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Activity Number: 454 - Advances in Spatial and Spatio-Temporal Methodology with Applications to Official Statistics
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Government Statistics Section
Abstract #323296 View Presentation
Title: Generating Partially Synthetic Geocoded Public-Use Data with Decreased Disclosure Risk Using Differential Smoothing
Author(s): Harrison Quick* and Scott H. Holan and Christopher Wikle
Companies: Drexel University and University of Missouri and University of Missouri
Keywords: Bayesian methods ; Data privacy ; Multiple imputation ; Spatial modeling ; Synthetic data

When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies prior to 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 collected data. 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, propose a solution -- referred to as "differential smoothing" -- and illustrate our approach using sale prices of homes in San Francisco.

Authors who are presenting talks have a * after their name.

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