Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks

arXiv:2401.04929v2 Announce Type: replace
Abstract: Machine learning models, in particular deep neural networks, are currently an integral part of various applications, from healthcare to finance. However, using sensitive data to train these models raises concerns about privacy and security. One method that has emerged to verify if the trained models are privacy-preserving is Membership Inference Attacks (MIA), which allows adversaries to determine whether a specific data point was part of a model's training dataset. While a series of MIAs have …

applications arxiv attacks cs.ai cs.cr cs.lg data finance healthcare machine machine learning machine learning models networks neural networks privacy privacy and security security sensitive sensitive data train verify