Conference Paper

A Blockchain-Based Privacy Sensitive Data Acquisition Scheme During Pandemic Through the Facilitation of Federated Learning

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Abstract

New diseases (e.g., monkeypox) are showing up and taking the form of a pandemic within a short time. Early detection can assist in reducing the spread. However, because of privacy-sensitive data, users do not share it continually. Thus, it becomes challenging to employ modern technologies (e.g., deep learning). Moreover, cyber threats encircle both communication and data. This paper introduces a blockchain-based data acquisition scheme during the pandemic in which federated learning (FL) is employed to assemble privacy-sensitive data as a form of the trained model instead of raw data. A secure training scheme is designed to mitigate cyber threats (e.g., man-in-the-middle-attack). An experimental environment is formulated based on a recent pandemic (i.e., monkeypox) to illustrate the feasibility of the proposed scheme.

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