Membership inference (MI) attacks affect user privacy by inferring whether given data
samples have been used to train a target learning model, eg, a deep neural network. There …

In membership inference attacks (MIAs), an adversary observes the predictions of a model to
determine whether a sample is part of the model's training data. Existing MIA defenses …

Deep learning models often raise privacy concerns as they leak information about their
training data. This enables an adversary to determine whether a data point was in a model's …

Transfer learning (TL) has been demonstrated to improve DNN model performance when
faced with a scarcity of training samples. However, the suitability of TL as a solution to …

We demonstrate how a target model's generalization gap leads directly to an effective
deterministic black box membership inference attack (MIA). This provides an upper bound …

Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and
code repositories that can greatly facilitate the development of ML models. Today, even …

Recently, membership inference attacks (MIAs) against machine learning models have been
proposed. Using MIAs, adversaries can inference whether a data record is in the training set …

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