Enas W. Abood¹, Ali A. Yassin^2,*, Zaid Ameen Abduljabbar^2,3,4,*, Vincent Omollo Nyangaresi^5,6, Iman Qays Abduljaleel², Abdulla J. Y. Aldarwish², Husam A. Neamah^7,8

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2305-2345, 2025, DOI:10.32604/cmc.2025.066579 - 23 September 2025

Abstract The digital revolution era has impacted various domains, including healthcare, where digital technology enables access to and control of medical information, remote patient monitoring, and enhanced clinical support based on the Internet of Health Things (IoHTs). However, data privacy and security, data management, and scalability present challenges to widespread adoption. This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT, highlighting their critical roles in ensuring secure data exchange and patient privacy. This includes various authentication technologies and strategies, such as biometric and multi-factor authentication, as well as the influence More >

Ning Tang^1,2, Wang Luo^1,2,*, Yiwei Wang^1,2, Bao Feng^1,2, Shuang Yang^1,2, Jiangtao Xu³, Daohua Zhu³, Zhechen Huang³, Wei Liang³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4091-4113, 2025, DOI:10.32604/cmc.2025.066241 - 23 September 2025

Abstract With the deep integration of edge computing, 5G and Artificial Intelligence of Things (AIoT) technologies, the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios. Traditional federated learning (FL) algorithms face significant limitations in practical applications due to client drift, model bias, and resource constraints under non-independent and identically distributed (Non-IID) data, as well as the computational overhead and utility loss caused by privacy-preserving techniques. To address these issues, this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method (FedEPC). This method introduces… More >

Amal Al-Rasheed¹, Hashim Ali^2,*, Rahim Khan^2,*, Aamir Saeed³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3419-3436, 2025, DOI:10.32604/cmc.2025.065156 - 23 September 2025

Abstract In the domain of Electronic Medical Records (EMRs), emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy. This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data. By leveraging the decentralized and immutable nature of blockchain, the proposed approach ensures transparency, integrity, and traceability of EMR transactions, effectively mitigating risks of unauthorized access and data tampering. Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions, eliminating reliance on intermediaries and reducing the potential for… 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 >

Zekun Liu¹, Junwei Ma^1,2,*, Xin Gong¹, Xiu Liu¹, Bingbing Liu¹, Long An¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 983-999, 2025, DOI:10.32604/cmc.2025.065978 - 29 August 2025

Abstract With the ongoing digitalization and intelligence of power systems, there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting. Nevertheless, power data often contains sensitive information, making it a critical industry challenge to efficiently utilize this data while ensuring privacy. Traditional Federated Learning (FL) methods can mitigate data leakage by training models locally instead of transmitting raw data. Despite this, FL still has privacy concerns, especially gradient leakage, which might expose users’ sensitive information. Therefore, integrating Differential Privacy (DP) techniques is essential for stronger privacy protection.… More >

Muhammad Usama¹, Arshad Aziz², Imtiaz Hassan², Shynar Akhmetzhanova³, Sultan Noman Qasem^4,*, Abdullah M. Albarrak⁴, Tawfik Al-Hadhrami⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1225-1248, 2025, DOI:10.32604/cmes.2025.067098 - 31 July 2025

Abstract The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and… More >

Ling Huang^1,2, Hao Zhang^1,2, Jiwei Mo^1,2, Yuehong Liu^1,2, Qiu Lu^1,2,*, Shuiwang Li^1,2,*

Journal of Information Hiding and Privacy Protection, Vol.7, pp. 45-60, 2025, DOI:10.32604/jihpp.2025.066524 - 31 July 2025

Abstract Rapid advances in computer vision have enabled powerful visual perception systems in areas such as surveillance, autonomous driving, healthcare, and augmented reality. However, these systems often raise serious privacy concerns due to their ability to identify and track individuals without consent. This paper explores the emerging field of identity-hiding visual perception, which aims to protect personal identity within visual data through techniques such as anonymization, obfuscation, and privacy-aware modeling. We provide a system-level overview of current technologies, categorize application scenarios, and analyze major challenges—particularly the trade-off between privacy and utility, technical complexity, and ethical risks. More >

Muhammad Adil^1,*, Mona M. Jamjoom², Zahid Ullah³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4363-4380, 2025, DOI:10.32604/cmc.2025.066551 - 30 July 2025

Abstract In today’s digital world, the Internet of Things (IoT) plays an important role in both local and global economies due to its widespread adoption in different applications. This technology has the potential to offer several advantages over conventional technologies in the near future. However, the potential growth of this technology also attracts attention from hackers, which introduces new challenges for the research community that range from hardware and software security to user privacy and authentication. Therefore, we focus on a particular security concern that is associated with malware detection. The literature presents many countermeasures, but… More >

Abdelhadi Zineddine^1,#, Yousra Belfaik^2,#, Abdeslam Rehaimi¹, Yassine Sadqi^3,*, Said Safi¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4019-4054, 2025, DOI:10.32604/cmc.2025.066139 - 30 July 2025

Abstract With the proliferation of online services and applications, adopting Single Sign-On (SSO) mechanisms has become increasingly prevalent. SSO enables users to authenticate once and gain access to multiple services, eliminating the need to provide their credentials repeatedly. However, this convenience raises concerns about user security and privacy. The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats, identify gaps in existing systems, and explore effective mitigation solutions. This need motivated the first systematic literature review (SLR) of SSO security and privacy, conducted in… More >

Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma

Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >