Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing

Abstract

When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies before making data publicly available. An alternative to releasing aggregated and/or perturbed data is to release synthetic data, where sensitive values are replaced with draws from models designed to capture distributional features in the data collected. The issues associated with spatially outlying observations in the data, however, have received relatively little attention. Our goal here is to shed light on this problem, to propose a solution—referred to as ‘differential smoothing’—and to illustrate our approach by using sale prices of homes in San Francisco.