Before revision:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
After revision:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy (in terms of membership inference attacks), in deep ensembles . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.