Distributed Privacy-Preserving Insurance Insight-Sharing Platform

15/12/2020 - 15/06/2021

Type of event : Privacy Protection & Cryptography, Machine Learning, Finance

Partner: Swiss Re

Partner contact: Sebastian Eckhardt

EPFL laboratory: Laboratory for Data Security (LDS)

EPFL contact: Prof. Jean-Pierre Hubaux, Juan Troncoso, Romain Bouyé

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.