Review of Different Privacy Preserving Techniques in PPDP

TL;DR

This paper reviews various privacy-preserving techniques in data publishing, analyzing their effectiveness and limitations, and introduces Differential Privacy as a novel approach to balance privacy and data utility.

Contribution

It provides a comprehensive review of existing anonymization algorithms and proposes Differential Privacy as a new method to improve privacy preservation.

Findings

Analyzes k-anonymity, l-diversity, and t-closeness algorithms.

Highlights limitations of existing anonymization techniques.

Introduces Differential Privacy as an effective alternative.

Abstract

Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and tcloseness, one can achieve privacy at minimum loss. Admitting these techniques has some limitations. We need to maintain trade-off between privacy and information loss. We introduce a novel approach called Differential Privacy.