Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

TL;DR

This paper investigates how knowledge of pairwise correlations in Gaussian user traces enhances an adversary's ability to breach privacy, extending previous work on discrete data to continuous Gaussian data.

Contribution

It extends privacy analysis from discrete to Gaussian traces, showing how known correlations increase the risk of de-anonymization.

Findings

Correlation knowledge significantly aids adversaries

Privacy requirements are more stringent with known correlations

Gaussian trace analysis reveals asymptotic privacy loss

Abstract

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.