Privacy-preserving Healthcare Informatics using Federated Learning and Blockchain
Abstract
In the Bigdata era, healthcare informatics need exploration of health records to identify hidden patterns. Machine learning and Deep learning techniques provide classification, clustering and prediction tasks. Healthcare data are processed in a centralized architecture pose a single point of failure and difficult to collaborate with different distributions of data to design a robust system. The sensitive data of the healthcare system are private and fragmented, difficult to collaborate for efficient learning models. Federated learning (FL) is a distributed preservation of privacy learning paradigm to address the data sensitiveness and silos. The model is trained with different distributions of data with distributed models to provide the global model. The sensitive data present in the local device model are not shared with the global model but only the gradients are transmitted till the convergence of the model. The privacy-preserving mechanism is essential to protect the model from privacy attacks. Differential privacy preserving is immune to privacy attacks on aggregated data of FL. The striking features of blockchain like decentralization, provenance, immutability, and finality enables a single shared ledger of the patient data and its distribution among the stakeholders with the mitigation of privacy threats. Blockchain provides the secure transaction between the local health model and the global health model during its gradient updation. Differential privacy mechanism…
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