ABSTRACT
Information related to Covid-19 either it is vaccination status of the country or the active Covid-19 cases both are the confidential matters. The privacy is utmost important concern in pandemic situation to secure access of patient vaccine data. Blockchain technique is one of the good techniques that affirm the privacy and data security. The consensus mechanisms in blockchain confirm that data stored in it, is authentic and secured. Proof of Work is one of the consensus algorithms, where miners in the blockchain network solves the puzzle and receive the reward accordingly. The difficulty level of the puzzle decides the security of the data in the network. Hence, this paper proposes blockchain based framework to store the vaccination data of patient by enhancing security using proof of work consensus algorithm. The performance of the proposed framework is measured on different level of difficulties, corresponding to time. The result shows that higher the difficulty level, take more time to solve the puzzle, results in more secure data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations. © 2021 IEEE.
ABSTRACT
Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.