Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. (arXiv:2202.10517v3 [cs.LG] UPDATED)

Applying machine learning (ML) to sensitive domains requires privacy
protection of the underlying training data through formal privacy frameworks,
such as differential privacy (DP). Yet, usually, the privacy of the training
data comes at the cost of the resulting ML models' utility. One reason for this
is that DP uses one uniform privacy budget epsilon for all training data
points, which has to align with the strictest privacy requirement encountered
among all data holders. In practice, different data holders have …

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