From Principle to Practice: Vertical Data Minimization for Machine Learning. (arXiv:2311.10500v1 [cs.LG])

Aiming to train and deploy predictive models, organizations collect large
amounts of detailed client data, risking the exposure of private information in
the event of a breach. To mitigate this, policymakers increasingly demand
compliance with the data minimization (DM) principle, restricting data
collection to only that data which is relevant and necessary for the task.
Despite regulatory pressure, the problem of deploying machine learning models
that obey DM has so far received little attention. In this work, we address
this …

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