Distributed Privacy-Preserving Insurance Insight-Sharing Platform

The collection and analysis of risk data are essential for the insurance-business model. The models for evaluating risk and predicting events that trigger insurance policies are based on knowledge derived from risk data.
The purpose of this project is to assess the scalability and flexibility of the software-based secure computing techniques in an insurance benchmarking scenario and to demonstrate the range of analytics capabilities they provide. These techniques offer provable technological guarantees that only authorized users can access the global models (fraud and loss models) based on the data of a network of collaborating organizations. The system relies on a fully distributed architecture without a centralized database, and implements advanced privacy-protection techniques based on multiparty homomorphic encryption, which makes it possible to efficiently compute machine-learning models on encrypted distributed data.