PRIVASA’s research teams and industry partners are exploring the potential and limitations of using synthetic data in health care. Synthetic data could support research and innovation activities when real data is not available due to legal, ethical or practical constraints. Synthetic data could also be used alongside real data to speed up the early phases of development.

For most applications in health care (mainly excluding primary care), it is critical that the datasets protect the individuals’ privacy. As a result, synthetic datasets are often expected to be anonymous. The process of generating synthetic data includes a well-known trade-off: synthetic datasets most similar to real data offer the least privacy protection. Likewise, synthetic datasets with little or no resemblance to real data offer strongest privacy protection.

By applying different techniques to generate synthetic datasets, we are mapping the practical implications of this privacy-utility trade-off. We are also empirically testing the usability of synthetic datasets in statistical analyses and comparing the performance to alternative solutions, such as private queries to real data.

In our research, we have focused on processing numeric tabular data and medical images.