Injecting entropy into your data in order to generate differentially private access actually typically improves model performance, rather than degrades it.

This behaviour is counterintuitive, but effectively you're enforcing a degree of regularization in a way similar to how data augmentation works. You'll get reduced training set performance and improved test set performance, as with other forms of regularization until you make it too strong.