Differentially Private Survival Function Estimation
Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator. Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern. We propose a first differentially private estimator of the survival function and show that it can be easily extended to provide differentially private confidence intervals and test statistics without spending any extra privacy budget. We further provide extensions for differentially private estimation of the competing risk cumulative incidence function. Using nine real-life clinical datasets, we provide empirical evidence that our proposed method provides good utility while simultaneously providing strong privacy guarantees.
The authors present a method that guarantees differential privacy for survival function estimates using Kaplan-Meier. They show their results in nine different datasets, and their survival curves are similar to the original survival curves while protecting patient privacy. There have been a number of survival functions with differential privacy discussion since Nguyen and Hui. Please check other papers that cite it. Overall, the writing is choppy, where ideas that should be a single sentence is separated into multiple sentences. For example, in the first few sentences of the Introduction, we have *An adversary, with only access to the published estimates... Effectively leading to the disclosure of sensitive information*, which should just be a single sentence. There are many instances of this throughout the paper, and they should be edited prior to re-submission. The authors claim that GANs do not work well for their application, but it would be more convincing if there are citations or experiments tha t can back this up. The authors write that their method *provides good utility*. They should explicitly state the metric of evaluation, as well as the increase in performance clearly in both the abstract and the introduction. Section 2.1 introducing the survival function is not necessary. Maybe just 2-3 sentences on the K-M estimator.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Differentially Private Survival Function Estimation
Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator. Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern. We propose a first differentially private estimator of the survival function and show that it can be easily extended to provide differentially private confidence intervals and test statistics without spending any extra privacy budget. We further provide extensions for differentially private estimation of the competing risk cumulative incidence function. Using nine real-life clinical datasets, we provide empirical evidence that our proposed method provides good utility while simultaneously providing strong privacy guarantees.
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>



The authors present a method that guarantees differential privacy for survival function estimates using Kaplan-Meier. They show their results in nine different datasets, and their survival curves are similar to the original survival curves while protecting patient privacy. There have been a number of survival functions with differential privacy discussion since Nguyen and Hui. Please check other papers that cite it. Overall, the writing is choppy, where ideas that should be a single sentence is separated into multiple sentences. For example, in the first few sentences of the Introduction, we have *An adversary, with only access to the published estimates... Effectively leading to the disclosure of sensitive information*, which should just be a single sentence. There are many instances of this throughout the paper, and they should be edited prior to re-submission. The authors claim that GANs do not work well for their application, but it would be more convincing if there are citations or experiments tha t can back this up. The authors write that their method *provides good utility*. They should explicitly state the metric of evaluation, as well as the increase in performance clearly in both the abstract and the introduction. Section 2.1 introducing the survival function is not necessary. Maybe just 2-3 sentences on the K-M estimator.
assistant
The authors present a method that guarantees differential privacy for survival function estimates using Kaplan-Meier. They show their results in nine different datasets, and their survival curves are similar to the original survival curves while protecting patient privacy. There have been a number of survival functions with differential privacy discussion since Nguyen and Hui. Please check other papers that cite it. Overall, the writing is choppy, where ideas that should be a single sentence is separated into multiple sentences. For example, in the first few sentences of the Introduction, we have *An adversary, with only access to the published estimates... Effectively leading to the disclosure of sensitive information*, which should just be a single sentence. There are many instances of this throughout the paper, and they should be edited prior to re-submission. The authors claim that GANs do not work well for their application, but it would be more convincing if there are citations or experiments tha t can back this up. The authors write that their method *provides good utility*. They should explicitly state the metric of evaluation, as well as the increase in performance clearly in both the abstract and the introduction. Section 2.1 introducing the survival function is not necessary. Maybe just 2-3 sentences on the K-M estimator.