Challenges and security issues in health care industry By using Cloud Competing or Hadoop
Challenges and security issues in Health care industry
1. Aim
The main aim of this task is to analyze the challenges and security issues in healthcare. The problem selected for our research is how the security and privacy issues are applied to health care industry. Security plays a vital role in all the industry and it creates great impact in the business sectors where the data should be maintained confidential. Big data environment setup helps to resolve the security issues. It shows how it is helpful in maintain the medical records, financial, clinical and patient details safely. Comment by I Watson (Academic): This is not an aim. The aim is one sentence that tells the reader what you intend to do, e.g. ‘the aim of this research is to evaluate the smith algorithm in cyber security’.
2. Background, Motivation, Relevance, Source of Knowledge
2.1 Background
Big data addresses both the security challenges and security issues in the healthcare industry and how it is rectified. The digital medical records has given a great paradigm shift in the field of health care industry and show the impact of how it is maintained with data integrity and security. In earlier days it is maintained with files which are not user friendly we can’t access the records anytime and anywhere. The analyst should understand the concepts of issues and challenges faced in the cloud environment and provide a framework to improve the security in healthcare industry to maintain all the data’s with security. There are several challenges in the big data environment like fraud detection, forensics, data privacy issues etc. It should be focused on tools and techniques of data mining to resolve the issues of security in big data by applying some encryption capabilities. This made to implement a rigorous security system to maintain the data integrity and security. Nowadays most of the organization is collecting and processing huge amount of data in all the sectors. When more amounts of data are stored we need to check the security of data. Lack of security causes data loss and damage to the system in the health care and the patient records should be maintained confidentially. Comment by I Watson (Academic): When was this 2019? Comment by I Watson (Academic): Paper based documents were not user friendly? Comment by I Watson (Academic): Who is this?
2.2 Relevance Comment by I Watson (Academic): Not clear you know what this section means.
There are several companies providing access to cloud storage with a minimum membership of amount. Health care industry has adopted cloud storage to access the data anytime. The process of storing and retrieving data becomes easy for all in the cloud medium. The data center in the cloud provides easier accessibility to the users with limited security. The challenges are implemented and the security issues are to be resolved to maintain the data integrity. Comment by I Watson (Academic): These companies are????/ Comment by I Watson (Academic): Very simplistic statement, where did you read this? Is this really true?
2.3 Motivation Comment by I Watson (Academic): This is supposed to by YOUR motivation for doing the work.
The motivation behind the data security is to enforce tight security among all the application services provided by the cloud environment to the health care industry. Most of the data security issues are caused by the lack of measures taken care by antivirus and firewall services. Big data storage goes beyond isolated systems in the environment. The big data industry with the cloud computing expertise is trying to make a continual growth in the health care sectors. There are several recommendations given to strengthen the big data security by mainly focusing on the application security of the system. It has some servers which consist of critical data with some isolated devices. It has some new innovations in introducing real time security information with the management of event activities. It provides data handling techniques with enormous data exchange capabilities among the system. When the security mechanism is not enforced properly there may be loss of data and user access can be unauthorized due to some non privileged users especially the medical records and financial data should be protected from the outsiders. Cloud computing has the capacity of huge amount of data storage with its wide technological services. The challenges should be addressed and the issues should be resolved by applying some counter measures to improve the security. (Kavitha, 2011). The risk mitigation policies should be followed with several capabilities to overcome the security issues. The issues can be addressed with different cloud computing services like SAAS, IAAS etc. Any of the platform can be used and the security platform should be improved in the health care area.
2.4 Literature Review
Big data is used in almost all the industries and organizations to use its wide features. The main objective is to develop the novel based big data analytics to process and store the data in health care industry. Most of the health care industry is HIPAA certified which means it does not guarantee the growth of patient safety. Organization need to use the potential benefits and impact of cloud platform to remodel the business process activities in the system. The infrastructure of the cloud services caters the needs of increasing volume of data. (Mozafari, et al., 2016). The research criteria predominantly concentrates on the cloud environment with massive storage of big data analytics with the features of scalability, elasiticity and some of the resource data processing. Comment by I Watson (Academic): Not clear what you mean? Comment by I Watson (Academic): What/where are your other objectives? Comment by I Watson (Academic): Novell? As in networking systems? Comment by I Watson (Academic): Not clear what this is?
In the era of big data cloud data storage is widely used in the potential growth of the data which is increasing exponentially. With the use of big data technologies, it is easy to make decision for the organization to improve its quality and quantity. The services provide aid in intelligent decision making process for the growing organization. (Mozafari, et al., 2016). Not only in the decision making process it also improve the financial analysis of the organization. Comment by I Watson (Academic): Source?
Security and challenges are discussed and analyzed with the big data life cycle where all the stages of life cycle are discussed. The stages are data acquisition, data storage and data analytics. Data acquisition is the process of collecting appropriate data with relevant data sources. The real time processing can be done with the checking of endpoints of the system. Comment by I Watson (Academic): You will do this? Or you read this somewhere?
Fig1: Security and privacy challenges of big data and their impact Comment by I Watson (Academic): Where does your research fit in with this? Where does the system you will build fit?
Data storage provides right data to store with data integrity and data access controls. It provides data backup and recovery anytime and anywhere. Data analytics is the process of performing right action to huge amount of data. It provides credible resources with contextual insights. There are several tools for analyzing the big data like SAS, SAP, Tableau, Power BI etc.
2.5 Source of Knowledge about the journals
2.5.1 Journal publications Comment by I Watson (Academic): Which journal? You must name a journal
The article should be published on the IEEE in the second national conference with the title cloud computing. The index and citations should be provided with the proper link. It should include the needed references. The article explains the big data with is challenges and security issues and how it should create impact in the organization. The journal should be proof read twice and checked with the subject experts before going for the publication.
2.5.2 Publications
This article is set to publish in IEEE journal with the standard edition. It should follow all the guidelines of IEEE journal for publishing it. The journal includes author name followed by designation and university. It may include multiple authors. The IEEE template format should be strictly followed for abstract, body of the report and conclusion. Proper referencing style should be given in IEEE with the necessary citations. Comment by I Watson (Academic): Is there one journal name IEEE standard edition?
2.5.3 Relevant Author
|
Journal Title |
Article Title |
Author |
Published Year |
|
IEEE journal |
Privacy protection beyond encryption for cloud big data |
H. Cheng, W. Wang, and C. Rong |
2014 |
|
IEEE journal |
A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities |
N. Kumar, A. V. Vasilakos, and J. Rodrigues |
2017 |
|
International Journal of Information Management |
Big data concepts, methods, and analytics |
A. Gandomi, M. Haider |
2015 |
|
Radio Communications Technology |
Survey of Security Issues in Big Data |
M. Li-chuan, P. Qing-qi, L. Hao, et al |
2015 |
Mind map is the pictorial representation of the research in which it can be depicted in different views. Here the mind map depicts the challenges and security issues in the cloud using big data. In the health care all the data should be protected carefully to ensure the patients safety.
Fig2: Mind Map
3. Scope, Objectives and Risk
3.1 Scope and Objective of the research Comment by I Watson (Academic): Where are these? They should detail the product you are going to build.
The main objective behind the research proposal is to provide security related challenges and to resolve the security issues in the health care industry. It depicts how medical records are stored in the hospital to maintain the patient history. Big data is the emerging technology in the industry to make decisions for the organizational issues. It provides right decision at right time. Some challenges provide solutions for some of the problems existing in the system with its wide functionalities. It provide huge amount of processing the data with the data analytics tool to provide better results with the help of visualization generated with the help of data analytics tool. (Tavares, 2018), several surveys are conducted to produce better performance results in the organization to improve the decision making and analytical skills. Comment by I Watson (Academic): You have several different objectives, which one is correct?
3.2 Risk template
|
Risk Type |
Risk Event |
Likelihood (1-10) |
Impact (1-10) |
Risk Value (1-100) |
Risk Monitoring/Control Flag |
Risk Management Strategy
|
Risk Review date |
Risk owner |
Commentary |
|
T |
Literature Review
|
4 |
5 |
81 |
Time delay to learn new concepts |
Need to manage time by selecting journals |
At lit review period |
Researcher |
New methodology of learning should be adopted. |
|
T |
Technology
|
3 |
4 |
60 |
Booming technology with new features need to be selected Comment by I Watson (Academic): What is ‘booming technology? A software package? App development environment? |
Review the technology |
During research period |
Researcher |
Knowledge of using tools |
|
T |
Fault during design and development |
2 |
3 |
50 |
Error detection and correction Comment by I Watson (Academic): Of what? |
Admin help Comment by I Watson (Academic): Who is this? |
During research |
Researcher |
Error correction with subject experts |
|
T |
Testing Comment by I Watson (Academic): Comment by I Watson (Academic): How can you test something if you have not built it first? |
3 |
4 |
45 |
Test cases are tested. |
Guidance and help |
During research |
Researcher |
- |
|
F |
Financial risk is not clearly defined |
0 |
0 |
0 |
- |
- |
- |
- |
- |
|
F |
Environmental risk is not defined |
0 |
0 |
0 |
- |
- |
- |
- |
- |
|
P |
People risk defined
|
1 |
1 |
0 |
usage |
Demo class |
After deployment |
client |
Working exp |
4. Ethics, Legal, Social, Security and Professional Consideration
This section deals with analysis and testing the article with social and ethical impacts and how it creates impacts to the society. It shows how social, legal, security and professional consideration are assessed. Comment by I Watson (Academic): How can you test an article? This should be dealing with your dissertation and the product/system you are to build.
4.1 Ethics
There are many methods that is used for proposing solution for the ethical issues with cloud computing and its technologies with the privacy and security policy of the organization. Some of the counter measures should be followed to resolve all the ethical issues in the organization. Comment by I Watson (Academic): This is the ethics associated with YOUR dissertation research
4.2 Legal Comment by I Watson (Academic): Again related to your work
The legal issues are in the organization for several reasons and need to be solved to create the impact on the growth of the organization. The reasons for legal issues are confidentiality, integrity and security mechanisms.
4.3 Social Comment by I Watson (Academic): As above – your project
There is a social network for addressing the social issues of the organization. There are several benefits and opportunities in addressing the socio economical issues. The information stored can be shared easily and accessed. (Rodrigues, 2017).
4.4 Security Comment by I Watson (Academic): As above
This research uses some techniques for resolving the security issues in the cloud storage. The lack of security in the organization leads to loss of data and occurs some crash in the system.
4.5 Professional issues Comment by I Watson (Academic): You need to look at the assessment guidance to see what this relates to.
There are some professional values maintained for each organization with its wide technological innovation. The data access and sharing becomes easy in the cloud environment with a wide variety of techniques. Almost the security challenges are addressed for the organization to maintain professionalism for maintaining the data with security.
5. Scheduling Activities of the research
5.1 Task List and Monitoring & control done Comment by I Watson (Academic): This is supposed to be for your dissertation, i.e. beginning January/May 2021
|
Task/Objective/Milestones Deliverable |
Duration |
Actual start |
Actual end |
Deliverable |
|
Project Initiation |
||||
|
Confirm supervisor |
7days |
25/01/2020 |
31/01/2020 |
Study phase |
|
Develop project idea and scope |
7days |
1/02/2020 |
08/02/2020 |
Study phase |
|
Research topic |
7days |
1/02/2020 |
08/02/2020 |
Study phase |
|
Planning |
||||
|
Produce first draft of project plan |
21 days |
02/02/2020 |
23/02/2020 |
Report Generation |
|
Literature Review |
||||
|
Research relevant literature |
7 days |
23/02/2020 |
29/02/2020 |
Report Generation |
|
Plan Literature Review |
7 days |
23/02/2020 |
29/02/2020 |
Report Generation |
|
Complete first draft |
7 days |
23/02/2020 |
29/02/2020 |
Report Generation |
|
Document review |
7 days |
01/03/2020 |
08/03/2020 |
Report Generation |
|
Amend and finalize |
7 days |
01/03/2020 |
08/03/2020 |
Report Generation |
|
Requirement Specification |
||||
|
Market research from comparable existing systems Comment by I Watson (Academic): You have done this already? |
14 days |
02/02/2020 |
16/02/2020 |
Report Generation |
|
Plan requirement specification |
7 days |
17/02/2020 |
23/02/2020 |
Report Generation |
|
Testing |
||||
|
Produce first draft of test plan |
7 days |
02/03/2020 |
09/03/2020 |
Report Generation |
|
Application Wireframes |
||||
|
Complete first draft |
7 days |
02/03/2020 |
09/03/2020 |
Report Generation |
|
Document review |
7 days |
03/03/2020 |
09/03/2020 |
Report Generation |
|
Amend and finalise |
7 days |
06/03/2020 |
12/03/2020 |
Report Generation |
|
Design |
||||
|
Amend application wireframes using User feedback |
7 days |
04/02/2020 |
10/02/2020 |
Configuration of software packages |
|
Research database requirements and schemas |
7 days |
04/02/2020 |
10/02/2020 |
Configuration of software packages |
|
Produce database design schema |
7 days |
11/02/2020 |
17/02/2020 |
Configuration of software packages |
|
Produce class diagrams |
7 days |
11/02/2020 |
17/02/2020 |
Configuration of software packages |
|
Produce User stories |
days |
|
|
Configuration of software packages |
|
Produce sequence diagrams |
7 days |
15/02/2020 |
21/02/2020 |
Configuration of software packages |
|
Design documentation review |
7 days |
15/02/2020 |
21/02/2020 |
Configuration of software packages |
|
Implementation and Testing |
||||
|
Implementation research |
42 days |
25/01/2020 06/03/2020 |
30/01/2020 12/03/2020 |
Report Generation |
|
Account creation implementation |
7 days |
06/02/2020 |
12/02/2020 |
Report Generation |
|
Account creation unit testing |
7 days |
07/02/2020 |
13/02/2020 |
Report Generation |
|
Database scheme implementation |
7 days |
07/02/2020 |
13/02/2020 |
Report Generation |
|
Content uploading implementation |
14 days |
14/02/2020 |
28/02/2020 |
Report Generation |
|
Content uploading unit testing |
14 days |
14/02/2020 |
28/02/2020 |
Report Generation |
|
User feeds implementation |
14 days |
14/02/2020 |
28/02/2020 |
Report Generation |
|
User feeds unit testing |
14 days |
13/02/2020 |
27/02/2020 |
Report Generation |
|
User feed and content uploading integration testing |
14 days |
13/02/2020 |
27/02/2020 |
Report Generation |
|
Content viewing and voting implementation |
21 days |
13/02/2020 |
05/03/2020 |
Report Generation |
|
Content viewing and voting unit testing |
14 days |
13/02/2020 |
27/02/2020 |
Report Generation |
|
Content viewing and voting integration testing |
14 days |
13/02/2020 |
27/02/2020 |
Report Generation |
|
Full system black box testing |
14 days |
19/02/2020 |
04/03/2020 |
Report Generation |
|
Plan user testing sessions |
7 days |
19/02/2020 |
25/02/2020 |
Report Generation |
|
Produce pre, and post survey documents |
7 days |
19/02/2020 |
25/02/2020 |
Report Generation |
|
Conduct user testing sessions |
14 days |
05/03/2020 |
18/03/2020 |
Report Generation |
|
Database schema discussion document |
7 days |
14/02/2020 |
20/02/2020 |
Report Generation |
|
Design vs. Implementation document |
7 days |
12/02/2020 |
18/02/2020 |
Report Generation |
|
Synthesis |
||||
|
Plan synthesis |
7 days |
12/02/2020 |
18/02/2020 |
Report Generation |
|
Complete first draft |
7 days |
12/02/2020 |
18/02/2020 |
Report Generation |
|
Document review |
7 days |
12/03/2020 |
18/03/2020 |
Report Generation |
|
Amend and finalize Comment by I Watson (Academic): How can you do these four tasks alongside each other? Surely one has to be completed before it can be amended? |
7 days |
12/03/2020 |
18/03/2020 |
Report Generation |
|
Evaluation |
||||
|
Plan synthesis |
7 days |
10/03/2020 |
16/03/2020 |
Report Generation |
|
Complete first draft |
7 days |
14/03/2020 |
20/03/2020 |
Report Generation |
|
Document review |
7 days |
12/03/2020 |
18/03/2020 |
Report Generation |
|
Amend and finalize |
7 days |
16/03/2020 |
22/03/2020 |
Report Generation |
|
Abstract and Introduction |
||||
|
Plan abstract and introduction |
7 days |
02/02/2020 |
08/02/2020 |
Report Generation |
|
Complete first draft |
7 days |
14/03/2020 |
20/03/2020 |
Report Generation |
|
Document review |
7 days |
12/03/2020 |
18/03/2020 |
Report Generation |
|
Amend and finalize |
7 days |
16/03/2020 |
22/03/2020 |
Report Generation |
|
Project Documentation deadline 26.03.2020 |
||||
|
Viva |
||||
|
Plan and prepare for Viva |
14 days |
16/04/2020 |
23/04/2020 |
Preparation for viva |
|
Present |
14 days |
16/04/2020 |
23/04/2020 |
Preparation for presentation and queries |
|
Project Viva Deadline 01/05/2020 |
5.2 Gantt chart Comment by I Watson (Academic): This is for your dissertation research – not the research methods module.
6. References
Frizzo-Barker, J., Chow-White, P. A., Mozafari, M. et al. (2016). An empirical study of the rise of big data in business scholarship, International Journal of Information Management, Vol. 36, pp. 403–413.
Gandomi, A., Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35, pp. 137–144.
Huang, T., Lan, L., Fang, X., et al. (2015). Promises and challenges of big data computing in health sciences, Big Data Res., 2, pp. 2–11.
Kibiwott, K. P., Zhao, Y., Kogo, J. et al. (2019). Verifiable fully outsourced attribute-based signcryption system for IoT eHealth big data in cloud computing, Mathematical Biosciences and Engineering, 16, pp. 3561–3594.
Kumar, N., Vasilakos, A. V. and Rodrigues, J. (2017). A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities, IEEE Commun. Mag. 55, pp. 14–21.
Leman, A., Hanghang, T., and Danai, T. K. (2015). Graph based anomaly detection and description: a survey, Data Min. Knowl. Disc., 29, pp. 626–688.
Li-chuan, M., Qing-qi, P., Hao, L. et al. (2015). Survey of Security Issues in Big Data, Radio Communications Technology, 41, pp. 1–7.
Rebello, C., and Tavares, E. (2018). Big Data Privacy Context: Literature Effects on Secure Informational Assets, Transactions on Data Privacy, 11, pp. 199–217.
Remya, G., and Mohan, A. (2015). Distributed Computing Based Methods for Anomaly Analysis in Large Datasets, International Journal of Advanced Research in Computer and Communication Engineering, 4, pp. 427–430.
Restuccia, F., Kanhere, S. D., Melodia, T. et al. (2018). Blockchain for the Internet of Things: Present and Future, IEEE Internet of Things Journal, 1, pp. 1–8.
Silva, C. R., Rodrigues, E. M. T. (2017), Privacy in Big Data: Overview and Research Agenda, Sistemas & Gestao, Vol. 12, pp. 491–505.