Property inference attacks reveal statistical properties about a training set
but are difficult to distinguish from the intrinsic purpose of statistical
machine learning, namely to produce models that capture statistical properties
about a distribution. Motivated by Yeom et al.’s membership inference
framework, we propose a formal and general definition of property inference
attacks. The proposed notion describes attacks that can distinguish between
possible training distributions, extending beyond previous property inference
attacks that infer the ratio of a particular type of data in the training data
set such as the proportion of females. We show how our definition captures
previous property inference attacks as well as a new attack that can reveal the
average node degree or clustering coefficient of a training graph. Our
definition also enables a theorem that connects the maximum possible accuracy
of inference attacks distinguishing between distributions to the effective size
of dataset leaked by the model. To quantify and understand property inference
risks, we conduct a series of experiments across a range of different
distributions using both black-box and white-box attacks. Our results show that
inexpensive attacks are often as effective as expensive meta-classifier
attacks, and that there are surprising asymmetries in the effectiveness of
attacks. We also extend the state-of-the-art property inference attack to work
on convolutional neural networks, and propose techniques to help identify
parameters in a model that leak the most information, thus significantly
lowering resource requirements for meta-classifier attacks.

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