A zero-knowledge proof federated learning on DLT for healthcare data

Abstract

With the increasingly widespread adoption of Healthcare 4.0 practices, new challenges have arisen for the utilization of collected sensitive data. On the one hand, these data have immense potential to unlock valuable insights for personalized medicine, early disease detection, and predictive analysis thanks to the use of Artificial Intelligence. On the other hand, ensuring the protection of patient privacy is of paramount importance to maintain trust and uphold ethical practices within the healthcare system. Classical centralized learning approaches do not fit well with the privacy and security requirements imposed by the law and the sensitivity of treated data, which is why decentralized learning approaches are gaining ground. Among these, Federated Learning (FL) stands out as a viable alternative, providing greater security and performance comparable to classic centralized learning approaches. However, there are still various attacks targeting the local parameters or gradients updated by the participants. Therefore, we present our architecture based on the conjunction of Zero-Knowledge Proof, FL, and blockchain that implements also the decentralized identifier standard. The adoption of this architecture can grant the execution, management, supervision, and updating of the FL process, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the performance, robustness, and implementation costs of the proposed architecture, we develop a case study on the prediction of blood glucose levels in people with Type-1-diabetes. The results of our analysis show an improved system in terms of balance between performance privacy and security, guaranteeing high levels of verifiability, therefore proving the proposed architecture suitable for most of the FL processes needed in the healthcare field.