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  • Open Access


    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

    Sandeep Dasari, Rajesh Kaluri*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2035-2051, 2024, DOI:10.32604/cmes.2024.049152

    Abstract The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the… More > Graphic Abstract

    2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

  • Open Access


    Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

    Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

    Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

  • Open Access


    Federated Learning on Internet of Things: Extensive and Systematic Review

    Meenakshi Aggarwal1, Vikas Khullar1, Sunita Rani2, Thomas André Prola3,4,5, Shyama Barna Bhattacharjee6, Sarowar Morshed Shawon7, Nitin Goyal8,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1795-1834, 2024, DOI:10.32604/cmc.2024.049846

    Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles.… More >

  • Open Access


    Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

    Muhammad Ahmad Nawaz Ul Ghani1, Kun She1,*, Muhammad Arslan Rauf1, Shumaila Khan2, Javed Ali Khan3, Eman Abdullah Aldakheel4, Doaa Sami Khafaga4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611

    Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

  • Open Access


    FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption

    Hao Sun*, Xiubo Chen, Kaiguo Yuan

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159

    Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

  • Open Access


    Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

    Nagaraju Pacharla, K. Srinivasa Reddy*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3089-3110, 2024, DOI:10.32604/cmc.2024.048133

    Abstract With the recent technological developments, massive vehicular ad hoc networks (VANETs) have been established, enabling numerous vehicles and their respective Road Side Unit (RSU) components to communicate with one another. The best way to enhance traffic flow for vehicles and traffic management departments is to share the data they receive. There needs to be more protection for the VANET systems. An effective and safe method of outsourcing is suggested, which reduces computation costs by achieving data security using a homomorphic mapping based on the conjugate operation of matrices. This research proposes a VANET-based data outsourcing… More >

  • Open Access


    An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

    Linlin Yuan1,2, Tiantian Zhang1,3, Yuling Chen1,*, Yuxiang Yang1, Huang Li1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907

    Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based… More >

  • Open Access


    A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

    Hao Jiang1, Yuerong Liao1, Dongdong Zhao2, Wenjian Luo3, Xingyi Zhang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653

    Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the… More >

  • Open Access


    Securing Mobile Cloud-Based Electronic Health Records: A Blockchain-Powered Cryptographic Solution with Enhanced Privacy and Efficiency

    Umer Nauman1, Yuhong Zhang2, Zhihui Li3, Tong Zhen1,3,*

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 15-34, 2024, DOI:10.32604/jimh.2024.048784

    Abstract The convergence of handheld devices and cloud-based computing has transformed how Electronic Health Records (EHRs) are stored in mobile cloud paradigms, offering benefits such as affordability, adaptability, and portability. However, it also introduces challenges regarding network security and data confidentiality, as it aims to exchange EHRs among mobile users while maintaining high levels of security. This study proposes an innovative blockchain-based solution to these issues and presents secure cloud storage for healthcare data. To provide enhanced cryptography, the proposed method combines an enhanced Blowfish encryption method with a new key generation technique called Elephant Herding… More >

  • Open Access


    VKFQ: A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

    Youlin Ji, Bo Yin*, Ke Gu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4205-4223, 2024, DOI:10.32604/cmc.2024.049086

    Abstract With its untameable and traceable properties, blockchain technology has been widely used in the field of data sharing. How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing. In this paper, we study verifiable keyword frequency (KF) queries with local differential privacy in blockchain. Both the numerical and the keyword attributes are present in data objects; the latter are sensitive and require privacy protection. However, prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries. We… More >

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