How to Combine Membership-Inference Attacks on Multiple Updated Models. (arXiv:2205.06369v1 [cs.LG])

A large body of research has shown that machine learning models are
vulnerable to membership inference (MI) attacks that violate the privacy of the
participants in the training data. Most MI research focuses on the case of a
single standalone model, while production machine-learning platforms often
update models over time, on data that often shifts in distribution, giving the
attacker more information. This paper proposes new attacks that take advantage
of one or more model updates to improve MI. A …

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