Conveners
Synthetic Data
- Jérémie Despraz (CHUV)
Description
Companies, and in particular hospitals, collect large quantities of personal data. This information is extremely valuable and serves as a basis for many scientific studies.
As of today, the reuse of such data is regulated by the Swiss Data Protection Law and the Swiss Law on Human Research. These laws prevent, to some extent, the processing, sharing, and publication of unmodified data that can potentially contain identifying information. As a result, research has to be performed on altered information that eventually reduces the accuracy of scientific experiments and data analyses.
With the advent of Machine Learning, and in particular Generative Adversarial Networks (GANs), we are now able to generate synthetic data that possesses similar statistical properties than a reference population without creating exact copies of the original individuals. Some authors claim that this creates a paradigm shift in this privacy – utility tradeoff where synthetic data is now able to keep its utility while remaining safe in terms of privacy disclosure.
This talk will present the preliminary results from a study carried out at CHUV to test and validate this synthetic data approach with the final objective being to determine its real potential in a hospital setting.
