Healthcare Data Storage Options Using Cloud

2021 ◽  
pp. 25-46
Author(s):  
Sandhya Armoogum ◽  
Patricia Khonje
Keyword(s):  
Data Storage ◽  
Healthcare Data

Geo-Location-Based File Security System for Healthcare Data

2018 ◽  
pp. 125-135
Author(s):  
Govinda K.
Keyword(s):  
Data Storage ◽  
Credit Card ◽  
Medical Information ◽  
Storage Systems ◽  
Storage System ◽  
Security System ◽  
Sensitive Data ◽  
Healthcare Data ◽  
File Storage ◽  
Cryptographic Techniques

Nowadays, a person's medical information is just as important as their financial records as they may include not only names and addresses but also various sensitive data such as their employee details, bank account/credit card information, insurance details, etc. However, this fact is often overlooked when designing a file storage system for storing healthcare data. Storage systems are increasingly subject to attacks, so the security system is quickly becoming a mandatory feature of the data storage systems. For the purpose of security, we are dependent on various methods such as cryptographic techniques, two-step verification, and even biometric scanners. This chapter provides a mechanism to create a secure file storage system that provides two-layer security. The first layer is in the form of a password, through which the file is encrypted at the time of storage, and second is the locations at which the user wants the files to be accessed. Thus, this system would allow a user to access a file only at the locations specified by him/her. Therefore, the objective is to create a system that provides secure file storage based on geo-location information.

scholarly journals Taming Performance Variability of Healthcare Data Service Frameworks with Proactive and Coarse-Grained Memory Cleaning

2019 ◽  
Vol 16 (17) ◽  
pp. 3096
Author(s):  
Eunji Lee
Keyword(s):  
Data Storage ◽  
Memory Management ◽  
Buffer Management ◽  
Coarse Grained ◽  
Healthcare Data ◽  
Management Scheme ◽  
High Responsiveness ◽  
Performance Optimizations ◽  
Memory Buffer ◽  
Embedded Database

This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version.

Big Data, Privacy, and Healthcare

2019 ◽  
pp. 162-175
Author(s):  
Nirav Bhatt ◽  
Amit Thakkar
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Disease Diagnosis ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Storage And Retrieval ◽  
Healthcare Data

In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.

scholarly journals Unsupervised Semantic Mapping for Healthcare Data Storage Schema

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fahad Ahmed Satti ◽  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Syed Imran Ali ◽  
Taqdir Ali ◽  
...  
Keyword(s):  
Data Storage ◽  
Semantic Mapping ◽  
Healthcare Data ◽  
Storage Schema

P8 Big data platform for cardiovascular healthcare data storage and data viewer

2019 ◽  
Author(s):  
Cesar Navarro-Paredes ◽  
Min Jing ◽  
Dewar Finlay ◽  
James McLaughlin
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Healthcare Data ◽  
Data Platform

Big Data, Privacy, and Healthcare

2021 ◽  
pp. 1491-1502
Author(s):  
Nirav Bhatt ◽  
Amit Thakkar
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Disease Diagnosis ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Storage And Retrieval ◽  
Healthcare Data

In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.

Healthcare Fusion: The Future is Here Now (Preprint)

2020 ◽  
Author(s):  
Kevin Zhai ◽  
Nasseer A Masoodi ◽  
Mohammad S Yousef ◽  
M. Walid Qoronfleh
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Healthcare Delivery ◽  
Disease Outbreaks ◽  
Molecular Data ◽  
Healthcare Management ◽  
Patient Centered ◽  
Public Health Agencies ◽  
Healthcare Data ◽  
Novel Coronavirus

UNSTRUCTURED Shakespeare famously wrote, “What is in a name?” What is healthcare fusion? Why is it important? Perhaps the ultimate goal for healthcare management is the delivery of effective patient-centered care in an equitable and timely manner. One essential tool to achieve this goal is an integrated and comprehensive pathway for healthcare data storage, analysis, and utilization. The potential exists for a real-time, cloud-based system that links physicians, hospitals, public health agencies, insurance and pharmaceutical companies, and most importantly, patients. The envisioned system provides a way to improve clinical quality management and deliver consistent and effective treatments. Indeed, massive integration of personalized health and large-scale epidemiological and molecular data, compounded with the use of artificial intelligence and machine learning, is underway. Here, we propose the healthcare fusion framework, which unifies the data and business sectors involved in healthcare delivery. This fusion aims to achieve culturally and demographically relevant outcomes in precision medicine and population health, in ways that appeal to stakeholders and investors. The proposed framework may prove highly relevant in informing governmental and private sector responses to infectious disease outbreaks, such as the novel coronavirus (COVID-19).

TBHM

2020 ◽  
Vol 15 (3) ◽  
pp. 1-17
Author(s):  
Lalit Mohan Gupta ◽  
Abdus Samad ◽  
Hitendra Garg
Keyword(s):  
Data Storage ◽  
Homomorphic Encryption ◽  
Decision Makers ◽  
Key Generation ◽  
Time Error ◽  
Health Records ◽  
Medical Practitioners ◽  
Healthcare Data ◽  
Insurance Firms ◽  
Encryption Decryption

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

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