  Differential Privacy can provide provable privacy guarantees for training
data in machine learning. However, the presence of proofs does not preclude the
presence of errors. Inspired by recent advances in auditing which have been
used for estimating lower bounds on differentially private algorithms, here we
show that auditing can also be used to find flaws in (purportedly)
differentially private schemes. In this case study, we audit a recent open
source implementation of a differentially private deep learning algorithm and
find, with 99.99999999% confidence, that the implementation does not satisfy
the claimed differential privacy guarantee.
