Property Unlearning: A Defense Strategy Against Property Inference Attacks. (arXiv:2205.08821v2 [cs.CR] UPDATED)

During the training of machine learning models, they may store or "learn"
more information about the training data than what is actually needed for the
prediction or classification task. This is exploited by property inference
attacks which aim at extracting statistical properties from the training data
of a given model without having access to the training data itself. These
properties may include the quality of pictures to identify the camera model,
the age distribution to reveal the target audience of …

attacks defense strategy