Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare.

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. To address this issue, the authors propose a hierarchical model within the Hyperledger Fabric enterprise application, focusing on the healthcare sector as a use case. This model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. The initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. The primary objective of the experiment is to test and improve the network's performance using this model. The model's performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. The benchmarking tool used for this assessment is Hyperledger Caliper, which measures indicators such as success and failure rates, throughput, and latency. Currently, the research focuses only on testing the model's scalability using patient data.

ProBlock: a novel approach for fake news detection.

The world is diving deeper into the digital age, and the sources of first information are moving towards social media and online news portals. The chances of being misinformed increase multifold as our reliance on sources of information are getting ambiguous. Traditional news sources followed strict codes of practice to verify stories, whereas today, users can upload news items on social media and unverified portals without proving their veracity. The absence of any determinants of such news articles' truthfulness on the Internet calls for a novel approach to determine the realness quotient of unverified news items by leveraging technology. This study presents a dynamic model with a secure voting system, where news reviewers can provide feedback on news, and a probabilistic mathematical model is used for predicting the truthfulness of the news item based on the feedback received. A blockchain-based model, ProBlock is proposed; so that correctness of information propagated is ensured.

Applying CHAID algorithm to investigate critical attributes of secured interoperable health data exchange.

With enhanced interoperability in healthcare environment the exchange of electronic health records (EHRs), both intra and inter organisations, has increased manifold. Sharing of the EHR creates room for illegal disclosures and confidentiality breaches. Interoperable healthcare is a complex system with many independent components. To design a secured framework for such a system, one need to understand the most important security attributes and predict various dependencies among them. The security attributes selected for statistical analysis are taken from the real-time study of patient-doctor relationship existing in any hospital or clinic. Hospitals with functional EHR-systems are the prerequisite of this study. The dependencies in the obtained data are generated through classification technique, chi-squared automatic interaction detection (CHAID). The decision tree obtained is analysed and verified using regression. The paper enabled the identification of the salient feature controlling which would maximally reduce security threats while sharing EHRs in interoperable healthcare environment.