On the utility and protection of optimization with differential privacy and classic regularization techniques. (arXiv:2209.03175v1 [cs.LG])

Nowadays, owners and developers of deep learning models must consider
stringent privacy-preservation rules of their training data, usually
crowd-sourced and retaining sensitive information. The most widely adopted
method to enforce privacy guarantees of a deep learning model nowadays relies
on optimization techniques enforcing differential privacy. According to the
literature, this approach has proven to be a successful defence against several
models' privacy attacks, but its downside is a substantial degradation of the
models' performance. In this work, we compare the …

differential privacy optimization privacy protection utility