Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. (arXiv:2204.00032v1 [cs.CR])

We introduce a new class of attacks on machine learning models. We show that
an adversary who can poison a training dataset can cause models trained on this
dataset to leak significant private details of training points belonging to
other parties. Our active inference attacks connect two independent lines of
work targeting the integrity and privacy of machine learning training data.

Our attacks are effective across membership inference, attribute inference,
and data extraction. For example, our targeted attacks can poison …

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