Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation

Jianfeng Chi
Jianfeng Chi
Yuan Tian
Yuan Tian

NIPS 2020, 2020.

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Experimental results show that the adversarial representation learning approaches are effective against attribute inference attacks and often achieve the best trade-off in terms of attribute obfuscation and accuracy maximization

Abstract:

Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory behind these methods....More

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