Have it your way: Individualized Privacy Assignment for DP-SGD. (arXiv:2303.17046v1 [cs.LG])

When training a machine learning model with differential privacy, one sets a
privacy budget. This budget represents a maximal privacy violation that any
user is willing to face by contributing their data to the training set. We
argue that this approach is limited because different users may have different
privacy expectations. Thus, setting a uniform privacy budget across all points
may be overly conservative for some users or, conversely, not sufficiently
protective for others. In this paper, we capture these …

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