Information-theoretic Estimation of the Risk of Privacy Leaks

TL;DR

This paper introduces an information-theoretic approach using correlation metrics like MIC to estimate and prevent privacy leaks in datasets, providing a computationally efficient measure of privacy risk.

Contribution

It extends existing privacy risk models by incorporating entropy, mutual information, and correlation metrics to better detect potential privacy breaches.

Findings

The proposed hybrid metric effectively identifies attribute dependencies linked to privacy leaks.

The method offers a computationally efficient worst-case privacy loss estimation.

Experimental results demonstrate improved detection of privacy vulnerabilities.

Abstract

Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the original data and their noisy responses from a randomizer as an indicator of potential privacy breaches. This paper aims to leverage information-theoretic measures, such as the Maximal Information Coefficient (MIC), to estimate privacy leaks and derive novel, computationally efficient privacy leak estimators. We extend the $\rho_1$-to-$\rho_2$ formulation~\cite{Evfimievski2003} to incorporate entropy, mutual information, and the degree of anonymity for a more comprehensive measure of privacy risk. Our proposed hybrid metric can identify correlation dependencies between attributes in the dataset, serving as a proxy for privacy leak vulnerabilities. This metric provides a computationally efficient worst-case measure of privacy loss, utilizing the inherent characteristics of the data to prevent privacy breaches.