A number of learning models used in consequential domains, such as to assist
in legal, banking, hiring, and healthcare decisions, make use of potentially
sensitive users' information to carry out inference. Further, the complete set
of features is typically required to perform inference. This not only poses
severe privacy risks for the individuals using the learning systems, but also
requires companies and organizations massive human efforts to verify the
correctness of the released information.
This paper asks whether it is necessary to require \emph{all} input features
for a model to return accurate predictions at test time and shows that, under a
personalized setting, each individual may need to release only a small subset
of these features without impacting the final decisions. The paper also
provides an efficient sequential algorithm that chooses which attributes should
be provided by each individual. Evaluation over several learning tasks shows
that individuals may be able to report as little as 10\% of their information
to ensure the same level of accuracy of a model that uses the complete users'
information.

本文探讨了在重要领域（如辅助法律、银行、招聘和医疗抉择的学习模型），是否需要全部的输入特征才能在测试时返回准确的预测，并表明在个性化的环境下，每个人仅需要发布一小部分的这些特征就能保证系统准确度不受影响。研究显示，在一些学习任务中，个体只需提交不到总数据的 10％即可确保与使用完整信息的模型同样准确。同时，本文还提供了一个高效的序列算法来选择哪些属性应由每个个体提供。