Pandemic Response Modelling with Privacy Enhancing Technology: a place-centric approach

This challenge seeks to develop Privacy Enhancing Technologies (PETs) that may unlock the sharing of sensitive data between health care providers, and use a federated learning approach to model infection risk.

Traditional machine learning techniques can result in a model that embeds sensitive data. Federated learning approaches struggle to promote learning from weak signals that occur in a single node to the aggregate model. Pandemic forecasting relies on inferring from weak signals within sensitive data, making the problem challenging.

Our approach will focus on prediction based on individuals' activity and location patterns.

We propose shifting the privacy burden to the feature engineering stage. This allows the use of simpler approaches to federated learning that need only handle privacy preserving features, rather than provide privacy preservation themselves.

We will do this by applying two key techniques: first through Homomorphic encryption, a cryptographic technique that allows computations to be performed on encrypted data. So that data passed between nodes and a central server are encrypted. Second, we will introduce noise into the system to mask the impact of an individual. The purpose of adding noise is to thwart attempts to discover data about an individual, a concept mathematically defined as _differential privacy_.

We will take a place-centric approach to this challenge. The features that we will focus on will be the locations of infected individuals. While our approach is implemented in terms of geospatial data, it could trivially be adapted to produce features based on other graph data, for example, the population contact graph rather than the place visitation graph.