  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 different
privacy requirements and data points of data holders with lower requirements
can contribute more information to the training process of the ML models. To
account for this need, we propose two novel methods based on the Private
Aggregation of Teacher Ensembles (PATE) framework to support the training of ML
models with individualized privacy guarantees. We formally describe the
methods, provide a theoretical analysis of their privacy bounds, and
experimentally evaluate their effect on the final model's utility using the
MNIST, SVHN, and Adult income datasets. Our empirical results show that the
individualized privacy methods yield ML models of higher accuracy than the
non-individualized baseline. Thereby, we improve the privacy-utility trade-off
in scenarios in which different data holders consent to contribute their
sensitive data at different individual privacy levels.
