Simulating Geography for Microdata Disclosure via Sparse Multinomial Probit Models

• Activity Number: 338
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Lane F. Burgette*+ and Jerome P. Reiter
• Companies: Duke University and Duke University
• Keywords: Confidentiality ; Multinomial probit ; Potts model ; Spatial ; Synthetic

Abstract

Public release of spatially-referenced microdata can entail significant risk that motivated intruders will be able to learn the identities of respondents who provide sensitive data. To mitigate this risk, it is standard to aggregate data over large geographic areas, which can degrade the utility of the data for legitimate researchers. As an alternative, we propose methods to produce synthetic sets of areal identifiers. Our goal is to simulate multiple sets of data that--on average--retain the statistical properties of the observed data, while protecting respondents' anonymity. We propose methods to simulate areal identifiers using a multinomial probit model. Because this results in a model that (in typical applications) will have hundreds or even thousands of response categories, we propose a sparse structure for the multinomial model. Further, we suggest a simplified, latent Potts model structure for the regression coefficients, which can help to preserve spatial relationships. We demonstrate our methods on simulated and genuine data.