  Existing work on privacy-preserving machine learning with Secure Multiparty
Computation (MPC) is almost exclusively focused on model training and on
inference with trained models, thereby overlooking the important data
pre-processing stage. In this work, we propose the first MPC based protocol for
private feature selection based on the filter method, which is independent of
model training, and can be used in combination with any MPC protocol to rank
features. We propose an efficient feature scoring protocol based on Gini
impurity to this end. To demonstrate the feasibility of our approach for
practical data science, we perform experiments with the proposed MPC protocols
for feature selection in a commonly used machine-learning-as-a-service
configuration where computations are outsourced to multiple servers, with
semi-honest and with malicious adversaries. Regarding effectiveness, we show
that secure feature selection with the proposed protocols improves the accuracy
of classifiers on a variety of real-world data sets, without leaking
information about the feature values or even which features were selected.
Regarding efficiency, we document runtimes ranging from several seconds to an
hour for our protocols to finish, depending on the size of the data set and the
security settings.
