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.

本文考虑在隐私、准确性和个性化之间的权衡，通过提供一种有效的顺序算法，确定在个性化背景下仅需披露小部分特征即可提供和利用敏感数据，并演示了在不同学习任务中，个体只需披露 10% 的属​性即可保持与全集的精度相同。