Privately Connecting Mobility to Infectious Diseases via Applied Cryptography
This work addresses privacy concerns in public health modeling for researchers and policymakers, though it is incremental as it builds on existing methods for data aggregation.
The paper tackled the problem of linking mobile phone mobility data with health records to model disease spread while preserving privacy, achieving a solution that processes eight million subscribers in 70 minutes using cryptographic techniques.
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.