Model Inversion Attacks that Exploit Confidence …

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 explore two in depth: decision trees for lifestyle surveysas used on machine-learning-as-a-service systems and neuralnetworks for facial recognition.

countermeasures, investigating a privacy-aware decision tree training algorithm that is a simple variant of CART learn-ing, as well as revealing only rounded con dence values. The lesson that emerges is that one can avoid these kinds of MI attacks with negligible degradation to utility. 1. INTRODUCTION

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  Model, Countermeasures, That, Attacks, Inversion, Exploits, Model inversion attacks that exploit

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