Data sharing is often limited by privacy issues. This is very common in particular for health datasets, given the inherent sensitivity of this type of data. When sharing of the original dataset is not possible, one method that can be used is to generate a synthetic dataset, which contains as much statistical information as possible from the original dataset, but which provides data on false individuals in order to protect the confidentiality of respondents.
This project is interested in rigorously measuring the confidentiality protection offered by a synthetic dataset. We will carefully examine some measures proposed in the literature, to understand their guarantees and the differences and similarities between them in order to identify the measure (s) that would be the most relevant for the sharing of synthetic data.