Private Ad Modeling with DP-SGD. (arXiv:2211.11896v1 [cs.LG])

A well-known algorithm in privacy-preserving ML is differentially private
stochastic gradient descent (DP-SGD). While this algorithm has been evaluated
on text and image data, it has not been previously applied to ads data, which
are notorious for their high class imbalance and sparse gradient updates. In
this work we apply DP-SGD to several ad modeling tasks including predicting
click-through rates, conversion rates, and number of conversion events, and
evaluate their privacy-utility trade-off on real-world datasets. Our work is
the first …

modeling