Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026

Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… More >

Yuewei Tian¹, Yang Su², Yujia Wang¹, Lisa Guo¹, Xuyang Wu^3,*, Lei Cao⁴, Fang Ren³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072081 - 12 January 2026

Abstract This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning. The framework integrates blockchain technology, the InterPlanetary File System (IPFS) for distributed storage, and a dynamic differential privacy mechanism to achieve collaborative security across the storage, service, and federated coordination layers. It accommodates both multimodal data classification and object detection tasks, enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection. This effectively mitigates the single-point… More >

Samuel Acheme^*, Glory Nosawaru Edegbe

Journal of Cyber Security, Vol.8, pp. 1-31, 2026, DOI:10.32604/jcs.2026.073923 - 07 January 2026

Abstract Federated learning (FL) enables collaborative model training across decentralized datasets, thus maintaining the privacy of training data. However, FL remains vulnerable to malicious actors, posing significant risks in privacy-sensitive domains like healthcare. Previous machine learning trust frameworks, while promising, often rely on resource-intensive blockchain ledgers, introducing computational overhead and metadata leakage risks. To address these limitations, this study presents a novel Decentralized Identity (DID) framework for mutual authentication that establishes verifiable trust among participants in FL without dependence on centralized authorities or high-cost blockchain ledgers. The proposed system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials… More >

Raghavendra Kulkarni¹, Venkata Satya Suresh kumar Kondeti¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069835 - 10 November 2025

Abstract The next-generation RAN, known as Open Radio Access Network (ORAN), allows for several advantages, including cost-effectiveness, network flexibility, and interoperability. Now ORAN applications, utilising machine learning (ML) and artificial intelligence (AI) techniques, have become standard practice. The need for Federated Learning (FL) for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques. However, the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference. Therefore, this research presents a… 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 >

Huaxiong Liao^1,2, Xiangxuan Zhong², Xueqi Chen², Yirui Huang³, Yuwei Lin², Jing Zhang^2,*, Bruce Gu⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069208 - 10 November 2025

Abstract The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns. However, the use of real-world trajectory data poses significant privacy risks, such as location re-identification and correlation attacks. To address these challenges, privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data. This paper introduces DPIL-Traj, an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation. Firstly, the framework incorporates Differential Privacy Clustering, which anonymizes trajectory data by applying differential privacy techniques that add noise, ensuring the… More >

Ginanjar Suwasono Adi¹, Samsul Huda^2,*, Griffani Megiyanto Rahmatullah³, Dodit Suprianto¹, Dinda Qurrota Aini Al-Sefy³, Ivon Sandya Sari Putri⁴, Lalu Tri Wijaya Nata Kusuma⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068619 - 10 November 2025

Abstract In the competitive retail industry of the digital era, data-driven insights into gender-specific customer behavior are essential. They support the optimization of store performance, layout design, product placement, and targeted marketing. However, existing computer vision solutions often rely on facial recognition to gather such insights, raising significant privacy and ethical concerns. To address these issues, this paper presents a privacy-preserving customer analytics system through two key strategies. First, we deploy a deep learning framework using YOLOv9s, trained on the RCA-TVGender dataset. Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >