Representation Learning for High-Dimensional Data Collection under Local Differential Privacy

TL;DR

This paper proposes a novel representation learning-based approach for high-dimensional data collection under local differential privacy, improving utility by adding noise to low-dimensional representations and introducing a denoising method for better downstream model performance.

Contribution

It introduces a new LDP mechanism leveraging low-dimensional representations and a denoising technique, addressing utility loss in high-dimensional data collection.

Findings

Outperforms existing LDP mechanisms in utility.

Effective noise addition on low-dimensional manifolds.

Enhanced downstream model accuracy with proposed methods.

Abstract

The collection of individuals' data has become commonplace in many industries. Local differential privacy (LDP) offers a rigorous approach to preserving privacy whereby the individual privatises their data locally, allowing only their perturbed datum to leave their possession. LDP thus provides a provable privacy guarantee to the individual against both adversaries and database administrators. Existing LDP mechanisms have successfully been applied to low-dimensional data, but in high dimensions the privacy-inducing noise largely destroys the utility of the data. In this work, our contributions are two-fold: first, by adapting state-of-the-art techniques from representation learning, we introduce a novel approach to learning LDP mechanisms. These mechanisms add noise to powerful representations on the low-dimensional manifold underlying the data, thereby overcoming the prohibitive noise requirements of LDP in high dimensions. Second, we introduce a novel denoising approach for downstream model learning. The training of performant machine learning models using collected LDP data is a common goal for data collectors, and downstream model performance forms a proxy for the LDP data utility. Our approach significantly outperforms current state-of-the-art LDP mechanisms.