  Large-scale datasets play a fundamental role in training deep learning
models. However, dataset collection is difficult in domains that involve
sensitive information. Collaborative learning techniques provide a
privacy-preserving solution, by enabling training over a number of private
datasets that are not shared by their owners. However, recently, it has been
shown that the existing collaborative learning frameworks are vulnerable to an
active adversary that runs a generative adversarial network (GAN) attack. In
this work, we propose a novel classification model that is resilient against
such attacks by design. More specifically, we introduce a key-based
classification model and a principled training scheme that protects class
scores by using class-specific private keys, which effectively hide the
information necessary for a GAN attack. We additionally show how to utilize
high dimensional keys to improve the robustness against attacks without
increasing the model complexity. Our detailed experiments demonstrate the
effectiveness of the proposed technique. Source code is available at
https://github.com/mbsariyildiz/key-protected-classification.
