Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Jingyang Zhang, Yiran Chen, Hai Li
20222022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)20 citationsDOI
Abstract
Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users’ private training images even when the training batch size is large. Code is available at https://github.com/zjysteven/PrivayAttack_AT_FL.
Topics & Concepts
Adversarial systemComputer scienceLeakage (economics)Training (meteorology)Computer securityTraining setArtificial intelligenceInternet privacyEconomicsMeteorologyMacroeconomicsPhysicsAdversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataCryptography and Data Security