Differentially Private Change-Point Detection
Differentially Private Change-Point Detection
复制标题
差分隐私变化点检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Wanrong Zhang
中科院分区：
文献类型：
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作者：
Rachel Cummings;Sara Krehbiel;Y. Mei;Rui Tuo;Wanrong Zhang
变化点检测问题旨在识别数据流中未知变化点 k* 处的分布变化。这个问题出现在许多涉及个人数据的重要实际环境中，包括生物监视、故障检测、金融、信号检测和安全系统。差异隐私领域提供的数据分析工具可以提供强大的最坏情况隐私保证。我们通过差分隐私的视角研究变点问题的统计问题。我们给出了在线和离线变化点检测的私有算法，从理论上分析了这些算法，然后对这些结果进行了实证验证。
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data. This problem appears in many important practical settings involving personal data, including biosurveillance, fault detection, finance, signal detection, and security systems. The field of differential privacy offers data analysis tools that provide powerful worst-case privacy guarantees. We study the statistical problem of change-point problem through the lens of differential privacy. We give private algorithms for both online and offline change-point detection, analyze these algorithms theoretically, and then provide empirical validation of these results.