  To address the issue that deep neural networks (DNNs) are vulnerable to model
inversion attacks, we design an objective function, which adjusts the
separability of the hidden data representations, as a way to control the
trade-off between data utility and vulnerability to inversion attacks. Our
method is motivated by the theoretical insights of data separability in neural
networking training and results on the hardness of model inversion.
Empirically, by adjusting the separability of data representation, we show that
there exist sweet-spots for data separability such that it is difficult to
recover data during inference while maintaining data utility.
