Protecting patient privacy in survival analyses
Protecting patient privacy in survival analyses
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DOI：
10.1093/jamia/ocz195
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发表时间：
2020-03-01
影响因子：
6.4
通讯作者：
Ohno-Machado, Lucila
中科院分区：
文献类型：
--
作者：
Bonomi, Luca;Jiang, Xiaoqian;Ohno-Machado, Lucila
目的：生存分析是许多医疗保健应用的基础，其中计算一组患者的“生存”概率（例如，摆脱某种疾病的时间，死亡时间）以指导临床决策。广泛应用于生物医学研究和医疗保健领域。然而，频繁地分享确切的生存曲线可能会泄露个别患者的信息，因为对手可能会推断出某个感兴趣的人是某项研究的参与者或某一特定群体的参与者。因此，研究在生存分析中保护患者隐私的方法势在必行。材料和方法：我们开发了一个基于差分隐私的正式模型的框架，它提供了针对知识渊博的对手的可证明的隐私保护。我们展示了广泛使用的Kaplan-Meier非参数生存模型的隐私保护解决方案的性能。结果：我们对流行的流行病学数据集和合成数据集的隐私保护框架的有效性和降低的隐私风险进行了实证评估。结果表明，与非私有方法相比，我们的方法显著降低了隐私风险，同时保留了生存曲线的效用。讨论：提出的框架证明了进行隐私保护生存分析的可行性。我们讨论了未来的研究方向，以进一步提高我们提出的解决方案在生物医学研究应用中的有用性。结论：结果表明，我们提出的隐私保护方法在保留生存分析有用性的同时提供了强有力的隐私保护。
Objective: Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis.Materials and Methods: We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. Results: We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset.Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves.Discussion: The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications.Conclusion: The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.