Samar Abbas Mangi^1,*, Samina Rajper¹, Noor Ahmed Shaikh¹, Shehzad Ashraf Chaudhry^2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077337 - 09 April 2026

Abstract Malicious domain detection (MDD) from DNS telemetry enables early threat hunting but is constrained by privacy and data-sharing barriers across organizations. We present a deployable federated learning (FL) pipeline that trains a compact deep neural network (DNN; 64-32-16 with ReLU and dropout 0.3) locally at each client and exchanges only masked model updates. Privacy is enforced via secure aggregation (the server observes only an aggregate of masked updates) and optional server-side differential privacy (DP) via clipping and Gaussian noise. Our feature schema combines DNS-specific lexical cues (character n-grams, entropy, TLD indicators) with lightweight behavioral signals More >

Abuzar Khan¹, Abid Iqbal^2,*, Ghassan Husnain^1,*, Fahad Masood¹, Mohammed Al-Naeem³, Sajid Iqbal⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077222 - 09 April 2026

Abstract Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic… More >

Wenxuan Yu¹, Wenjing Gao¹, Jiuru Wang², Rong Hao^1,*, Jia Yu^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076731 - 09 April 2026

Abstract Non-negative Matrix Factorization (NMF) is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally. Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers, effectively solving the problem of local computing difficulties. However, the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation, resulting in low efficiency on the server side. In order to improve the efficiency of outsourcing computation, we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers. We adopt the matrix blocking technique to divide the computation task into multiple subtasks, More >

Michael Juma Ayuma^1,*, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka²

Journal on Internet of Things, Vol.8, pp. 31-65, 2026, DOI:10.32604/jiot.2026.074919 - 16 March 2026

Abstract Encryption is essential for safeguarding sensitive data by transforming it into a secret code, which can only be decrypted by authorized parties. This ensures privacy and protects data from unauthorized access. While various encryption algorithms exist, relying on a single method may not provide sufficient security, particularly in the context of token transmission. Common threats such as brute force attacks, man-in-the-middle (MITM) attacks, token modification, and replay attacks are prevalent in adversarial attempts to breach the security of tokens during transmission. When these vulnerabilities are not addressed, they can compromise token integrity and the security… 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 >

Jian Wu^*, Xiaowei Hao, Chao Han

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071023 - 27 February 2026

Abstract Owing to the development of communication technologies and control systems, the integration of numerous Internet of Things (IoT) nodes into the power grid has become increasingly prevalent. These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption, thereby transforming the traditional power grid into a smart grid (SG). However, the openness of wireless communication channels introduces vulnerabilities, as it allows potential eavesdroppers to intercept sensitive information. This poses threats to the secure and efficient operation of the IoT-driven smart grid. To address these challenges, we propose a… More >

Alaa Tolah^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074997 - 10 February 2026

Abstract The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors. This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field. To address this, we propose TeachSecure-CTI, a novel framework for adaptive cybersecurity curriculum generation that integrates real-time Cyber Threat Intelligence (CTI) with AI-driven personalization. Our framework employs a layered architecture featuring a CTI ingestion and clustering module, natural language processing for semantic concept extraction, and a reinforcement learning agent for adaptive content sequencing. By… More >

Ye Tian^1,*, Zhuokun Fan¹, Yifeng Zhang²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073935 - 10 February 2026

Abstract Amid the increasing demand for data sharing, the need for flexible, secure, and auditable access control mechanisms has garnered significant attention in the academic community. However, blockchain-based ciphertext-policy attribute-based encryption (CP-ABE) schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration. To address these issues, we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration. This scheme incorporates a multi-authority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption, effectively reducing both computational and communication overhead. It 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 >

Han-Yu Lin, Tung-Tso Tsai^*, Yi-Jia Ye

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073553 - 10 February 2026

Abstract Cloud data sharing is an important issue in modern times. To maintain the privacy and confidentiality of data stored in the cloud, encryption is an inevitable process before uploading the data. However, the centralized management and transmission latency of the cloud makes it difficult to support real-time processing and distributed access structures. As a result, fog computing and the Internet of Things (IoT) have emerged as crucial applications. Fog-assisted proxy re-encryption is a commonly adopted technique for sharing cloud ciphertexts. It allows a semi-trusted proxy to transform a data owner’s ciphertext into another re-encrypted ciphertext… More >