Private and Collaborative Kaplan-Meier Estimators
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909271068770304 |
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| author | Rahimian, Shadi Kerkouche, Raouf Kurth, Ina Fritz, Mario |
| author_facet | Rahimian, Shadi Kerkouche, Raouf Kurth, Ina Fritz, Mario |
| contents | Kaplan-Meier estimators are essential tools in survival analysis, capturing the survival behavior of a cohort. Their accuracy improves with large, diverse datasets, encouraging data holders to collaborate for more precise estimations. However, these datasets often contain sensitive individual information, necessitating stringent data protection measures that preclude naive data sharing.
In this work, we introduce two novel differentially private methods that offer flexibility in applying differential privacy to various functions of the data. Additionally, we propose a synthetic dataset generation technique that enables easy and rapid conversion between different data representations. Utilizing these methods, we propose various paths that allow a joint estimation of the Kaplan-Meier curves with strict privacy guarantees. Our contribution includes a taxonomy of methods for this task and an extensive experimental exploration and evaluation based on this structure. We demonstrate that our approach can construct a joint, global Kaplan-Meier estimator that adheres to strict privacy standards ($\varepsilon = 1$) while exhibiting no statistically significant deviation from the nonprivate centralized estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_15359 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Private and Collaborative Kaplan-Meier Estimators Rahimian, Shadi Kerkouche, Raouf Kurth, Ina Fritz, Mario Cryptography and Security Applications Kaplan-Meier estimators are essential tools in survival analysis, capturing the survival behavior of a cohort. Their accuracy improves with large, diverse datasets, encouraging data holders to collaborate for more precise estimations. However, these datasets often contain sensitive individual information, necessitating stringent data protection measures that preclude naive data sharing. In this work, we introduce two novel differentially private methods that offer flexibility in applying differential privacy to various functions of the data. Additionally, we propose a synthetic dataset generation technique that enables easy and rapid conversion between different data representations. Utilizing these methods, we propose various paths that allow a joint estimation of the Kaplan-Meier curves with strict privacy guarantees. Our contribution includes a taxonomy of methods for this task and an extensive experimental exploration and evaluation based on this structure. We demonstrate that our approach can construct a joint, global Kaplan-Meier estimator that adheres to strict privacy standards ($\varepsilon = 1$) while exhibiting no statistically significant deviation from the nonprivate centralized estimator. |
| title | Private and Collaborative Kaplan-Meier Estimators |
| topic | Cryptography and Security Applications |
| url | https://arxiv.org/abs/2305.15359 |