Data Minimization at Inference Time
Cuong Tran Ferdinando Fioretto
In domains with high stakes such as law, recruitment, and healthcare,
learning models frequently rely on sensitive user data for inference,
necessitating the complete set of features. This not only poses significant
privacy risks for individuals but also demands substantial human effort from
organizations to verify information accuracy. This paper asks whether it is
necessary to use \emph{all} input features for accurate predictions at
inference time. The paper demonstrates that, in a personalized setting,
individuals may only need to disclose a small subset of their features without
compromising decision-making accuracy. The paper also provides an efficient
sequential algorithm to determine the appropriate attributes for each
individual to provide. Evaluations across various learning tasks show that
individuals can potentially report as little as 10\% of their information while
maintaining the same accuracy level as a model that employs the full set of
user information.