  In this evolving era of machine learning security, membership inference
attacks have emerged as a potent threat to the confidentiality of sensitive
data. In this attack, adversaries aim to determine whether a particular point
was used during the training of a target model. This paper proposes a new
method to gauge a data point's membership in a model's training set. Instead of
correlating loss with membership, as is traditionally done, we have leveraged
the fact that training examples generally exhibit higher confidence values when
classified into their actual class. During training, the model is essentially
being 'fit' to the training data and might face particular difficulties in
generalization to unseen data. This asymmetry leads to the model achieving
higher confidence on the training data as it exploits the specific patterns and
noise present in the training data. Our proposed approach leverages the
confidence values generated by the machine learning model. These confidence
values provide a probabilistic measure of the model's certainty in its
predictions and can further be used to infer the membership of a given data
point. Additionally, we also introduce another variant of our method that
allows us to carry out this attack without knowing the ground truth(true class)
of a given data point, thus offering an edge over existing label-dependent
attack methods.
