Releasing Large-Scale Human Mobility Histograms with Differential Privacy

TL;DR

This paper presents a new differentially private mechanism for releasing large-scale human mobility data, balancing privacy and utility, with potential applications in environmental policy and transportation planning.

Contribution

It introduces a novel mechanism achieving approximately 2-privacy budget while satisfying strict utility constraints for group-by-sum workloads in mobility data.

Findings

Achieves $ \\epsilon \\approx 2 $-DP with high utility

Outperforms natural baseline mechanisms

Applicable to broad group-by-sum data workloads

Abstract

Environmental Insights Explorer (EIE) is a Google product that reports aggregate statistics about human mobility, including various methods of transit used by people across roughly 50,000 regions globally. These statistics are used to estimate carbon emissions and provided to policymakers to inform their decisions on transportation policy and infrastructure. Due to the inherent sensitivity of this type of user data, it is crucial that the statistics derived and released from it are computed with appropriate privacy protections. In this work, we use a combination of federated analytics and differential privacy to release these required statistics, while operating under strict error constraints to ensure utility for downstream stakeholders. In this work, we propose a new mechanism that achieves $ \epsilon \approx 2 $-DP while satisfying these strict utility constraints, greatly improving over natural baselines. We believe this mechanism may be of more general interest for the broad class of group-by-sum workloads.