Membership Inference

Membership inference attacks aim to determine whether a specific data point was used to train a machine learning model, posing a significant privacy risk. Current research focuses on developing and evaluating these attacks across various model architectures, including large language models, diffusion models, and image classifiers, often employing techniques like contrastive decoding, likelihood-based comparisons, and adversarial perturbations to enhance detection accuracy. The ability to effectively perform membership inference has crucial implications for data privacy regulations, copyright protection, and the development of more privacy-preserving machine learning techniques.