journalarticle

Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning

Mohammad Yaghini, B. Kulynych, C. Troncoso

arXiv.org2019-06-02 41 citations37 references

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TL;DRIt is shown that the attack success varies across different subgroups of the data, which means that even if MIA's success looks no better than random guessing over the whole population, subgroups can still be vulnerable. (Semantic Scholar)

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Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning

Abstract

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