Conditional Masking to Numerical Data

TL;DR

This paper introduces a hybrid data obfuscation method for numerical datasets that balances privacy with utility, providing unbiased distribution estimates and reliable statistical measures.

Contribution

A novel mixed obfuscation technique combining data swapping and noise addition that preserves distribution shape and statistical accuracy.

Findings

Unbiased estimation of the original data distribution achieved.

Reliable estimates of moments and correlation maintained.

Enhanced privacy protection with minimal utility loss.

Abstract

Protecting the privacy of data-sets has become hugely important these days. Many real-life data-sets like income data, medical data need to be secured before making it public. However, security comes at the cost of losing some useful statistical information about the data-set. Data obfuscation deals with this problem of masking a data-set in such a way that the utility of the data is maximized while minimizing the risk of the disclosure of sensitive information. Two popular approaches to data obfuscation for numerical data involves (i) data swapping and (ii) adding noise to data. While the former masks well sacrificing the whole of correlation information, the latter gives estimates for most of the popular statistics like mean, variance, quantiles, correlation but fails to give an unbiased estimate of the distribution curve of the original data. In this paper, we propose a mixed method of obfuscation combining the above two approaches and discuss how the proposed method succeeds in giving an unbiased estimation of the distribution curve while giving reliable estimates of the other well-known statistics like moments, correlation.