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

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.