Differentially Private Mixed-Type Data Generation For Unsupervised Learning
In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of GANs. This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that should satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabled emph{mixed-type data}, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.
This paper proposed a new algorithm for synthetic data generation under differential privacy. The algorithmic architecture combines autoencoder and GAN in a way that it only needs to add the DP-SGD noise to the decoder of the autoencoder and the discriminator of the GAN. This seems to be a good idea to be explored further. The authors claimed that the proposed new evaluation metrics are novel contributions of the paper but there is no discussion on why they are good metrics for evaluating the quality of synthetic datasets nor which metric should be used in what scenarios. It is unclear how the experimental results (Figure 2, 3, 4 and Table 2, 3) are interpreted. The authors mentioned comparison with DP-GAN but it is not marked in the figures the performance of DP-GAN and how its results compared with DP-auto-GAN. Please clearly state what each figure means and why the results are significant. I wonder if it is possible to have a version of GAN that also predicts the labels of data so you can use classification task as evaluation metrics, which might be easier and more interpretable. In Section 3.1, “not adding noise to the decoder” should be encoder. In Section 3.3, “do not add noise to decoder” should be encoder. The presentation of the paper needs to be improved. There are too many typos and grammar mistakes. The labels of figures in the experiment section are too small. The paper does not have a conclusion section.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Differentially Private Mixed-Type Data Generation For Unsupervised Learning
In this work we introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of GANs. This framework can be used to take in raw sensitive data, and privately train a model for generating synthetic data that should satisfy the same statistical properties as the original data. This learned model can be used to generate arbitrary amounts of publicly available synthetic data, which can then be freely shared due to the post-processing guarantees of differential privacy. Our framework is applicable to unlabled emph{mixed-type data}, that may include binary, categorical, and real-valued data. We implement this framework on both unlabeled binary data (MIMIC-III) and unlabeled mixed-type data (ADULT). We also introduce new metrics for evaluating the quality of synthetic mixed-type data, particularly in unsupervised settings.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposed a new algorithm for synthetic data generation under differential privacy. The algorithmic architecture combines autoencoder and GAN in a way that it only needs to add the DP-SGD noise to the decoder of the autoencoder and the discriminator of the GAN. This seems to be a good idea to be explored further. The authors claimed that the proposed new evaluation metrics are novel contributions of the paper but there is no discussion on why they are good metrics for evaluating the quality of synthetic datasets nor which metric should be used in what scenarios. It is unclear how the experimental results (Figure 2, 3, 4 and Table 2, 3) are interpreted. The authors mentioned comparison with DP-GAN but it is not marked in the figures the performance of DP-GAN and how its results compared with DP-auto-GAN. Please clearly state what each figure means and why the results are significant. I wonder if it is possible to have a version of GAN that also predicts the labels of data so you can use classification task as evaluation metrics, which might be easier and more interpretable. In Section 3.1, “not adding noise to the decoder” should be encoder. In Section 3.3, “do not add noise to decoder” should be encoder. The presentation of the paper needs to be improved. There are too many typos and grammar mistakes. The labels of figures in the experiment section are too small. The paper does not have a conclusion section.
assistant
This paper proposed a new algorithm for synthetic data generation under differential privacy. The algorithmic architecture combines autoencoder and GAN in a way that it only needs to add the DP-SGD noise to the decoder of the autoencoder and the discriminator of the GAN. This seems to be a good idea to be explored further. The authors claimed that the proposed new evaluation metrics are novel contributions of the paper but there is no discussion on why they are good metrics for evaluating the quality of synthetic datasets nor which metric should be used in what scenarios. It is unclear how the experimental results (Figure 2, 3, 4 and Table 2, 3) are interpreted. The authors mentioned comparison with DP-GAN but it is not marked in the figures the performance of DP-GAN and how its results compared with DP-auto-GAN. Please clearly state what each figure means and why the results are significant. I wonder if it is possible to have a version of GAN that also predicts the labels of data so you can use classification task as evaluation metrics, which might be easier and more interpretable. In Section 3.1, “not adding noise to the decoder” should be encoder. In Section 3.3, “do not add noise to decoder” should be encoder. The presentation of the paper needs to be improved. There are too many typos and grammar mistakes. The labels of figures in the experiment section are too small. The paper does not have a conclusion section.