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 …

Models can expose sensitive information about their training data. In an attribute inference
attack, an adversary has partial knowledge of some training records and access to a model …

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

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How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …

We quantitatively investigate how machine learning models leak information about the
individual data records on which they were trained. We focus on the basic membership …

Membership Inference Attacks (MIAs) aim to determine the presence of a record in a
machine learning model's training data by querying the model. Recent work has …

Machine learning models have been shown to leak information violating the privacy of their
training set. We focus on membership inference attacks on machine learning models which …

When building machine learning models using sensitive data, organizations should ensure
that the data processed in such systems is adequately protected. For projects involving …