Auror: defending against poisoning attacks in collaborative deep learning systems
Deep learning in a collaborative setting is emerging as a corner stone of many upcoming applications, wherein untrusted users collaborate to generate more accurate models. From the security perspective, this opens collaborative deep learning to poisoning attacks, wherein adversarial users deliberately alter their inputs to mis train the model. These attacks are known for machine learning systems in general, but their impact on new deep learning systems is not well established. We investigate the setting of indirect collaborative deep learning a form of practical deep learning wherein users submit masked features rather than direct data. Indirect collaborative deep learning is preferred over direct, because it distributes the cost of computation and can be made privacy preserving. In this paper, we study the susceptibility of collaborative deep learning systems to adversarial poisoning attacks. Specifically, we obtain the following empirical results on 2 popular datasets for handwritten images (MNIST) and traffic signs (GTSRB) used in auto driving cars. For collaborative deep learning systems, we demonstrate that the attacks have 99% success rate for misclassifying specific target data while poisoning only 10% of the entire training dataset. As a defense, we propose Auror, a system that detects malicious users and generates an accurate model. The accuracy under the deployed defense on practical datasets is nearly unchanged when operating in the absence of attacks. The accuracy of a model trained using Auror drops by only 3% even when 30% of all the users are adversarial. Auror provides a strong guarantee against evasion; if the attacker tries to evade, its attack effectiveness is bounded.
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