Differential Privacy and What It Protects

Differential privacy protects against membership inference attacks—someone trying to figure out whether your data was in the training set—and attribute inference, where attackers reconstruct specific details about you by querying the model repeatedly. It's less about hiding that a dataset was used and more about guaranteeing that even an adversary with unlimited computing power can't reliably extract facts about individuals.