Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation.

ABSTRACT: There is a growing expectation that data collected by government-funded studies should be openly available to ensure research reproducibility, and so is the concern on data-privacy. A strategy to protect individuals' identity is to release multiply imputed (MI) synthetic datasets with masked sensitivity values (Rubin, 1993). However, information loss or incorrectly specified imputation models can weaken or invalidate the inferences obtained from the MI-datasets. Studying a restricted-use Canadian Scleroderma Research Group (CSRG) dataset, the authors investigate the use of a new masking framework with a data-augmentation (DA) component and a tuning mechanism that balances between protecting identity-disclosure and preserving data-utility. They found, respectively in a work-disability and a

SUBMITTER: Jiang B 

PROVIDER: S-EPMC11466287 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature