Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.

ABSTRACT:

Background

There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.

Objective

The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.

Methods

A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samp

SUBMITTER: El Emam K 

PROVIDER: S-EPMC7704280 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature