Yongsheng Zhu^1,2,*, Chong Liu^3,4, Chunlei Chen⁵, Xiaoting Lyu^3,4, Zheng Chen^3,4, Bin Wang⁶, Fuqiang Hu^3,4, Hanxi Li^3,4, Jiao Dai^3,4, Baigen Cai¹, Wei Wang^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1305-1325, 2024, DOI:10.32604/cmes.2024.054820 - 27 September 2024

Abstract The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency. Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data. However, despite its privacy benefits, federated learning systems are vulnerable to poisoning attacks, where adversaries alter local model parameters on compromised clients and send malicious updates to the server, potentially compromising the global model’s accuracy. In this study, we introduce PMM (Perturbation coefficient Multiplied by Maximum value), a new poisoning attack method that perturbs model More >

Cong Shen^1,*, Wei Zhang^1,2,*, Tanping Zhou^1,2, Yiming Zhang¹, Lingling Zhang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

Abdullah Alabdulatif¹, Navod Neranjan Thilakarathne^2,*, Zaharaddeen Karami Lawal^3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150 - 12 September 2024

Abstract The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining… More >

Peicong He, Yang Xin^*, Yixian Yang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777 - 15 August 2024

Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

Linghong Kuang^1,2, Wenlong Shi^1,2, Jing Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3193-3219, 2024, DOI:10.32604/cmc.2024.054377 - 15 August 2024

Abstract The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing… More >

Ahmad Rahdari^1,6, Ahmad Jalili², Mehdi Esnaashari³, Mehdi Gheisari^1,4,7,8,*, Alisa A. Vorobeva⁵, Zhaoxi Fang¹, Panjun Sun^1,*, Viktoriia M. Korzhuk⁵, Ilya Popov⁵, Zongda Wu¹, Hamid Tahaei¹

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.052994 - 15 August 2024

Abstract Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes.… More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

Qiuyan Wang, Haibing Dong^*, Yongfei Huang, Zenglei Liu, Yundong Gou

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775 - 15 August 2024

Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

Fang Hu¹, Siyi Qiu², Xiaolian Yang¹, Chaolei Wu¹, Miguel Baptista Nunes³, Hui Chen^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2897-2915, 2024, DOI:10.32604/cmc.2024.052570 - 15 August 2024

Abstract As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and… More >

Yifei Zhao, Zhaohui Li, Siyi Lv^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 217-234, 2024, DOI:10.32604/cmc.2024.052310 - 18 July 2024

Abstract The EU’s Artificial Intelligence Act (AI Act) imposes requirements for the privacy compliance of AI systems. AI systems must comply with privacy laws such as the GDPR when providing services. These laws provide users with the right to issue a Data Subject Access Request (DSAR). Responding to such requests requires database administrators to identify information related to an individual accurately. However, manual compliance poses significant challenges and is error-prone. Database administrators need to write queries through time-consuming labor. The demand for large amounts of data by AI systems has driven the development of NoSQL databases.… More >