  With an increase in low-cost machine learning APIs, advanced machine learning
models may be trained on private datasets and monetized by providing them as a
service. However, privacy researchers have demonstrated that these models may
leak information about records in the training dataset via membership inference
attacks. In this paper, we take a closer look at another inference attack
reported in literature, called attribute inference, whereby an attacker tries
to infer missing attributes of a partially known record used in the training
dataset by accessing the machine learning model as an API. We show that even if
a classification model succumbs to membership inference attacks, it is unlikely
to be susceptible to attribute inference attacks. We demonstrate that this is
because membership inference attacks fail to distinguish a member from a nearby
non-member. We call the ability of an attacker to distinguish the two (similar)
vectors as strong membership inference. We show that membership inference
attacks cannot infer membership in this strong setting, and hence inferring
attributes is infeasible. However, under a relaxed notion of attribute
inference, called approximate attribute inference, we show that it is possible
to infer attributes close to the true attributes. We verify our results on
three publicly available datasets, five membership, and three attribute
inference attacks reported in literature.
