Analysis and Comparison of Anonymous Techniques for Privacy Preserving in Big Data

Abstract: Modern technology and networking generates huge volume of data. Privacy of data is a crucial issue and a topic for significant research. Data Publishing undergoes the major problem of deciding how to publish the useful data while preserving privacy-sensitive information according to the associated 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 represents an analysis and classification of various anonymous techniques for privacy preservation like t-closeness, k-anonymity, l-diversity, slicing and differential privacy.

Keywords: Privacy-Sensitive Information, Preservation, t-closeness, k-anonymity, l-diversity slicing and Differential Privacy.