Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026

Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

Akash Shah¹, Mudasir Ahmad Wani^2,*, Ravi Prakash Chaturvedi³, Shri Kant³, Nidhi Sindhwani¹, Kashish Ara Shakil⁴, Sulieman Alshuhri²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082713 - 23 July 2026

Abstract Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction More >

Lei Wang¹, Hongji Luo², Yong Heng², Jingnan Tang², Xiaochuan Ju², Jianwei An^1,*, Haitao Xu¹, Xianwei Zhou¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082418 - 15 June 2026

Abstract In intelligent connected vehicles (ICVs) system, driving users connect to service providers (SPs) to obtain location-based services (LBS). Users transmit large volumes of encrypted sensitive information related to their itineraries to SPs to access value-added services. Attackers may launch chosen-ciphertext attacks (CCA) against SPs by exploiting the malleability of homomorphic encryption. This enables adversaries to infer or steal private key information, thereby threatening the long-term privacy of user data. Furthermore, existing key management technologies in ICVs system predominantly rely on passive defense strategies and suffer from limitations such as single protection mechanisms, delayed updates, and More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

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 >