Comparison and Analysis of Anonymization Techniques for Preserving Privacy in Big Data
Johny Antony P, Antony Selvadoss Thanamani · Advances in Computational Sciences and Technology · 2017
Modern technology and networking generates huge volume of data .Privacy of data is a crucial issue and a topic for significant research.Data publishing faces the problem of deciding how to publish useful data while preserving privacy-sensitive information according to the privacy requirements of data holders.According to the concept of the privacy protection, it is defined as such the accessing of published data must not allow the unwanted users to identify anything about the targeted individuals.This paper presents a classification and analysis of various anonymization techniques for privacy preservation like k-anonymity, l-diversity, t-closeness, differential privacy, slicing.