Title: Improved Accounting for Differentially Private Learning,
Abstract: We consider the problem of differential privacy accounting, i.e. estimation
of privacy loss bounds, in machine learning in a broad sense. We propose two
versions of a generic privacy accountant suitable for a wide range of learning
algorithms. Both versions are derived in a simple and principled way using
well-known tools from probability theory, such as concentration inequalities.
We demonstrate that our privacy accountant is able to achieve state-of-the-art
estimates of DP guarantees and can be applied to new areas like variational
inference. Moreover, we show that the latter enjoys differential privacy at
minor cost.