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

Yi Jie Zhang; Mamoun Alazab; Bala Anand Muthu

doi:10.1007/s13369-021-06028-1

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 journal › Article › peer-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 language	English
Pages (from-to)	1-11
Number of pages	11
Journal	Arabian Journal for Science and Engineering
Issue number	Special Issue
DOIs	https://doi.org/10.1007/s13369-021-06028-1
Publication status	Published - 16 Aug 2021

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

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.",

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KW - Big data

KW - Machine learning

KW - Privacy

KW - Security

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ER -

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

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