Trusted Compliance Enforcement Framework for Sharing Health Big Data

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.

A Semantic Framework for Secure and Efficient Contact Tracing of Infectious Diseases

Contact tracing is the process of identifying people who came into contact with an infected person ('case') and collecting information about these contacts. Contact tracing is an essential part of public health infrastructure and slows down the spread of infectious diseases. Existing contact tracing methods are extremely time and labor intensive due to their reliance on manually interviewing cases, contacts, and locations visited by cases. Additionally, complex privacy regulations mean that contact tracers must be extensively trained to avoid improper data sharing. App-based contact tracing, a proposed solution to these problems, has not been widely adopted by the general public due to privacy and security concerns. We develop a secure, semantically rich framework for automating the contact tracing process. This framework includes a novel, flexible ontology for contact tracing and is based on a semi-federated data-as-a-service architecture that automates contact tracing operations. Our framework supports security and privacy through situation-aware access control, where distributed query rewriting and semantic reasoning are used to automatically add situation based constraints to protect data. In this paper, we present our framework along with the validation of our system via common use cases extracted from CDC guidelines on COVID-19 contact tracing. © 2021 IEEE.

A Semantically Rich Knowledge Graph to Automate HIPAA Regulations for Cloud Health IT Services

As healthcare organizations adopt cloud-based services to manage their patient data, compliance with the rules and policies of the Health Insurance Portability and Accountability Act (HIPAA) regulation becomes increasingly complex. At present, HIPAA rules are available only in large textual format and require significant human effort to implement in the Health IT systems. Moreover, every change in the regulation, like the recent relaxation in telehealth policy due to the COVID-19 pandemic, has to be manually implemented in the IT system. We have developed a semantically rich Knowledge graph, using Semantic Web technologies to represent HIPAA rules in a machine-processable format. This will significantly help in automatically reasoning of HIPAA policies. In this paper, we describe our design along with the results of our study of the current status of research on HIPAA ontology. We have validated our design against use cases defined by the US Department of Health and Human Services (HHS). This knowledge graph can be integrated with existing healthcare systems to provide automated compliance with HIPAA policies. © 2021 IEEE.

A Framework for Situation-Aware Access Control in Federated Data-as-a-Service Systems Based on Query Rewriting

Organizations often need to share mission dependent data in a secure and flexible way. Examples include contact tracing for a contagious disease such as COVID-19, maritime search and rescue operations, or creating a collaborative bid for a contract. In such examples, the ability to access data may need to change dynamically, depending on the situation of a mission (e.g., whether a person tested positive for a disease, a ship is in distress, or a bid offer with given properties needs to be created). We present a novel framework to enable situation-aware access control in a federated Data-as-a-Service architecture by using semantic web technologies. Our framework allows distributed query rewriting and semantic reasoning that automatically adds situation based constraints to ensure that users can only see results that they are allowed to access. We have validated our framework by applying it to two dynamic use cases: maritime search and rescue operations and contact tracing for surveillance of a contagious disease. This paper details our implemented solution and experimental results of the two use cases. Our framework can be adopted by organizations that need to share sensitive data securely during dynamic, limited duration scenarios.

Preliminary detection of covid-19 using deep learning and machine learning techniques on radiological data

Covid-19 is a dangerous pandemic in the year 2020." Covid-19 positive" is the most negative word heard this year, which caused terror worldwide. As it is a contagious pandemic, early detection of this pandemic will minimize its threat. The primary issue is its detection. To detect Covid-19 through a blood test, a person must wait for an extended period to get the results. Using our model, one can primarily detect Covid-19 immediately using Deep Learning algorithm CNN and Machine Learning algorithm Logistic Regression. Input to these techniques is radiological data like CT-Scan and X-ray images. Covid-19 positive cases will be easily detected faster with the help of this model. © 2021, Engg Journals Publications. All rights reserved.