Scalable l-Diversity: An Extension to Scalable k-Anonymity for Privacy Preserving Big Data Publishing

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Author(s): Udai Pratap Rao (Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, India), Brijesh B. Mehta (Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, India)and Nikhil Kumar (Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, India)
Copyright: 2021
Pages: 15
Source title: Research Anthology on Privatizing and Securing Data
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8954-0.ch048

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

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Scalable l-Diversity: An Extension to Scalable k-Anonymity for Privacy Preserving Big Data Publishing

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