Data Minimization at Inference Time
This addresses privacy risks and reduces human effort for organizations in law, recruitment, and healthcare, though it is incremental as it builds on existing inference methods.
The paper tackles the problem of minimizing sensitive user data required for inference in high-stakes domains by showing that individuals can disclose as little as 10% of their features without compromising accuracy, using a sequential algorithm to determine necessary attributes.
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.