Before revision:  At the same time, ensemble learning has also been proposed to mitigate privacy leakage in terms of membership inference (MI), where the goal of an attacker is to infer whether a particular data sample has been used to train a target model .
After revision:  At the same time, ensemble learning has also been used to defend against membership inference attacks that undermine privacy .