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 keybased 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.
In this paper, the authors proposed a counter measure to protect collaborative training of DNN against the GAN attack in (Hitaj et al. 2017). The motivation of the paper is clear and so is the literature review. But for me the algorithm is not clearly defined and it is difficult to evaluate how the proposed procedure works. I am not saying that this is not the solution. I am just saying that the paper is not clear enough to say that it is (or it is not). From, my perspective this will make the paper a clear reject. 
I think the authors should explain a few things more clearly in order to make the paper foolproof. The first one seems to me the most clear problem with the approach proposed in the paper:
1 $\psi(c)$ defines the mapping from each class to a high dimensional vector that allows protection against the GAN attack. $\psi(c)$ is suppose to be private for each class (or user if each class belong only to one user). This is the key aspect in the paper. But if more than one user have the same class they will need to share this key. Furthermore, at test time, these keys need to be known by everyone, because the output of the neural network needs to be correlated against all keys to see which is the true label. Of course the keys can only be released after the training is completed. But the adversary can also claim to have examples from the class it is trying to attack and hence the legitimate user that generated the key will have to give the attacker the key from the training phase. For example, let assume the legitimate user only has ones from MNIST and declares that it only has one class. The attacker says it has two classes the same one that the legitimate user and some other label. In this case the legitimate user needs to share $\psi(c)$ with the attacker. Of course this sounds “fishy” and might be a way of finding who the attacker is, but there might be many cases in which it makes sense that two or more users shares the same labels and in a big system might be complicated to decide who has access to which key.
2 I do not understand the definition of $\phi(x)$. Is this embedding fixed for each user? Is this embedding the DNN? In Eq. 4 I would assume that $\phi(x)$ is the DNN and that it should be $\phi_\theta(x)$, because otherwise the equation does not make sense. But this is not clearly explained in the paper and Eq 4 makes no sense at all. In a way the solution to the maximization in Eq 4 is Theta=\infty. Also the term $\phi(x)$ is not mentioned in the paper after page 5. My take is that the authors want to maximize the inner product, but then the regularizer should go the other way around. 
3 In the paper in page 5 we can read: “Here, we emphasize the first reason why it is important to use l2-normalized class keys and embedding outputs: in this manner, the resulting classification score is by definition restricted to the range [-1; +1],” If I understand correctly the authors are dividing the inner product by ||$\psi(c)|| ||$\phi(x)||. I can see that we can easily divide by ||$\psi(c)||, but I cannot see how we can do dive by ||$\phi(x)||, if this term depends on \theta. If this term does not depend on \theta, then Eq 4 does not make sense.
To summarize, I have the impression that there are many elements in the paper that does not makes sense in the way that they are explained and that the authors need to tell the paper in a way that can be easily understood and replicated. I recommend the authors to run the paper by someone in their circle that could help them rewrite the paper in a way that is more accessible.