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

Membership inference attacks seek to infer membership of individual training instances of a
model to which an adversary has black-box access through a machine learning-as-a-service …

Membership inference attacks (MIA) exploit the fact that deep learning algorithms leak
information about their training data through the learned model. It has been treated as an …

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 …

The membership inference attack refers to the attacker's purpose to infer whether the data
sample is in the target classifier training dataset. The ability of an adversary to ascertain the …

Diffusion-based generative models have shown great potential for image synthesis, but
there is a lack of research on the security and privacy risks they may pose. In this paper, we …

Membership inference (MI) attacks exploit the fact that machine learning algorithms
sometimes leak information about their training data through the learned model. In this work …

Membership inference attacks (MIAs) against machine learning models lead to serious
privacy risks for the training dataset used in the model training. The state-of-the-art defenses …

A membership inference attack (MIA) poses privacy risks for the training data of a machine
learning model. With an MIA, an attacker guesses if the target data are a member of the …

Most existing membership inference attacks (MIAs) utilize metrics (eg, loss) calculated on
the model's final state, while recent advanced attacks leverage metrics computed at various …