Recently, machine learning models have seen considerable growth in size and popularity, lead-

ing to concerns regarding dataset privacy, especially around sensitive data containing personal information.

To address data extrapolation from model weights, various privacy frameworks ensure that the outputs of

machine learning models do not reveal their training data. However, this often results in diminished model

performance due to the necessary addition of noise to model weights. By enhancing models’ resistance to