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In this evolving era of machine learning security, membership inferenceattacks have emerged as a potent threat to the confidentiality of sensitivedata. In this attack, adversaries aim to determine whether a particular pointwas used during the training of a target model. This paper proposes a newmethod to gauge a data point's membership in a model's training set. Instead ofcorrelating loss with membership, as is traditionally done, we have leveragedthe fact that training examples generally exhibit higher confidence values whenclassified into their actual class. During training, the model is essentiallybeing 'fit' to the training data and might face particular difficulties ingeneralization to unseen data. This asymmetry leads to the model achievinghigher confidence on the training data as it exploits the specific patterns andnoise present in the training data. Our proposed approach leverages theconfidence values generated by the machine learning model. These confidencevalues provide a probabilistic measure of the model's certainty in itspredictions and can further be used to infer the membership of a given datapoint. Additionally, we also introduce another variant of our method thatallows 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-dependentattack methods.

Confidence Is All You Need for MI Attacks