Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks

arXiv:2211.10209v2 Announce Type: replace-cross
Abstract: 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 …

adversary applications arxiv attack attacks attributes blackbox criminal cs.cr cs.lg exploiting fairness healthcare high justice knowledge machine machine learning ml models sensitive trains vulnerable work