Synthetic data generation is being increasingly used as a privacy preserving approach for
sharing health data. In addition to protecting privacy, it is important to ensure that generated …

This paper builds on the results of the ESANN 2019 conference paper" Privacy Preserving
Synthetic Health Data"[16], which develops metrics for assessing privacy and utility of …

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

Recent advances in deep generative models have greatly expanded the potential to create
realistic synthetic health datasets. These synthetic datasets aim to preserve the …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-
related data beyond its initial collection while addressing privacy concerns. However, there …

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 …

We develop metrics for measuring the quality of synthetic health data for both education and
research. We use novel and existing metrics to capture a synthetic dataset's resemblance …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

With the recent advancement in the field of machine learning, health synthetic data has
become a promising technique to address difficulties with time consumption when accessing …