A privacy protection method for health care big data management based on risk access control

2019 ◽  
Vol 23 (3) ◽  
pp. 427-442 ◽  
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Xiaohan Hu ◽  
Jingwei Shang
Keyword(s):  
Health Care ◽  
Big Data ◽  
Access Control ◽  
Data Management ◽  
Privacy Protection ◽  
Protection Method

scholarly journals A Survey on Securing and Optimizing Health Care Bigdata

2018 ◽  
Vol 7 (4.10) ◽  
pp. 504
Author(s):  
K. Kavitha ◽  
D. Anuradha ◽  
P. Pandian
Keyword(s):  
Health Care ◽  
Big Data ◽  
Access Control ◽  
Mathematical Optimization ◽  
Optimization Techniques ◽  
Role Based Access Control ◽  
Attribute Based Access Control ◽  
Optimization And Simulation ◽  
Role Based ◽  
The Cost

Huge amount of health care data are available online to improve the overall performance of health care system. Since this huge health care Big-data is valuable and sensitive, it requires safety. In this paper we analyze numerous ways in which the health care Big-data can be protected. In recent days many augmented security algorithm that are suitable for Big-data have emerged like, El-Gamal, Triple-DES, and Homomorphic algorithms. Also authentication and access control can be implemented over Big-data using Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) schemes.Along with security to Big-data we try to evolve the ways in which the valuable Big-data can be optimized to improve the Big-data analysis. Mathematical optimization techniques such as simple and multi-purpose optimization and simulation are employed in Big-data to maximize the patient satisfaction and usage of doctor’s consulting facility. And also, to minimize the cost spent by patient and energy wasted.  

scholarly journals Big Data for Biomedical Research and Personalised Medicine: an Epistemological and Ethical Cross-Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 13-36
Author(s):  
Mathieu Guillermin ◽  
Thierry Magnin
Keyword(s):  
Health Care ◽  
Big Data ◽  
Biomedical Research ◽  
Privacy Protection ◽  
Personalised Medicine ◽  
Data Driven ◽  
Human Intelligence

Abstract Big data techniques, data-driven science and their technological applications raise many serious ethical questions, notably about privacy protection. In this paper, we highlight an entanglement between epistemology and ethics of big data. Discussing the mobilisation of big data in the fields of biomedical research and health care, we show how an overestimation of big data epistemic power – of their objectivity or rationality understood through the lens of neutrality – can become ethically threatening. Highlighting the irreducible non-neutrality at play in big data tools, we insist upon the ethical importance of a critical epistemological approach in which big data are understood as possibly valuable only when coupled with human intelligence and evaluative rationality.

scholarly journals Big data management with machine learning inscribed by domain knowledge for health care

2017 ◽  
Vol 6 (4) ◽  
pp. 98 ◽  
Author(s):  
EPhzibah E.P. ◽  
Sujatha R
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Domain Knowledge ◽  
Disease Diagnosis ◽  
Computing Methods ◽  
Support Vector ◽  
Vector Method ◽  
Medical Practitioners

In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.

Application of Big Data in Healthcare

2016 ◽  
pp. 149-168
Author(s):  
Md Rakibul Hoque ◽  
Yukun Bao
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Management ◽  
Healthcare Spending ◽  
Well Being ◽  
Healthcare Industry ◽  
The World ◽  
Hospital Systems ◽  
New Challenges

This chapter investigates the application, opportunities, challenges and techniques of Big Data in healthcare. The healthcare industry is one of the most important, largest, and fastest growing industries in the world. It has historically generated large amounts of data, “Big Data”, related to patient healthcare and well-being. Big Data can transform the healthcare industry by improving operational efficiencies, improve the quality of clinical trials, and optimize healthcare spending from patients to hospital systems. However, the health care sector lags far behind compared to other industries in leveraging their data assets to improve efficiencies and make more informed decisions. Big Data entails many new challenges regarding security, privacy, legal concerns, authenticity, complexity, accuracy, and consistency. While these challenges are complex, they are also addressable. The predominant ‘Big Data' Management technologies such as MapReduce, Hadoop, STORM, and others with similar combinations or extensions should be used for effective data management in healthcare industry.

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