Preserving data utility via BART

• Published in: Journal of statistical planning and inference Vol. 140; no. 9; pp. 2551 - 2561
• Main Authors: Wang, Xinlei, Karr, Alan F.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.09.2010; Elsevier
• Subjects: Bayesian additive regression trees; Combinatorics; Combinatorics. Ordered structures; Confidentiality; Disclosure limitation; Exact sciences and technology; General topics; Graph theory; Linear inference, regression; Mathematics; MCMC; Probability and statistics; Sciences and techniques of general use; Statistics; Swapping; Utility; Variable selection; Disclosure limitation; Utility; Confidentiality; MCMC; Bayesian additive regression trees; Swapping; Variable selection; Bayes estimation; Monte Carlo method; Statistical decision; Markov chain; Statistical method; Statistical regression; Simulation
• ISSN: 0378-3758; 1873-1171
• DOI: 10.1016/j.jspi.2010.03.022

When preparing data for public release, information organizations face the challenge of preserving the quality of data while protecting the confidentiality of both data subjects and sensitive data attributes. Without knowing what type of analyses will be conducted by data users, it is often hard to alter data without sacrificing data utility. In this paper, we propose a new approach to mitigate this difficulty, which entails using Bayesian additive regression trees (BART), in connection with existing methods for statistical disclosure limitation, to help preserve data utility while meeting confidentiality requirements. We illustrate the performance of our method through both simulation and a data example. The method works well when the targeted relationship underlying the original data is not weak, and the performance appears to be robust to the intensity of alteration.