Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing

TL;DR

This paper introduces a 'differential smoothing' method to generate partially synthetic geocoded data that reduces disclosure risk while preserving data utility, especially addressing the challenge of spatial outliers.

Contribution

The paper proposes a novel differential smoothing technique to better handle spatial outliers in synthetic geocoded data, improving privacy protection without sacrificing data quality.

Findings

Effective in reducing disclosure risk in simulated data

Maintains data utility for spatial analysis

Demonstrated on San Francisco home prices

Abstract

When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies prior to making data publicly available due to data privacy obligations. An alternative to releasing aggregated and/or perturbed data is to release multiply-imputed synthetic data, where sensitive values are replaced with draws from statistical models designed to capture important distributional features in the collected data. One issue that has received relatively little attention, however, is how to handle spatially outlying observations in the collected data, as common spatial models often have a tendency to overfit these observations. The goal of this work is to bring this issue to the forefront and propose a solution, which we refer to as "differential smoothing." After implementing our method on simulated data, highlighting the effectiveness of our approach under various scenarios, we illustrate the framework using data consisting of sale prices of homes in San Francisco.