Training Differentially Private Ad Prediction Models with Semi-Sensitive
  Features
Lynn Chua Qiliang Cui Badih Ghazi Charlie Harrison Pritish Kamath Walid Krichene Ravi Kumar Pasin Manurangsi Krishna Giri Narra Amer Sinha Avinash Varadarajan Chiyuan Zhang
Motivated by problems arising in digital advertising, we introduce the task
of training differentially private (DP) machine learning models with
semi-sensitive features. In this setting, a subset of the features is known to
the attacker (and thus need not be protected) while the remaining features as
well as the label are unknown to the attacker and should be protected by the DP
guarantee. This task interpolates between training the model with full DP
(where the label and all features should be protected) or with label DP (where
all the features are considered known, and only the label should be protected).
We present a new algorithm for training DP models with semi-sensitive features.
Through an empirical evaluation on real ads datasets, we demonstrate that our
algorithm surpasses in utility the baselines of (i) DP stochastic gradient
descent (DP-SGD) run on all features (known and unknown), and (ii) a label DP
algorithm run only on the known features (while discarding the unknown ones).