Hybrid Optimization Techniques for Data Privacy Preserving in the Metaverse Ecosystem

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Author(s): M. P. Karthikeyan (JAIN University (Deemed), India), K. Krishnaveni (Sri S. Ramasamy Naidu Memorial College, Sattur, India), T. Revathi (Woxsen University, India)and A. Hema Ambiha (Karpagam Academy of Higher Education, India)
Copyright: 2023
Pages: 14
Source title: Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse
Source Author(s)/Editor(s): Alex Khang (Global Research Institute of Technology and Engineering, USA), Vrushank Shah (Institute of Technology and Engineering, Indus University, India)and Sita Rani (Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, India)
DOI: 10.4018/978-1-6684-8851-5.ch021

Keywords: Artificial Intelligence / Engineering Science Reference / Media & Communications / Virtual Communities & Virtual Reality

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Abstract

Large-scale electronic databases are being maintained by businesses and can be accessed via the internet or intranet. Employing data mining techniques, significant information was extracted from the data. The privacy of the data is inherently at risk while data mining operations are being carried out. All users shouldn't have access to the private information stored in the database. Methods for protecting privacy have been suggested in the literature. Algorithms used in privacy-preserving data mining (PPDM) on private data are unknown even to the algorithm operator. Personal information about users and data on their collective behaviour are the two main aspects of privacy preservation. The majority of privacy-preserving techniques rely on reducing the level of granularity used to represent the data. Although privacy is improved, information is lost as a result. As a result, with PPDM, there is a trade-off between privacy and information loss. Effective methods that don't undermine the security defences are needed.

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Hybrid Optimization Techniques for Data Privacy Preserving in the Metaverse Ecosystem

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