Private Ad Modeling with DP-SGD
This work addresses privacy concerns in ad modeling for advertisers and platforms, but it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of applying differentially private stochastic gradient descent (DP-SGD) to ad modeling tasks, demonstrating empirically that it can achieve both privacy and utility for predicting click-through rates, conversion rates, and conversion events on real-world datasets.
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 to empirically demonstrate that DP-SGD can provide both privacy and utility for ad modeling tasks.