Utilizing Noise Addition for Data Privacy, an Overview

TL;DR

This paper provides an overview of noise addition techniques for data privacy, discussing their statistical foundations, current state, and future research directions to enhance data confidentiality.

Contribution

It offers a foundational perspective on noise addition methods, including statistical considerations and a review of current advancements in data privacy.

Findings

Noise addition helps protect data privacy effectively.

Current techniques are evolving with new statistical models.

Future research should focus on optimizing noise for better utility and privacy.

Abstract

The internet is increasingly becoming a standard for both the production and consumption of data while at the same time cyber-crime involving the theft of private data is growing. Therefore in efforts to securely transact in data, privacy and security concerns must be taken into account to ensure that the confidentiality of individuals and entities involved is not compromised, and that the data published is compliant to privacy laws. In this paper, we take a look at noise addition as one of the data privacy providing techniques. Our endeavor in this overview is to give a foundational perspective on noise addition data privacy techniques, provide statistical consideration for noise addition techniques and look at the current state of the art in the field, while outlining future areas of research.