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 >

Arshiya Sajid Ansari^1,*, Ghadir Altuwaijri², Fahad Alodhyani¹, Moulay Ibrahim El-Khalil Ghembaza³, Shahabas Manakunnath Devasam Paramb³, Mohammad Sajid Mohammadi³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3713-3752, 2025, DOI:10.32604/cmc.2025.064027 - 19 May 2025

Abstract IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing… More >

Sulaiman Al Amro^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4623-4658, 2025, DOI:10.32604/cmc.2025.063734 - 19 May 2025

Abstract The rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy.… More >

Junjie Cao^1,2, Zhiyong Yu^2,*, Xiaotao Xu¹, Baohong Zhu³, Jian Yang²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5235-5257, 2025, DOI:10.32604/cmc.2025.062371 - 19 May 2025

Abstract This paper introduces a quantum-enhanced edge computing framework that synergizes quantum-inspired algorithms with advanced machine learning techniques to optimize real-time task offloading in edge computing environments. This innovative approach not only significantly improves the system’s real-time responsiveness and resource utilization efficiency but also addresses critical challenges in Internet of Things (IoT) ecosystems—such as high demand variability, resource allocation uncertainties, and data privacy concerns—through practical solutions. Initially, the framework employs an adaptive adjustment mechanism to dynamically manage task and resource states, complemented by online learning models for precise predictive analytics. Secondly, it accelerates the search for… More >

Muhammad Saad, Ki-Woong Park^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1881-1900, 2025, DOI:10.32604/cmc.2025.061733 - 16 April 2025

Abstract The proliferation of Internet of Things (IoT) devices introduces substantial security challenges. Currently, privacy constitutes a significant concern for individuals. While maintaining privacy within these systems is an essential characteristic, it often necessitates certain compromises, such as complexity and scalability, thereby complicating management efforts. The principal challenge lies in ensuring confidentiality while simultaneously preserving individuals’ anonymity within the system. To address this, we present our proposed architecture for managing IoT devices using blockchain technology. Our proposed architecture works on and off blockchain and is integrated with dashcams and closed-circuit television (CCTV) security cameras. In this… More >

Aishwarya Rajasekar^*, Vetriselvi Vetrian

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 225-254, 2025, DOI:10.32604/cmes.2025.062642 - 11 April 2025

Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… More >

Hyunwoo Park^1,2, Jaedong Lee^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 827-847, 2025, DOI:10.32604/cmes.2025.061178 - 11 April 2025

Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >

Hao Li^1,#, Kuan Shao^1,#, Xin Wang², Mufeng Wang³, Zhenyong Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718 - 06 March 2025

Abstract Due to the development of cloud computing and machine learning, users can upload their data to the cloud for machine learning model training. However, dishonest clouds may infer user data, resulting in user data leakage. Previous schemes have achieved secure outsourced computing, but they suffer from low computational accuracy, difficult-to-handle heterogeneous distribution of data from multiple sources, and high computational cost, which result in extremely poor user experience and expensive cloud computing costs. To address the above problems, we propose a multi-precision, multi-sourced, and multi-key outsourcing neural network training scheme. Firstly, we design a multi-precision More >

Yonghang Yan¹, Xin Xie¹, Hengyi Ren², Ying Cao^1,*, Hongwei Chang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5035-5055, 2025, DOI:10.32604/cmc.2025.058276 - 06 March 2025

Abstract Fingerprint features, as unique and stable biometric identifiers, are crucial for identity verification. However, traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks, potentially leading to user data leakage. Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data, effectively addressing privacy and security concerns. However, variations in fingerprint data due to factors such as region, ethnicity, sensor quality, and environmental conditions result in significant heterogeneity across clients. This heterogeneity adversely impacts the generalization ability of the global model, limiting its performance across… More >