposes a network address jump active defense method based on a dynamic random graph, designed to improve the unpredictability of IP address translation. Firstly, in order to make IP address transformation unpredictable in space and time, a random graph model is designed to generate a pseudo-random sequence of IP address randomization; these pseudo-random can meet the unpredictability of IP address translation in both space and time. Then, based on these pseudo-random sequences… More >

Yu-Ran Jeon¹, Seung-Ha Jee¹, Su-Kyoung Kim¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077784 - 30 March 2026

Abstract As cyberattacks become increasingly sophisticated and intelligent, demand for machine-learning-based anomaly detection systems is growing. However, conventional systems generally assume a trusted server environment, where traffic data is collected and analyzed in plaintext. This assumption introduces inherent privacy risks, as privacy-sensitive information may be exposed if the server is compromised or misused. To address this limitation, privacy-preserving anomaly detection approaches have been actively studied, enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data. While these approaches offer strong confidentiality guarantees, they suffer from significant drawbacks, including substantial computational overhead, high… More >

Michael Juma Ayuma^1,*, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka²

Journal on Internet of Things, Vol.8, pp. 31-65, 2026, DOI:10.32604/jiot.2026.074919 - 16 March 2026

Abstract Encryption is essential for safeguarding sensitive data by transforming it into a secret code, which can only be decrypted by authorized parties. This ensures privacy and protects data from unauthorized access. While various encryption algorithms exist, relying on a single method may not provide sufficient security, particularly in the context of token transmission. Common threats such as brute force attacks, man-in-the-middle (MITM) attacks, token modification, and replay attacks are prevalent in adversarial attempts to breach the security of tokens during transmission. When these vulnerabilities are not addressed, they can compromise token integrity and the security… More >

Salma Akther¹, Wencheng Yang^1,*, Song Wang², Shicheng Wei¹, Ji Zhang¹, Xu Yang³, Yanrong Lu⁴, Yan Li¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076358 - 12 March 2026

Abstract As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are effective for traditional networks and applications but lack the capabilities to address DL-specific threats, such as model inversion, membership inference, and adversarial attacks. This review provides a comprehensive analysis of penetration testing for the privacy of DL models, examining the shortfalls of existing frameworks, tools, and testing methodologies. Through systematic evaluation of existing literature and empirical analysis, we identify three major contributions: (i) a critical… More >

Somia A. Abd El-Mottaleb¹, Amira G. Mohamed², Mehtab Singh³, Hassan Yousif Ahmed⁴, Medien Zeghid⁴, Abu Sufian A. Osman^5,*, Sami Mourou⁵

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075669 - 12 March 2026

Abstract This paper presents an image encryption scheme for underwater optical wireless communication (UOWC) systems based on dynamically generated hyperchaotic S-boxes, aiming to enhance both data security and transmission performance in underwater environments. The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics. Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8 (7.9925–7.9993), with very low correlation coefficients in horizontal, vertical, and diagonal directions, as well as the system achieves high values in key… More >

Chia-Hui Liu¹, Zhen-Yu Wu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075495 - 12 March 2026

Abstract Federated Learning (FL) enables collaborative medical model training without sharing sensitive patient data. However, existing FL systems face increasing security risks from post quantum adversaries and often incur non-negligible computational and communication overhead when encryption is applied. At the same time, training high performance AI models requires large volumes of high quality data, while medical data such as patient information, clinical records, and diagnostic reports are highly sensitive and subject to strict privacy regulations, including HIPAA and GDPR. Traditional centralized machine learning approaches therefore pose significant challenges for cross institutional collaboration in healthcare. To address… More >