Privately Connecting Mobility to Infectious Diseases via Applied Cryptography

TL;DR

This paper presents a privacy-preserving protocol using cryptography to analyze mobile phone mobility data of infected individuals, enabling disease modeling without compromising individual privacy.

Contribution

It introduces a novel cryptographic protocol combining homomorphic encryption, zero-knowledge proofs, and differential privacy for secure mobility data aggregation.

Findings

Processes eight million subscribers in 70 minutes

Ensures privacy while aggregating mobility data of infected individuals

Provides a practical open-source implementation

Abstract

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.