  Differential privacy (DP), as a rigorous mathematical definition quantifying
privacy leakage, has become a well-accepted standard for privacy protection.
Combined with powerful machine learning techniques, differentially private
machine learning (DPML) is increasingly important. As the most classic DPML
algorithm, DP-SGD incurs a significant loss of utility, which hinders DPML's
deployment in practice. Many studies have recently proposed improved algorithms
based on DP-SGD to mitigate utility loss. However, these studies are isolated
and cannot comprehensively measure the performance of improvements proposed in
algorithms. More importantly, there is a lack of comprehensive research to
compare improvements in these DPML algorithms across utility, defensive
capabilities, and generalizability.
  We fill this gap by performing a holistic measurement of improved DPML
algorithms on utility and defense capability against membership inference
attacks (MIAs) on image classification tasks. We first present a taxonomy of
where improvements are located in the machine learning life cycle. Based on our
taxonomy, we jointly perform an extensive measurement study of the improved
DPML algorithms. We also cover state-of-the-art label differential privacy
(Label DP) algorithms in the evaluation. According to our empirical results, DP
can effectively defend against MIAs, and sensitivity-bounding techniques such
as per-sample gradient clipping play an important role in defense. We also
explore some improvements that can maintain model utility and defend against
MIAs more effectively. Experiments show that Label DP algorithms achieve less
utility loss but are fragile to MIAs. To support our evaluation, we implement a
modular re-usable software, DPMLBench, which enables sensitive data owners to
deploy DPML algorithms and serves as a benchmark tool for researchers and
practitioners.
