Transcription of Model Inversion Attacks that Exploit Confidence …
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Model Inversion Attacks that Exploit Confidence Informationand Basic CountermeasuresMatt FredriksonCarnegie Mellon UniversitySomesh JhaUniversity of Wisconsin MadisonThomas RistenpartCornell TechABSTRACTM achine-learning (ML) algorithms are increasingly utilizedin privacy-sensitive applications such as predicting lifestylechoices, making medical diagnoses, and facial recognition. Ina Model Inversion attack, recently introduced in a case studyof linear classifiers in personalized medicine by Fredriksonet al. [13], adversarial access to an ML Model is abusedto learn sensitive genomic information about Model Inversion Attacks apply to settings outsidetheirs, however, is develop a new class of Model Inversion attack thatexploits confidence values revealed along with new Attacks are applicable in a variety of settings, andwe explor
a model inversion attack, recently introduced in a case study of linear classi ers in personalized medicine by Fredrikson et al. [13], adversarial access to an ML model is abused to learn sensitive genomic information about individuals. Whether model inversion attacks apply to settings outside theirs, however, is unknown.
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