Improved Generalization Guarantees in Restricted Data Models. (arXiv:2207.10668v1 [cs.CR])

Differential privacy is known to protect against threats to validity incurred
due to adaptive, or exploratory, data analysis — even when the analyst
adversarially searches for a statistical estimate that diverges from the true
value of the quantity of interest on the underlying population. The cost of
this protection is the accuracy loss incurred by differential privacy. In this
work, inspired by standard models in the genomics literature, we consider data
models in which individuals are represented by a sequence of attributes with
the property that where distant attributes are only weakly correlated. We show
that, under this assumption, it is possible to “re-use” privacy budget on
different portions of the data, significantly improving accuracy without
increasing the risk of overfitting.



Related post