Using partially synthetic microdata to protect sensitive cells in business statistics

Article type: Research Article

Authors: Miranda, Javier^a | Vilhuber, Lars^{b; *}

Affiliations: [a] Bureau of the Census, Washington, DC, USA | [b] Cornell University, Ithaca, NY, USA

Correspondence: [*] Corresponding author: Lars Vilhuber, Cornell University, Ithaca, NY, USA. E-mail:lars.vilhuber@cornell.edu

Abstract: We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau's Business Dynamics Statistics and Synthetic Longitudinal Business Database, highlighting accuracy and protection afforded by the method when compared to existing public-use tabulations (with suppressions).

Keywords: Synthetic data, statistical disclosure limitation, time-series, local labor markets, gross job flows, confidentiality protection

DOI: 10.3233/SJI-160963

Journal: Statistical Journal of the IAOS, vol. 32, no. 1, pp. 69-80, 2016