  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.
