We also give unique insights into its effectiveness.

We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).

Among all the contributing factors, the quality and selection of data is becoming increasingly recognized for its importance in training LLMs effectively.

An adversary may be able to "steal" the model by querying for counterfactuals and training a