Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …

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 …

Objectives There are increasing requirements to make research data, especially clinical trial
data, more broadly available for secondary analyses. However, data availability remains a …

Background A regular task by developers and users of synthetic data generation (SDG)
methods is to evaluate and compare the utility of these methods. Multiple utility metrics have …

Background Synthetic tabular data generation is a potentially valuable technology with great
promise for data augmentation and privacy preservation. However, prior to adoption, an …

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art
technologies engaging artificial intelligence. The unavailability of quality data due to both …

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 …

Objective With the growing demand for sharing clinical trial data, scalable methods to
enable privacy protective access to high-utility data are needed. Data synthesis is one such …

Data-driven medical care delivery must always respect patient privacy—a requirement that
is not easily met. This issue has impeded improvements to healthcare software and has …