Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks. (arXiv:2211.10209v1 [cs.LG])

Machine learning (ML) models have been deployed for high-stakes applications,
e.g., healthcare and criminal justice. Prior work has shown that ML models are
vulnerable to attribute inference attacks where an adversary, with some
background knowledge, trains an ML attack model to infer sensitive attributes
by exploiting distinguishable model predictions. However, some prior attribute
inference attacks have strong assumptions about adversary's background
knowledge (e.g., marginal distribution of sensitive attribute) and pose no more
privacy risk than statistical inference. Moreover, none of …

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