用手机数据进行流行病学建模的标准化差异隐私框架
Merveille Koissi Savi1, Akash Yadav2, Wanrong Zhang3
1Department of Medical Oncology, Dana Farber Cancer Institute, Harvard School of Medicine, Boston, Massachusetts, United States of America.
PLOS digital health
|October 27, 2023
概括
不同隐私保护个人隐私在公共卫生的手机移动数据. 这种方法保持了流行病学指标的准确性,确保了可靠的人口层面的洞察力,而不影响匿名性.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 移动电话数据对于在COVID-19等大流行期间跟踪人类流动性至关重要.
- 保护个人位置数据的隐私对于道德公共政策和流行病学建模至关重要.
- 传统的数据聚合方法无法保证匿名性,需要先进的隐私保护技术.
研究的目的:
- 在流行病学模型中评估差异性隐私作为手机移动数据的匿名化工具.
- 评估统计量化噪声对常见流行病学指标的影响.
- 为了确定公共卫生应用程序的差异隐私的稳定性.
主要方法:
- 通过向手机位置数据添加统计噪声来应用差异隐私.
- 分析了对十个常见的流行病学指标引入的偏差.
- 使用计数过渡矩阵量化噪声水平和度量精度之间的关系.
- 开发了一个模块化软件管道,以实现可重复性.
主要成果:
- 许多流行病学指标仍然准确,接近非私人值,噪声水平低于20 (ε = 0.05每次释放).
- 不同的隐私证明了对身份识别的统计可验证的保护.
- 该研究证实了保护隐私的移动数据对公共卫生的有用性.
结论:
- 不同隐私为匿名移动数据提供了强大的解决方案,同时保留了有价值的流行病学见解.
- 这种方法平衡了个人隐私与公共卫生中人口层面数据的需求.
- 开发的框架支持进一步研究和应用保护隐私的流行病学建模.
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