Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Yiming Zhang¹, Wei Zhang^1,2,*, Cong Shen³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3185-3211, 2025, DOI:10.32604/cmc.2025.067563 - 23 September 2025

Abstract Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement… 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 >

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 >

Zhipeng Qin^1,2,*, Hanbing Yan³, Biyang Zhang², Peng Wang², Yitao Li³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.063308 - 19 May 2025

Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating 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 >

Conghuan Ye^1,*, Shenglong Tan¹, Shi Li¹, Jun Wang¹, Qiankun Zuo¹, Bing Xiong²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2927-2946, 2025, DOI:10.32604/cmc.2025.062051 - 16 April 2025

Abstract With the fast development of multimedia social platforms, content dissemination on social media platforms is becoming more popular. Social image sharing can also raise privacy concerns. Image encryption can protect social images. However, most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial domain. In this work, the authors propose a secure social image-sharing method with watermarking/fingerprinting and encryption. First, the fingerprint code with a hierarchical community structure is designed based on social network analysis. Then, discrete wavelet transform (DWT) from block discrete cosine transform (DCT) directly… More >

Kaiyang Han¹, Xiaoqiang Jia¹, Yangfei Lin², Tsutomu Yoshinaga², Yalong Li², Jiale Wu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2971-2988, 2025, DOI:10.32604/cmc.2025.061563 - 16 April 2025

Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >

Yanjin Cheng^1,2, Wenmin Li^1,2,*, Sujuan Qin^1,2, Tengfei Tu^1,2

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4777-4795, 2025, DOI:10.32604/cmc.2025.060380 - 06 March 2025

Abstract Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture. Existing research has shown that attackers can compromise user privacy and security by stealing model parameters. Therefore, differential privacy is applied in federated learning to further address malicious issues. However, the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance optimization. Therefore, we propose an adaptive adjusted differential privacy federated learning method. First, a dynamic adaptive privacy budget allocation strategy… 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 >