Multimethod synthetic data generation for confidentiality and measurement of disclosure risk. (1st January 2012)

• Record Type: Journal Article
• Authors: Larsen, Michael D.; Huckett, Jennifer C.
• Is Part Of: International journal of information privacy, security and integrity. Volume 1:Number 2/3(2012)
• Page Start: 184
• Page End: 204
• Publication Date: 2012-01-01
• Subjects: disclosure control -- hot deck imputation -- quantile regression -- rank swapping -- simulation -- statistical disclosure limitation -- SDL -- synthetic data -- disclosure avoidance; Computer security -- Periodicals; Computer networks -- Access control -- Periodicals; Information technology -- Security measures -- Periodicals; Data protection -- Periodicals; Privacy, Right of -- Periodicals; 005.805
• Languages: English
• ISSNs: 1741-8496

Abstract

Government agencies must simultaneously maintain confidentiality of individual records and disseminate useful microdata. We propose a method to create synthetic data that combines quantile regression, hot deck imputation, and rank swapping. The result from implementation of the proposed procedure is a releasable dataset containing original values for a few key variables, synthetic quantile regression predictions for several variables, and imputed and perturbed values for remaining variables. To measure the disclosure risk in the resulting synthetic dataset, we extend existing probabilistic risk measures that aim to imitate an intruder attempting to match a record in the released data with information previously available on a target respondent.