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通过投影隐私:通过随机特征的投影来估计联邦人口密度估计

Zixiao Zong¹, Mengwei Yang¹, Justin Ley¹

¹University of California, Irvine, Irvine, CA, USA.

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium

|January 23, 2024

概括

本研究引入了联邦随机里叶特征内核密度估计 (KDE) 来从移动设备位置数据中估计人口密度. 这种保护隐私的方法使数据保持本地化,比现有的技术提供了更好的公用事业隐私平衡.

科学领域:

计算机科学计算机科学
数据科学数据科学数据科学
移动计算移动计算

背景情况:

人口密度估计通常使用核心密度估计 (KDE) 与集中位置数据.
集中式数据收集引发了大量用户隐私问题.
现有的隐私保护方法可能会损害估计准确性.

研究的目的:

为人口密度估计提出一个保护隐私的联邦内核密度估计 (KDE) 框架.
确保用户位置数据保留在设备上,同时提供针对恶意服务器的隐私保证.
在估计实用性和用户隐私之间实现优越的权衡.

主要方法:

开发了一个联合随机里埃特征 (RFF) KDE 方法.
利用随机特征表示来将用户位置数据投射到空间移位的基础函数上.
确保了不可逆转的投影,以增强隐私,防止精确的用户定位.

主要成果:

与 GeoInd.Ind.等最先进的方法相比,联合的 RFF KDE 展示了优越的实用程序隐私权权权衡.
调整每个用户的基本功能的数量进一步优化了隐私与实用性的平衡.
基于面积单位大小和内核带宽来得本地化分析界限.

结论:

联合RFF KDE提供了一种有效且保护隐私的解决方案,用于使用众包移动位置数据进行人口密度估计.
该方法成功地平衡了准确密度估计的需要和强大的用户隐私.
该框架代表了基于位置的安全数据分析的重大进步.

关键词:

联邦化分析 (Federated Analytics) 是一个统一的分析系统.核密度估计 (KDE) 是指核密度的估计.人口建模的人口建模隐私隐私隐私隐私隐私隐私随机里埃特征是随机里埃特征.

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