Weibull分布参数生存分析的隐私保护机制
Privacy-Preserving Mechanisms for Parametric Survival Analysis with Weibull Distribution
摘要：
生存分析研究了直到感兴趣的事件发生的时间的统计特性。它通常用于研究医学治疗的有效性或人口的寿命。然而，生存分析可能会泄露数据集中个体的机密信息。最先进的技术在发布结果时应用ad-hoc隐私保护机制来保护隐私。这些技术通常发布经过消毒和随机化的答案，这些答案承诺保护数据集中的个人隐私，但没有提供任何正式的隐私保护机制。在本文中，我们提出了私人机制的参数生存分析与威布尔分布。我们证明，我们提出的机制实现了差分隐私，一个强大的和严格的定义的隐私保护。我们的机制利用本地敏感性的属性，精心设计的效用函数，使我们能够发布高精度的威布尔分布的参数。我们的实验研究表明，我们的机制可以发布有用的答案，并在真实数据集上优于其他差异隐私技术。
英文摘要：
Survival analysis studies the statistical properties of the time until an event of interest occurs. It has been commonly used to study the effectiveness of medical treatments or the lifespan of a population. However, survival analysis can potentially leak confidential information of individuals in the dataset. The state-of-the-art techniques apply ad-hoc privacy-preserving mechanisms on publishing results to protect the privacy. These techniques usually publish sanitized and randomized answers which promise to protect the privacy of individuals in the dataset but without providing any formal mechanism on privacy protection. In this paper, we propose private mechanisms for parametric survival analysis with Weibull distribution. We prove that our proposed mechanisms achieve differential privacy, a robust and rigorous definition of privacy-preservation. Our mechanisms exploit the property of local sensitivity to carefully design a utility function which enables us to publish parameters of Weibull distribution with high precision. Our experimental studies show that our mechanisms can publish useful answers and outperform other differentially private techniques on real datasets.
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