  Increasing use of machine learning (ML) technologies in privacy-sensitive
domains such as medical diagnoses, lifestyle predictions, and business
decisions highlights the need to better understand if these ML technologies are
introducing leakage of sensitive and proprietary training data. In this paper,
we focus on model inversion attacks where the adversary knows non-sensitive
attributes about records in the training data and aims to infer the value of a
sensitive attribute unknown to the adversary, using only black-box access to
the target classification model. We first devise a novel confidence score-based
model inversion attribute inference attack that significantly outperforms the
state-of-the-art. We then introduce a label-only model inversion attack that
relies only on the model's predicted labels but still matches our confidence
score-based attack in terms of attack effectiveness. We also extend our attacks
to the scenario where some of the other (non-sensitive) attributes of a target
record are unknown to the adversary. We evaluate our attacks on two types of
machine learning models, decision tree and deep neural network, trained on
three real datasets. Moreover, we empirically demonstrate the disparate
vulnerability of model inversion attacks, i.e., specific groups in the training
dataset (grouped by gender, race, etc.) could be more vulnerable to model
inversion attacks.
