Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis

ABSTRACT: While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients’ trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients’ privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an “avatar data”, for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statist

SUBMITTER: Guillaudeux M 

PROVIDER: S-EPMC10006164 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature