Related papers: Data Minimization at Inference Time

Data Minimization at Inference Time

URL: http://arxiv.org/abs/2305.17593v1
Date: Sat, 27 May 2023 23:03:41 GMT
Title: Data Minimization at Inference Time
Authors: Cuong Tran and Ferdinando Fioretto
Abstract summary: In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference. This paper asks whether it is necessary to use emphall 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.
Score: 44.15285550981899
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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