Validating a membership disclosure metric for synthetic health data.

ABSTRACT:

Background

One of the increasingly accepted methods to evaluate the privacy of synthetic data is by measuring the risk of membership disclosure. This is a measure of the F1 accuracy that an adversary would correctly ascertain that a target individual from the same population as the real data is in the dataset used to train the generative model, and is commonly estimated using a data partitioning methodology with a 0.5 partitioning parameter.

Objective

Validate the membership disclosure F1 score, evaluate and improve the parametrization of the partitioning method, and provide a benchmark for its interpretation.

Materials and methods

We performed a simulated membership disclosure attack on 4 population datasets: an Ontario COVID-19 dataset, a state hospital discharge da

SUBMITTER: El Emam K 

PROVIDER: S-EPMC9553223 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature