On the Risks of Collecting Multidimensional Data Under Local Differential Privacy

TL;DR

This paper examines privacy vulnerabilities in local differential privacy protocols for multidimensional data, highlighting threats like re-identification and attribute inference, and proposes a countermeasure to enhance privacy and utility.

Contribution

It analyzes privacy threats in existing LDP protocols for multidimensional data and introduces a countermeasure to improve privacy robustness and utility.

Findings

Identified re-identification and attribute inference risks in LDP protocols.

Experimentally assessed five widely used LDP mechanisms.

Proposed a countermeasure enhancing privacy and utility.

Abstract

The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differential privacy (LDP). Numerous LDP protocols have been developed for the task of frequency estimation of single and multiple attributes. These studies mainly focused on improving the utility of the algorithms to ensure the server performs the estimations accurately. In this paper, we investigate privacy threats (re-identification and attribute inference attacks) against LDP protocols for multidimensional data following two state-of-the-art solutions for frequency estimation of multiple attributes. To broaden the scope of our study, we have also experimentally assessed five widely used LDP protocols, namely, generalized randomized response, optimal local hashing, subset selection, RAPPOR and optimal unary encoding. Finally, we also proposed a countermeasure that improves both utility and robustness against the identified threats. Our contributions can help practitioners aiming to collect users' statistics privately to decide which LDP mechanism best fits their needs.