All projects Privacy Protection & CryptographyBlockchains & Smart ContractsSoftware VerificationDevice & System SecurityMachine LearningFinanceHealthGovernment & HumanitarianCritical InfrastructureDigital Information
Jan 2022 → Dec 2023 Project

PAIDIT: Private Anonymous Identity for Digital Transfers

Partner: ICRC, funded by HAC
Partner contact: TBD
EPFL laboratory: Decentralized Distributed Systems Laboratory (DEDIS)
EPFL contact: Prof. Bryan Ford

To serve the 80 million forcibly-displaced people around the globe, direct cash assistance is gaining acceptance. ICRC’s beneficiaries often do not have, or do not want, the ATM cards or mobile wallets normally used to spend or withdraw cash digitally, because issuers would subject them to privacy-invasive identity verification and potential screening against sanctions and counterterrorism watchlists. On top of that, existing solutions increase the risk of data leaks or surveillance induced by the many third parties having access to the data generated in the transactions. The proposed research focuses on the identity, account, and wallet management challenges in the design of a humanitarian cryptocurrency or token intended to address the above problems.

TopicsPrivacy Protection & CryptographyBlockchains & Smart ContractsDevice & System SecurityFinanceGovernment & Humanitarian

Dec 2020 → Jun 2021 Project

Distributed Privacy-Preserving Insurance Insight-Sharing Platform

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.

TopicsPrivacy Protection & CryptographyMachine LearningFinance