  This paper considers the problem of enhancing user privacy in common machine
learning development tasks, such as data annotation and inspection, by
substituting the real data with samples form a generative adversarial network.
We propose employing Bayesian differential privacy as the means to achieve a
rigorous theoretical guarantee while providing a better privacy-utility
trade-off. We demonstrate experimentally that our approach produces
higher-fidelity samples, compared to prior work, allowing to (1) detect more
subtle data errors and biases, and (2) reduce the need for real data labelling
by achieving high accuracy when training directly on artificial samples.
