  The digitization of medical records ushered in a new era of big data to
clinical science, and with it the possibility that data could be shared, to
multiply insights beyond what investigators could abstract from paper records.
The need to share individual-level medical data to accelerate innovation in
precision medicine continues to grow, and has never been more urgent, as
scientists grapple with the COVID-19 pandemic. However, enthusiasm for the use
of big data has been tempered by a fully appropriate concern for patient
autonomy and privacy. That is, the ability to extract private or confidential
information about an individual, in practice, renders it difficult to share
data, since significant infrastructure and data governance must be established
before data can be shared. Although HIPAA provided de-identification as an
approved mechanism for data sharing, linkage attacks were identified as a major
vulnerability. A variety of mechanisms have been established to avoid leaking
private information, such as field suppression or abstraction, strictly
limiting the amount of information that can be shared, or employing
mathematical techniques such as differential privacy. Another approach, which
we focus on here, is creating synthetic data that mimics the underlying data.
For synthetic data to be a useful mechanism in support of medical innovation
and a proxy for real-world evidence, one must demonstrate two properties of the
synthetic dataset: (1) any analysis on the real data must be matched by
analysis of the synthetic data (statistical fidelity) and (2) the synthetic
data must preserve privacy, with minimal risk of re-identification (privacy
guarantee). In this paper we propose a framework for quantifying the
statistical fidelity and privacy preservation properties of synthetic datasets
and demonstrate these metrics for synthetic data generated by Syntegra
technology.
