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 >

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 >

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 >

Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073883 - 10 February 2026

Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Ray-I Chang¹, Chia-Hui Wang^2,*, Yen-Ting Chang¹, Lien-Chen Wei²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 503-518, 2025, DOI:10.32604/cmc.2025.068516 - 29 August 2025

Abstract As data analysis often incurs significant communication and computational costs, these tasks are increasingly outsourced to cloud computing platforms. However, this introduces privacy concerns, as sensitive data must be transmitted to and processed by untrusted parties. To address this, fully homomorphic encryption (FHE) has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service (MLaaS), enabling computation on encrypted data without revealing the plaintext. Nevertheless, FHE remains computationally expensive. As a result, approximate homomorphic encryption (AHE) schemes, such as CKKS, have attracted attention due to their efficiency. In our previous work, we proposed RP-OKC, a CKKS-based clustering… 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 >

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 >

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 >