Machine Learning-Based Holistic Privacy Decentralized Framework for Big Data Security and Privacy in Smart City

Yi Jie Zhang, Mamoun Alazab, Bala Anand Muthu

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Big data growth and the evolution of IoT technology figured prominently in making smart city projects feasible. The risk factors for big data in the smart city include data security, and privacy is considered an important factor. In this paper, machine learning-based holistic privacy decentralized framework (ML-HPDF) has been proposed to enhance public safety and confidentiality of the data accessibility for a statistics consumer. Hence, double authentication private-preserving analysis is integrated with ML-HPDF to guarantee the accessibility of transaction data, data providers' secrecy, and fairness between information providers and information customers. The simulation investigation is undertaken based on safety, efficiency, and confidentiality.

    Original languageEnglish
    Number of pages11
    JournalArabian Journal for Science and Engineering
    Early online date16 Aug 2021
    DOIs
    Publication statusE-pub ahead of print - 16 Aug 2021

    Fingerprint

    Dive into the research topics of 'Machine Learning-Based Holistic Privacy Decentralized Framework for Big Data Security and Privacy in Smart City'. Together they form a unique fingerprint.

    Cite this