With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients’ data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

Industrial blockchain is believed to be a promising technology for modern industries. Despite the capacity of blockchain to protect users’ privacy, its inherent transparency renders data provenance traceable, a situation that does not fully guarantee the confidentiality of users and data sources. Users’ preferences, habits, identity, and other sensitive privacy information can be analyzed by adversaries after collecting a series of users’ requests. This paper integrates existing oracle technologies and proposes a distributed oracle architecture DISOC based on TOTP and ring signature to overcome the privacy shortcomings of traditional oracles. DISOC can be used to implement trustworthy off-chain data sharing from different data source with strong privacy-preservation. It can effectively protect the privacy of data sources while protecting user privacy to mitigate potential data leakage issues. After analysis and testing, DISOC can perfectly meet the data sharing needs and privacy preservation of multiple data sources.