Data Smearing: An Approach to Disclosure Limitation for Tabular Data

• Activity Number: 301
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Daniell Toth*+
• Companies: Bureau of Labor Statistics
• Keywords: contingency tables ; synthetic data ; confidentiality ; nearest neighbor

Abstract

Statistical agencies often collect sensitive data for release to the public at aggregated levels in the form of tables. In this article, we propose a new disclosure limitation method to replace the full set of micro-data with synthetic data for use in producing released data in tabular form. This synthetic data is obtained by breaking each unit's data into small pieces and spreading them among the nearest neighbors by repeatedly sampling from the set of fragmented original micro-data. The synthetic data is produced in a way to give approximately unbiased estimates for aggregate cells as the number of units in the cell increases. The method is applied to the U.S. Bureau of Labor Statistics' Quarterly Census of Employment and Wages data, which is released to the public quarterly, in tabular form, aggregated across varying scales of time, area, and economic sector.