  Recent work has shown that cell phone mobility data has the unique potential
to create accurate models for human mobility and consequently the spread of
infected diseases. While prior studies have exclusively relied on a mobile
network operator's subscribers' aggregated data in modelling disease dynamics,
it may be preferable to contemplate aggregated mobility data of infected
individuals only. Clearly, naively linking mobile phone data with health
records would violate privacy by either allowing to track mobility patterns of
infected individuals, leak information on who is infected, or both. This work
aims to develop a solution that reports the aggregated mobile phone location
data of infected individuals while still maintaining compliance with privacy
expectations. To achieve privacy, we use homomorphic encryption, validation
techniques derived from zero-knowledge proofs, and differential privacy. Our
protocol's open-source implementation can process eight million subscribers in
70 minutes.
