Center for Effective Global Action, UC Berkeley, Berkeley, 94704, USA.
School of Information, UC Berkeley, Berkeley, 94704, USA.
Sci Rep. 2024 Nov 19;14(1):28565. doi: 10.1038/s41598-024-79561-2.
Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response.
个人移动数据来自移动电话和其他传感器，越来越多地用于在大流行病、自然灾害和其他人道主义危机期间为决策提供信息。然而，即使是聚合的移动轨迹也可能向潜在的恶意行为者揭示个人移动的私人信息。本文开发并测试了一种发布私人移动数据的方法，该方法为基础主体的隐私提供了正式保证。具体来说，我们：（1）引入了一种用于构建差分隐私移动矩阵的算法，并推导出该算法的隐私和准确性边界；（2）使用来自阿富汗和卢旺达移动电话运营商的真实世界数据，展示了该算法如何能够在两个高风险政策决策中使用私人移动数据：大流行病应对和人道主义援助的分配；（3）讨论了在实施该方法时需要做出的实际决策，例如如何最优地平衡隐私和准确性。总的来说，这些结果可以帮助在人道主义应对中负责任地使用私人移动数据。
