LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy

TL;DR

LoPub introduces an efficient method for publishing high-dimensional crowdsourced data with local differential privacy, preserving data utility while ensuring privacy through joint distribution estimation and dimension reduction.

Contribution

The paper presents novel algorithms based on EM and Lasso regression for joint distribution estimation under local privacy, enabling effective high-dimensional data publication.

Findings

Efficient multivariate distribution estimation scheme demonstrated.

LoPub effectively preserves privacy while maintaining data utility.

Experimental results confirm the scheme's practicality on real-world datasets.

Abstract

High-dimensional crowdsourced data collected from a large number of users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to participants. Local privacy, a variant of differential privacy, is proposed as a means to eliminate the privacy concern. Unfortunately, achieving local privacy on high-dimensional crowdsourced data raises great challenges on both efficiency and effectiveness. Here, based on EM and Lasso regression, we propose efficient multi-dimensional joint distribution estimation algorithms with local privacy. Then, we develop a Locally privacy-preserving high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. In particular, both correlations and joint distribution among multiple attributes can be identified to reduce the dimension of crowdsourced data, thus achieving both efficiency and effectiveness in locally private high-dimensional data publication. Extensive experiments on real-world datasets demonstrated that the efficiency of our multivariate distribution estimation scheme and confirm the effectiveness of our LoPub scheme in generating approximate datasets with local privacy.