Title: Key Protected Classification for GAN Attack Resilient Collaborative Learning. Abstract: Large-scale publicly available datasets play a fundamental role in training deep learning models. However, large-scale
datasets are difficult to collect in problems that involve processing of sensitive information.
Collaborative learning techniques provide a privacy-preserving solution in such cases, by enabling
training over a number of private datasets that are not shared by their owners.
Existing collaborative learning
techniques, combined with differential privacy, are shown to be resilient against a passive
adversary which tries to infer the training data only from the model parameters. However, recently, it has
been shown that the existing collaborative learning techniques are vulnerable to an active adversary that runs a GAN
attack during the learning phase. In this work, we propose a novel key-based collaborative learning technique that is
resilient against such GAN attacks. For this purpose, we present a collaborative learning formulation in which class scores 
are protected by class-specific keys, and therefore, prevents a GAN attack. We also show that
very high dimensional class-specific keys can be utilized to improve robustness against attacks, without increasing the model complexity. 
Our experimental results on two popular datasets, MNIST and AT&T Olivetti Faces, demonstrate the effectiveness of the proposed technique
against the GAN attack. To the best of our knowledge, the proposed approach is the first collaborative learning
formulation that effectively tackles an active adversary, and, unlike model corruption or differential privacy formulations,
our approach does not inherently feature a trade-off between model accuracy and data privacy.