Censoring Representations with Multiple-Adversaries over Random Subspaces
Adversarial feature learning (AFL) is one of the promising ways for explicitly constrains neural networks to learn desired representations; for example, AFL could help to learn anonymized representations so as to avoid privacy issues. AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary. This paper proposes a novel design of the adversary, {em multiple adversaries over random subspaces} (MARS) that instantiate the concept of the {em volunerableness}. The proposed method is motivated by an assumption that deceiving an adversary could fail to give meaningful information if the adversary is easily fooled, and adversary rely on single classifier suffer from this issues. In contrast, the proposed method is designed to be less vulnerable, by utilizing the ensemble of independent classifiers where each classifier tries to predict sensitive variables from a different {em subset} of the representations. The empirical validations on three user-anonymization tasks show that our proposed method achieves state-of-the-art performances in all three datasets without significantly harming the utility of data. This is significant because it gives new implications about designing the adversary, which is important to improve the performance of AFL.
 MARS is suggested to combine multiple adversaries with different roles. Experiments show that it is suited to create censoring representations for increased anonymisation of data in the context of wearables. Experiments a are satisfying and show good performance when compared to other methods. It could be made clearer how significance is tested given the frequent usage of the term. The idea is slightly novel, and the framework otherwise state-of-the-art. The paper is well written, but can use some proof-reading. Referencing is okay.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Censoring Representations with Multiple-Adversaries over Random Subspaces
Adversarial feature learning (AFL) is one of the promising ways for explicitly constrains neural networks to learn desired representations; for example, AFL could help to learn anonymized representations so as to avoid privacy issues. AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary. This paper proposes a novel design of the adversary, {em multiple adversaries over random subspaces} (MARS) that instantiate the concept of the {em volunerableness}. The proposed method is motivated by an assumption that deceiving an adversary could fail to give meaningful information if the adversary is easily fooled, and adversary rely on single classifier suffer from this issues. In contrast, the proposed method is designed to be less vulnerable, by utilizing the ensemble of independent classifiers where each classifier tries to predict sensitive variables from a different {em subset} of the representations. The empirical validations on three user-anonymization tasks show that our proposed method achieves state-of-the-art performances in all three datasets without significantly harming the utility of data. This is significant because it gives new implications about designing the adversary, which is important to improve the performance of AFL.
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>



 MARS is suggested to combine multiple adversaries with different roles. Experiments show that it is suited to create censoring representations for increased anonymisation of data in the context of wearables. Experiments a are satisfying and show good performance when compared to other methods. It could be made clearer how significance is tested given the frequent usage of the term. The idea is slightly novel, and the framework otherwise state-of-the-art. The paper is well written, but can use some proof-reading. Referencing is okay.
assistant
 MARS is suggested to combine multiple adversaries with different roles. Experiments show that it is suited to create censoring representations for increased anonymisation of data in the context of wearables. Experiments a are satisfying and show good performance when compared to other methods. It could be made clearer how significance is tested given the frequent usage of the term. The idea is slightly novel, and the framework otherwise state-of-the-art. The paper is well written, but can use some proof-reading. Referencing is okay.