Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk

TL;DR

This paper introduces a new entropy-based measure to evaluate privacy risks in data sharing techniques, enabling comparison of methods like query restriction, sampling, and noise addition.

Contribution

It proposes a novel entropy-based security measure applicable to various privacy-preserving techniques, facilitating their evaluation and comparison.

Findings

The measure effectively quantifies privacy risk across different methods.

Empirical evaluation shows differences in privacy protection levels.

The approach aids in selecting appropriate privacy techniques.

Abstract

It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and k-anonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.