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 propose a differentially private estimator for survival functions, their confidence intervals and test statistics. An Extension for the competing risk cumulative incidence function is also presented. Experiments on nine datasets are presented. The algorithm is simple, readily extends to KM*s confidence interval, the logrank test statistic and the competing risk cumulative function. Theoretical results follow from the count nature of the data (number of events and risk set size). From the experiments it is noticeable that the the differentially private estimator consistently overestimates risk (see Figure 1 nad Table 2, left) and the logrank test statistic (table 2, right). This is not addressed in the paper. The work, although interesting, is of very limited value to the ICLR community.
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 propose a differentially private estimator for survival functions, their confidence intervals and test statistics. An Extension for the competing risk cumulative incidence function is also presented. Experiments on nine datasets are presented. The algorithm is simple, readily extends to KM*s confidence interval, the logrank test statistic and the competing risk cumulative function. Theoretical results follow from the count nature of the data (number of events and risk set size). From the experiments it is noticeable that the the differentially private estimator consistently overestimates risk (see Figure 1 nad Table 2, left) and the logrank test statistic (table 2, right). This is not addressed in the paper. The work, although interesting, is of very limited value to the ICLR community.
assistant
The authors propose a differentially private estimator for survival functions, their confidence intervals and test statistics. An Extension for the competing risk cumulative incidence function is also presented. Experiments on nine datasets are presented. The algorithm is simple, readily extends to KM*s confidence interval, the logrank test statistic and the competing risk cumulative function. Theoretical results follow from the count nature of the data (number of events and risk set size). From the experiments it is noticeable that the the differentially private estimator consistently overestimates risk (see Figure 1 nad Table 2, left) and the logrank test statistic (table 2, right). This is not addressed in the paper. The work, although interesting, is of very limited value to the ICLR community.