  Differential privacy allows quantifying privacy loss resulting from accessing
sensitive personal data. Repeated accesses to underlying data incur increasing
loss. Releasing data as privacy-preserving synthetic data would avoid this
limitation, but would leave open the problem of designing what kind of
synthetic data. We propose formulating the problem of private data release
through probabilistic modelling. This approach transforms the problem of
designing the synthetic data into choosing a model for the data, allowing also
including prior knowledge, which improves the quality of the synthetic data. We
demonstrate empirically, in an epidemiological study, that statistical
discoveries can be reliably reproduced from the synthetic data. We expect the
method to have broad use in creating high-quality anonymized data twins of key
data sets for research.
