Despite living in the 20th century, even the first world countries have

99 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 5 DOI: 10.4018/978-1-5225-2607-0.ch005 ABSTRACT Cloud computing has shifted our old documents up into the clouds, with the advancement of technology. Fast-growing virtual document storage platforms provide amenities with minimal expense in the corporate society. Despite living in the 20th century, even the first world countries have issues with the maintenance of document storage. Cloud computing resolves this issue for business and clinic owners as it banishes the requirement of planning, provisioning, and allows corporations to advance their filling system according to service demands. Medical practices heavily, rely on document storage as; almost all information contained in medical files is stored in a printed format. Medical practices urgently need to revolutionize their storage standards, to keep up with the growing population. The traditional method of paper storage in medical practice has completely been obsolete and needs to improve in order to assist patients with faster diagnosis in critical situations. Obtaining Knowledge and sharing it is an important part of medical practice, so it needs immediate attention to reach its full service potential. This chapter has analyzed content from literature that highlights issues regarding data storage and recommends solution. This inquiry has found a useful tool that can be beneficial for the development of this problem which is, ‘data mining’ as it gives the option of predictive, and preventative health care options, when medical data is searched. The functionality and worthiness of each algorithm and methods are also determined in this study. By using cloud and big data services to improve the analysis of medical data in network of regional health information system, has huge advancements that assure convenient management, easy extension, flexible investment, and low requirements for low technical based private medical units. The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage Sushma Munugala Charles Sturt University, Australia Gagandeep K. Brar Charles Sturt University, Australia Ali Syed Charles Sturt University, Australia Azeem Mohammad Charles Sturt University, Australia Malka N. Halgamuge Charles Sturt University, Australia 100 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  INTRODUCTION Cloud computing has become one of the fastest emerging techniques in the area of information technology. Information technology has started to gain interest due to population increase and virtualization of documents in a business environment, as it gives possible solutions to this rising problems. Organizations need a sustainable filing system that copes with current demands to solve this issue, in order to serve their clients better and faster. The technology of cloud computing provides a few number of benefits. Firstly, it is convenient, with a common shared infrastructure that provides servers with storage disks, networking components with wires, switches, hubs, and routers. Secondly, the implantation of cloud allows medical related information to be available over the Internet; thereby rendering this information to make it accessible is evolutionary. A huge number of people who use the Internet inspire to reach this ultimate goal. Storage of data, with secure confidentiality, and analysis of the stored data has three important aspects that make cloud computing easy to manage. Firstly, the information collected in the medical field is referred to as “raw data”. This data is stored in the data warehouse for future use or analysis. This Data Warehouse is a collection of databases where volumes of data are stored, then used when needed. Here, the collected data can be stored both in structured and unstructured format. To convert the unstructured data into a structured format, the data needs to be clustered. In addition to the k-means cluster algorithm for clustering the unstructured data into some structured format is also revolutionary. Once the data is clustered, then we will get various patterns that are then subjected to analysis. This enhances the analysis phase by allowing various interesting patterns to rise, and consequently data is abstracted. The aforementioned Fuzzy logic technique is one of the most common methods used for the decision-making process during the analysis. Additionally, in cloud computing, one of the major issues is security and confidentially of sensitive data. To overcome the security problems of data storage in cloud is to use an encrypted format that makes it hard for hackers to interrupt, and understand. Next, we reviewed the studies in related area about bigdata clustering and analyzed cloud computing techniques in relevant medical decision-makings situations. Considering a vast amount of medical data that has been available on the Internet, the easy retrieval of data is helpful to health service providers, and particularly for specialists who need to identify diseases in depth in a limited timeframe. Once medically relevant data has been collected from networks, it then needs to be stored in a database to precede data analysis (i.e. clustering approach) this process allows users to obtain required information. The current system allows medical organizations to share their confidential information through the Internet, and causes leaks of confidential data. The current system also does not provide sufficient techniques or functions to secure confidential data while transmitting it through the Internet. In lieu of health service providing, organizations that have faced problems when analyzing the required information about a particular medicine or disease through the Internet will also benefit from this function. To avoid these issues in the “medical data security” field, this chapter proposes a technique that will help to organize collected information from the Internet securely. This highlighted method would help these advanced algorithms to share information with others while sustaining confidentiality. Furthermore, Kmeans technique is used to cluster big data from the database to retrieve required medical information. Some of the necessary steps involved in Parallel clustering algorithms are based on k-means in big data; this is displayed as follows: (i) Centroid-based clustering, (ii) Density-based clustering (iii) Connectivitybased clustering, (iv) High-dimensional clustering, (v) Similarity-based clustering, and (vi) Co-clustering. 101 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Implementing ‘decision making’ with cloud computing technologies in medical fields is one of the hardest parts of this process. It challenges various traditional approaches when applying design, and management of medical files in an organization or medical data centers, particularly if there are issues with security portability and interoperability. Some decision-making algorithms have been available on networks that use fuzzy logic technique in decision-making, which helps to identify the possible information about various medical data, nevertheless they have been unsuccessful. The decision-making is done based on health service providers’ requirements. The fuzzy logic concept is examined by different researchers and was suggested to make decisions as it provides better results when compared with other algorithms. Another reason for using fuzzy logic is that it is convenient because it explains the decision clearly, and it is process that can be implemented easily. One of the main challenges in ensuring “medical data security” for big data is to come up with a balanced approach towards regulation and analytics of this approach. It shows how various organizations carry out useful analytics to secure the privacy of patients. There are number of techniques available for privacy and preservation of data mining, as
privacy-preserving data integration, and privacy preserving information retrieval from big data have been developed with the assurance of security. In the past few years, many organizations started to use cloud computing to store their data for this reason. Companies store confidential and sensitive data in public cloud confidently, as most organization around the world use Big Data for the purpose of decision support, and to get cloud services offered by third parties. There are many benefits of increasing the security usage of cloud computing in business. Some of the advantages are as follows; parallel computing, scalability, elasticity and inexpensive. This study has used published articles and has compared and contracted studies to analyze highlighted trends of big data. This paper has used content analysis method to draw data from various methods in order to understand multiple aspect of the trends. The comparisons and contrasts of published scholarly articles have used different algorithms and methods that highlighted these trends. The purpose is to evaluate functionality and worthiness of each algorithm method and determined and explained possible issues and ways to overcome them. The main issues that rises from this content analysis is the fact that, the implementation of cloud computing has various possible ways, to overcome the adverse effects of unstructured data by using cloud computing is effecatious. This chapter has used specific methods to identify trends of algorithms that compares and contrasts data by using specific tools from each research article. The aim of this chapter is to give a better comprehension of cloud computing on medical data and to identify issues that are important for future research directions. . This study has used tools to measure information sourced from articles, for example, “K-means clustering algorithm” to structure data, and encryption algorithms to specifically secure medical data. CLOUD COMPUTING DATA SECURITY Data security in cloud computing is massive as it gives a futuristic solution to an existing problem getting bigger day by day because of data breaches on cloud. The protection of users’ privacy is the biggest challenge for cloud because it contains vast amount of information from all over the globe. Cloud provides various benefits to the organizations such as low cost security, easy access of data and less management; nonetheless it is risky to upload sensitive data on cloud which can be stolen, modified and deleted by the 102 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  attackers (Svantesson, 2010). This paper presents security steps which helps an organization to secure their information, and get facilities from service providers, who are aware about some rights and policies etc., (Zhang, 2016). This study will show some basic security steps to protect cloud data as a solution. Cloud computing provides a set of resources on the internet for user conveniences. For example, a user can store, manage and process data on cloud rather than on local server or a hard disk. Cloud computing or internet computing has also big data centers all over the world, accessible anytime, anywhere, to connect you to your business by using web-enabled devices such as smartphones, tablets and laptops. This gives users big benefits to organize and move their data on cloud because they do not need to purchase big data storage devices to store their data (Pandith, 2014). They can easily store information on cloud and authorize employees to access data by using their username and password even by sitting at home, this is called virtual office. However, to transfer data on cloud is also very risky too. This is because personal information can be stolen and used in an unethical manner (Pitchai, 2016). There have been numerous accounts of news on cloud data breaches of how hackers hack personal accounts and get sensitive data. There are number issues on cloud because of financial data, personal information, and medical data breaches that can have detrimental effect on clients/patients. Without appropriate measures to establish safety set up, and information storage it becomes defenseless against leaks and, conducive to security ruptures and assaults (Mandal, 2013). How Data Breaches Can Happen 1. Failure of Authentication and Authorization: As cloud data can be accessed by anyone from anywhere if the authorization and authentication process is not strong, then data breaches can be happening. 2. Account Hijacking: This can be done by expert hackers who can modify data and manipulate transactions. 3. Dos Attacks: Denial of service attacks that slows the system or simply time out where you can just sit and wait. 4. Exfiltration: If the cybercriminal gets into one computer in a company then the entire medical data can be extracted (Chen, 2009). This shows that medical data security is a significant concern in cloud environment when it guarantees approved access. Information security manages information assurance and security as this includes protection of information from being lost or decimated, tainted or altered. Rather than putting away the information locally, clients store it in cloud. This way, rightness and accessibility of information must be guaranteed. The essential worry in distributed computing is security of client information which is the biggest concern of all (Waseem, 2016). There are many methods to secure “medical data” in cloud computing such as Encryption, digital signature, secure authorization, nonetheless, these methods have some limitations which are shown below. The best solution for storing medical data security on cloud is stated according to findings of this study that can be a “powerful solution of keeping medical data in cloud, as it is a combination of three things: Data lockdown, Access policies and security intelligence” (Kumar, 2016). Basic security methods can be given by: 103 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  • Encryption: According to this study, utilizing an algorithmic plan to change plain content (data) into a non-meaningful structure is called cipher-text. The opposite procedure is decryption which decodes the data from its encoded structure back to a plain content. To avert unapproved access to plain content information, the numerical calculation requires a secret value, known as key, with a specific end goal to scramble or decode the information appropriately. Cloud encryption is utilized to safe personal data saved and handled through networks, the web, tablets and remote gadgets (Chaves, 2011). • Limitation: As mentioned above the information security models need few perspectives to satisfy the assessment criteria of security systems. Also, a coordinated methodology utilizing diverse strategies for verification, security and information integrity also needs to be exploited to illuminate the pitfalls of existing frameworks. • Digital Signatures: Author said it is based on asymmetric cryptography, as RSA algorithm helps to generate a private and public key which is linked together mathematically. Then this is used to private key to encrypt the hash (Kaur, 2013). Limitation- Digital signatures are products with short life nonetheless it allows to get the software verification, as the sender and receiver does not have to pay any costs. • Secure Authorization: In this security step, there should be a limited number of users and all the users have to have different names and passwords to access the authorized data. These passwords should be strong and secure to avoid breaches (Hagos, 2016). • Limitation: The hacker can guess or break the passwords and access sensitive data. According to the first key-point the data should not be in readable format which provides a strong key-management, and it should be done by incremental encryption. Incremental encryption use “Collision free hashing” and “Digital signatures”. Secondly, after implementing access policies, only the authorized
person can get access to sensitive information. The root users or Privileged users also cannot explore sensitive data after implementing these policies. Last but not least, the security intelligence will be incorporated to produce log information of users in cloud. This helps to check the behavior of users and generate alerts against hackers. RESEARCH ISSUES Issues and limitations related to big data medical health organizations are described in this section: 1. online information reliability. 2. Big data management and analytics. 3. Improving data analytic techniques. 4. Integrating big data with cloud computing. 5. Security and privacy challenges in cloud computing system. 6. Query and runtime optimizing for iterative and distributed programs.7 Declarative specifications and optimizes asynchronous computations 8. Data protection 9. Administrative rights. Online Information Reliability Retrieving medically relevant information from the Internet is one of the hardest processes, because many people around the world use their native language to communicate. This creates some difficulty for researchers or for people in the system that retrieves data from these networks. Medical data will 104 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  be extracted from different resources with different qualities. However, identifying which information source is more reliable than others is impossible and is not a natural process (Hannan 2014). Identifying the trustworthiness of the data taken from online sources is one of the most important aspects of online research. Due to living in a knowledge-based society surrounded by high-tech gadgets, people have access to various online medical details that help online users to search and identify information about a particular medicine or disease. The drawback of this is the fact that there needs to be increased levels of privacy to assure “medical data security” for confidentiality. The growth of social media, computer technology, medical and other data sets on the Internet, basically heightens the need for a secure storage system than ever. Additionally, data mining also handles the flow of data properly and the prediction of existing relational database, considering other data mining techniques that are insufficient compared to retrieving data from the Internet. Big Data Management and Analytics Big data management and analytics are critical in the proposed system, because big data helps to store all information that is collected from the Internet. One of the main issues is implementing infrastructure and high-performance computing techniques for storage of big data. Managing retrieved data from multiple sources and securing access to big data is crucial and hard. There needs to be more concentration on data analytics techniques that will help to manipulate and analyze big data to extract small chunks of information (Thuraisingham, 2015). Wang (2009) proposed a technique, which helps to increase security of Big Data that not only stores cloud nonetheless also validates the required data to perform some analytical processes. This method contributes to increase the number of cloud users, and allows them to retrieve required medical information quickly from the Internet. The author proposed a technique that helps to identify the frequently searched information from the Internet as well, and also provides natural methods to analyze medical information. Improving Data Analytic Techniques Data plays a vital role in the proposed system; here we use k-means algorithms to require data from big data. Using the advanced k-means algorithm can easily improve data analytics. However, developing the data analytic techniques in the proposed system provides more benefits in collecting and analyzing relevant medical data. Cloud environment provides various techniques and methods to maximize the analytics of data (P.R, 2012). To show the exact value from the database or cloud data analysis methods or tools, there needs to be an inspection system in place or to transform the auditing progress. Author (HAN HU, 2014) states that the proposed concept to improve data analytic techniques, is as follows: • Searching with keyword that matches several native language codes that helps to increase the search information. • Application fields leverage opportunities presented by generous data and proposed technique which will retrieve domain-specific analytical methods to derive the intended issues. • Using the proposed techniques in data analytics will also provide many benefits (Campos, 2010). 105 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Integrating Big Data with Cloud Computing “ConPaaS” provides an integrated cloud environment for big data, as it helps to minimize the complexity of cloud computing. It also offers two services in Big-data such as MapReduce and Task Farming. This tool will be very helpful for integrating both big-data and cloud (Madden, 2012). Big data mainly concentrates on achieving deep business value from various deployments of advanced analytics and trustworthy data on Internet scales in medical fields. By categorizing and accessing the application loads can be beneficial as it will inadvertently help to improve big data integration with cloud computing, as this will be ideal (Changqing 2012). The author Chandrashekar, (2015) proposed a novel technique to integrate both cloud computing and big data, He explained how this method helps to minimize the cost, and overhead, as it also triggers rapid provisioning that gives time to market with flexibility and scalability. Security and Privacy Challenge in Cloud Computing Collecting data from “Internet storage manipulation” and controlling the medical data security in cloud computing environment is one of the hardest processes that results in security and privacy considerations as mentioned before. However, different methods and techniques have been proposed to handle big data in cloud computing system, as this technology provides high security for data that is stored in cloud computing (Thuraisingham, 2015). Query and Runtime Optimization for Iterative and Distributed Programs Runtime optimization is one of the most important processes in the proposed system as it needs to analyze the data sets that are relevant to medical data. Many operations can easily be handled with the help of proposed algorithms. K-means algorithm provides a separate way to cluster information from Big Data to allow it to configure programs that match keywords. The easiest process in this algorithm is one that can easily integrate this approach in the proposed system (Baek 2015). First of all, the proposal of big data query, and runtime is to measure, evaluate, and compare big data systems and its architecture. To retrieve required information from the Internet is one of the hardest parts that need an algorithm query processer, which matches the information retrieved from the Internet. Wang, (2014) proposed a benchmarking method to process the query, which helps people to retrieve required information from the Internet. This method contributes to minimize security issues as well as contribute to increase the query runtime of the process in the proposed system. Declarative Specifications and Optimization for Asynchronous Computations Big data provides room for researchers, to retract data for their research topics in their areas. Declarative specifications which play a vital role in the proposed system gives space to concentrate more on the optimizer for asynchronous computations that leads to high success factor in retrieving relevant medical data from big-data storage (Dean, 2013). 106 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Data Protection To improve data efficiency many cloud environments such as Hadoop stores data without encryption or any other security methods. If any unauthorized user or hackers accesses a set of machines, then there is no way to stop them from stealing critical medical da
ta stored in machines (Ren, 2012). The need for an advanced technique to provide data protection in both Big Data and cloud computing seems to be the resolution. Zhang, (2010) Proposed a quick grid method to secure medical data as he developed a framework to maximize the security level of data in Big Data and Cloud computing that helps secure customers medical details as well as healthcare information. At the same time, the development of a security framework that consists of four main parts such as security governance, security management, security maintenance and security technology is still much needed. Furthermore, numerous security solutions have been proposed by researchers and developers to protect users, specifically, medically relevant information in both Big Data and Cloud Computing. Many of these researchers have proposed an identity-based encryption and proxy re-encryption schemes that helps to improve security for communication services in particular processes. Some of the existing techniques and methods that contribute to secure the system are as follows; white hat security, proof point, DocTrackr, Cipher Cloud, Vaultive and SilverSky (Agrawal, 2016). Administrative Rights Administrative rights are the most important aspects of the aforementioned systems because they will control all activities that flow in the system. The administrator provides access controls of users, as this method also provides a particular kind of security for both the cloud computing operations as well as data (F.C.P, Oct 6-9, 2013). Most organizations around the world are unaware of the fact that, employees have administrative rights. The administration has access to critical information that poses a risk, and to permit employees to access sensitive documents that can lead to data theft in organizations leaving them with consequences. Employees or intruders can easily upload viruses or warm codes in the organization to steal confidential information. Therefore, providing administrative rights to particular employees to access sensitive files in an organization is one of the most crucial aspects of secure storage (Perry, 2012). OVERVIEW OF EXISTING SOLUTIONS RELATED TO ISSUES Computing information is also another significant aspect in the concept of storage and analysis of data. Only storing medical data securely is not enough, it also needs to be easily retrieved. So it is necessary to turn raw data into some structured format so that it will be easy to retrieve, otherwise it defeats the purpose. Brett and Hannan (2014) have used information fusion algorithm to structure raw data that is collected. They collated similar patterns that are formed by joining existing patterns and leaving out the unnecessary information behind unnoticed. This chapter demonstrates that the use of k-means clustering algorithm is needed to convert raw data into structured one, by clustering them into various patterns, can 107 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Table 1. Comparison of research issues in big data medical health organization Security Issues and Challenges Concerns Analysis and Findings Limitation Authors Privacy of data transmission, data breaches. Confidentiality of data Sensitive data expose or access while transmission Multi-layered security where the authors compared ‘CCAF multi-layered security with a single-layered approach by performing experiments. It takes more than 50 hours to secure all 2 PB information and above 125 hours to raise a caution to take control of the circumstance in the ULCC Data Centre Time consuming and expensive process Chang & Ramachandran (2016) Accounts hijacking Authorization and authentication of data Proposed data security model In this model all the layers have to interact with each other before starting any process. Pitchai, & Jayashri (2016) APIs Management on cloud Signals self-collected from good subjects are utilized as health information. All information is changed into binary format based on a particular quantization determination Actions are performed on each and every layer Bechtel (2016) Security and privacy challenge in cloud computing Collecting data from computer storage, manipulation and controlling of data in the cloud is difficult Encryption Algorithms, e.g. DET Algorithms differ from one organization to other depend on security challenge. These algorithms facilitate data to be secured Thuraisingham et al. (2015) Data is being accessed by the unauthorized users Certificate management, authentication breakage Comparison between the old traditional system and three level authentication mechanisms Three level authentication mechanisms to get higher level of cloud data security. Sirohi & Aggarwal (2015) Healthcare – Electronic healthcare records Privacy sensitive health records are released to the third party in cloud Used Anonymization With MapReduce method and anonymize health care data via generalization using two-phase clustering approach A third party in cloud has access to healthcare records Zhang et al. (2015) [ Healthcare – All digital healthcare industry Regional secure data process Raspberry Pi is a pocket-sized computer used in forensic medicine, forensic etymology collect limit issues in future health care Feng et al. (2015) Malicious attacks Monitoring, management Risk for healthcare applications Data partitioning and scrambling – ECG signals from MIT- BIH arrhythmia database and ECG It requires all the TCP-IP layer management and different security Wang & Yang (2015) Data loss, sensitive data breaches Confidentiality, integrity and availability of data Access control, Encryption. Cloud hosted data remains secret via encrypted transmission of data and encrypted storage of data Account hijacking, exposer of cloud hosted data. If the private key expose, then whole data will be lost. Devi & Ganesan (2015) Hijacking of accounts Cyber-attacks. Account Credential performs, Preventing Phishing attacks Both reaction as well as preventive measures in consistence with best industry practices and international standards This is a kind of research, analysis and prevention plan of account hijacking not a particular solution of that Tirumala et al. (2015) continued on next page 108 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Security Issues and Challenges Concerns Analysis and Findings Limitation Authors Risk of data exposure, Security of data Untrusted third party attacks Holomorphic encryption, the pain-text encryption done by using Holomorphic encryption before sending Complicated algorithms used Jain & Madan(2015) Attacks done by malware Integrity of data, availability of data, Limited data availability, updating of data without authority Email-filter set up at high mode – from email setting the filter mode changed to high mode Not applicable on vast data. Just suitable for email security Sharif & Cooney (2015) No authorization, No encryption Increasing Data breaches User errors: Methodology Encryption by complex algorithms Training about new software, and data security by using Complex algorithm Asaduzzaman & Jain (2015) Password recovery, Data location, Confidentiality, Data concealment Performance issues with cipher-text Dynamic virtualization of software, hardware Time consuming Pandith (2014) Confidentiality, authentication Control over Information leakage. User errors Training methods – Proper training session to train employee about new software or techniques Gong (2014) Authorization, authentication Control over unauthorized access Malicious attacks, Signatures signed on paper and scanned to save in computer then cropped to create a picture Less effective Kaaniche & Laurent (2014) Improving data analytic techniques Data plays a vital role in the proposed system. So there is a need to develop techniques used for data analysis K-means algorithm To cluster require data from big data Hu et al. (2014) Query and runtime optimizers for iterative and distributed programs. Runtime optimization is most important and critical process that was difficult to achieve Benc
hmarking Method To minimize security issues as well as it helps to increase the query and runtime of the process in the proposed study Wang et al. (2014) Online information security The identifying truthfulness of data that is shared online Map-reduce Algorithm To analyze various clusters and recommend services used by other users or researchers for the same type of work Ramamoorthy et al. (2013) Data exposure, sensitive information leakage Difficult to stop data exposure and leakage Phishing attacks, Digital signatures. Less effective for huge amount of data security Signatures signed on paper and scanned to save in computer then cropped to create a picture Sirohi & Agarwal (2013) Data confidentiality: Challenging multitenant environment Remote server attaches Virtual infrastructure provided to host services to client for its usage and management of stored data in cloud servers Public key based framework Barbori (2012) continued on next page Table 1. Continued 109 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  give a clear image in the data because of its patterns, and its eases retrieval. During the retrieval section of various algorithms that are used in general, here we use the fuzzy logic algorithm in the knowledge retrieval process, simply because the data is stored in a data warehouse so that is not called education. The data is converted into knowledge only if it is retrieved properly. By using the fuzzy logic techniques, the retrieval of data information is done in an efficient manner. Up until now, this study has looked at, medical data storing and retrieval of stored data. The next section will talk about the importance of data transportation. The primary concern with data transportation is the tedious security requirements. The security issue is one of the threatening factors in cloud computing. There are enormous advantages in the field of cloud computing, and its main drawback is security issues. Blanke et al (2015) describes the usage of artificial intelligence to resolve security issues during the data transformation process. Using artificial intelligence, the author found that whether Security Issues and Challenges Concerns Analysis and Findings Limitation Authors Administrative Rights They play a major role as they control all activities and flow in the system Access permissions should be provided to particular employees to access confidential files helps to improve security level for data as well as organization benefits Perry, (2012) Healthcare – Covers the person suffering from Alzheimer’s Disease Current system to diagnose the patient has slight range and is not secure A new method proposed with the long-range outdoor environment with GPS and finegrained distributed data access control. Using location tracking technology, telediagnosis, Access using PKC Part of data is not secure Pramila et al. (2012) Integrating big data with Cloud computing The complexity of cloud increases, as there is no integration of big data with the cloud MapReduce and Task Farming These are used to integrate Big data and cloud, which reduces cost, overhead Madden et al. (2012) Healthcare Privacy preserving for healthcare data. Changing the data values by using noise perturbation, data aggregation, and data swapping. Spent $39.4 billion in 2008 Data masking Motiwalla et al. (2010) Data protection Unauthorized users or hackers access critical data Smart Grid Method To maximize security level of data that helps to secure customer’s personal data as well as health care information Zhang, (2010) Big data management and analytics Implementing infrastructure and high-performance computing technique for storage of big data Attribute-based Encryption Increase security for Big data that is stored in the cloud. Wang et al. (2009) Table 1. Continued 110 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  the medical data is transferred to the correct destination or not detecting injections or hackings during the transformation is important. On top of this, investigating various security features (Pham, 2010a, 2010b, 2011) to avoid hacking so that the system could be an interesting avenue to explore in the future to protect BigData. Implementing artificial intelligence will be a cost consuming process, so to secure the data simply by a data encryption as well as decryption methods. DES encryption algorithm is a method used for the encryption and decryption of the medical data. Therefore, the data information stored in cloud will be retained only in the encrypted format and the encrypted data will be sent during data transfer. Even if there is a hack in between, the hacker may not find anything useful with the data retrieved, as it will not be in an understandable format because it is encrypted. DISCUSSION This chapter has mainly focused on exploring data storage systems from various regional health organizations. It also accomplished the exchange and sharing of medical information between different medical institutions of certain areas that have been studied after a critiquing analysis. Consequently, this chapter proposed a new framework to share medical information across the medical organizations. At present, the information system that is used in regional health organization is still at its initial stage, and their needs to be more development to eliminate possible security and data privacy breaches. Private experts of medical information suggested that the core of regional health information is to acknowledging the importance of shared electronic health records, and electronic transmission. The accessible medical records will create a potential growth in the medical field, however, it is important to develop a sharing framework that gives attention to the security of personal details. Medical organizations share confidential information about the different diseases and patients, so in that situations, they need to enable high security for particular data transmission in a network. As a result, the emergence of cloud computing technology will bring a brand-new understanding for the development of medical information to store and retrieve confidential data. The proposed system in this study has found that, Big Data may have many benefits to the medical organization to collect and store information about medical details of clients. Using big data and cloud computing services will significantly help to increase the assistances of decision making in the management of medical organization. CONCLUSION Cloud computing provides gigantic advantages, and also increases the security levels of medical data. However, there are some difficulties that still exist even after the adaptation and promotion of cloud computing. There is a strong need for an advanced higher functioning tools and approaches to secure confidentiality requirements of an industry that is growing rapidly. This research has established that Cloud computing and big data provides heightened benefits to the consumers when they adopt to improve their working process in an organization. Additionally, this study has found that, the use of cloud and Big Data services also improves the analysis of medically relevant data in the network and the regional 111 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  health information systems which is based on cloud computing that has benefits such as, convenient management, easy extension, flexible investment and low requirements for low technical based personal medical units. This study recommends that the above proposed suggestions have been found by this content that will help to increase the quality of medical reports and enhance health information system and inadvertently leverage their storage systems. The extraction, containment and confidentiality of medical records need to be sustained in order to compete with the growing challenges and demands. REFERENCES Agrawal, V. (Accessed 2016). Securing Big Data On Cloud – Tools and Measures. Retrieved from http://
loud.html Blanke, C. A. (2015). The (Big) Data-security assemblage: Knowledge and critique. Big Data & Society. Brett Hannan, X. Z. (2014, August 11-14). iHANDs: Intelligent Health Advising and Decision-Support Agent. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (Vol. 3, pp. 294 – 301). Campos, L. M. (2010). Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, 51(7), 785–799. doi:10.1016/j.ijar.2010.04.001 Chandrashekar, R. M. K. (2015). Integration of Big Data in Cloud computing environments for enhanced data processing capabilities. International Journal of Engineering Research and General Science, 3(3 Part 2), 2091–2730. Changqing Ji, Y. L. (2012). Big Data Processing in Cloud Computing Environments. Proceedings of the 2012 International Symposium on Pervasive Systems, Algorithms and Networks. Chaves, S. (2011). The Risks Issue in Cloud Computing. doi:10.2139/ssrn.1991156 Chen, Q., & Deng, Q. (2009). Cloud computing and its key techniques. Journal of Computer Applications, 29(9), 2562–2567. doi:10.3724/SP.J.1087.2009.02562 Dean, J. G. C. (2013). Large Scale Distributed Deep Networks. Advances in Neural Information Processing Systems. Feng, J. X., Onafeso, B., & Liu, E. (2016). Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing. Pervasive Intelligence and Computing. Hagos, D. (2016). Software-Defined Networking for Scalable Cloud-based Services to Improve System Performance of Hadoop-based Big Data Applications. International Journal of Grid and High Performance Computing, 8(2), 1–22. doi:10.4018/IJGHPC.2016040101 Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. 112 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Joonsang B., Q. H. (2015, April/June). A Secure Cloud Computing Based Framework for Big Data Information Management of Smart Grid. IEEE Transactions On Cloud Computing, 3(2). Kaur, M., & Singh, R. (2013). Implementing Encryption Algorithms to Enhance Data Security of Cloud in Cloud Computing. International Journal of Computers and Applications, 70(18), 16–21. doi:10.5120/12167-8127 Madden, S. (2012, May). From Databases to Big Data. IEEE Internet Computing, 16(3), 4–6. Retrieved from doi:10.1109/MIC.2012.50 Mandal, S. (2013). Enhanced Security Framework to Ensure Data Security in Cloud using Security Blanket Algorithm. International Journal of Research in Engineering and Technology, 2(10), 225–229. doi:10.15623/ijret.2013.0210033 Motiwalla, L. (2010). Value Added Privacy Services for Healthcare Data. Muhtaroglu, F. C. P., Demir, S., Obali, M., & Girgin, C. (2013, October 6-9). Business model canvas perspective on big data applications. Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA (pp. 32 – 37). Pandith, M. (2014). Data Security and Privacy Concerns in Cloud Computing. IOTCC, 2(2), 6. doi:10.11648/j.iotcc.20140202.11 Perry, D. (2012). Most organizations unaware of employees with admin rights. Retrieved from http://www.,1-353.html Pham, D. V., Syed, A., & Halgamuge, M. N. (2011). Universal serial bus based software attacks and protection solutions. Digital Investigation, 7(3), 172–184. doi:10.1016/j.diin.2011.02.001 Pham, D. V., Syed, A., Mohammad, A., & Halgamuge, M. N. (2010, June 14-16). Threat Analysis of Portable Hack Tools from USB Storage Devices and Protection Solutions. Proceedings of the International Conference on Information and Emerging Technologies, Karachi, Pakistan. doi:10.1109/ ICIET.2010.5625728 Pham, D. V., Halgamuge, M. N., Syed, A., & Mendis, P. (2010). Optimizing windows security features to block malware and hack tools on USB storage devices. Proceedings of Progress in electromagnetics research symposium (pp. 350-355). Pitchai, R., Jayashri, S., & Raja, J. (2016). Searchable Encrypted Data File Sharing Method Using Public Cloud Service for Secure Storage in Cloud Computing. Wireless Personal Communications, 90(2), 947–960. doi:10.1007/s11277-016-3273-1 P.R. (2012). Third Party Data Protection Applied To Cloud and Xacml Implementation in the Hadoop Environment With Sparql. Ramamoorthy, S. (2013). Optimized Data Analysis in Cloud using BigData Analytics Techniques. Proceedings of 4th ICCCNT. Rathod, K. R. (2016). Cloud Computing – Key Pillar for Digital India. International Journal of Information, 6(1/2), 27–33. 113 The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage  Ren, Y. a. (2012, October 30-November 1). A Service Integrity Assurance Framework For Cloud Computing Based On Mapreduce. Proceedings of IEEE CCIS’12, Hangzhou (pp. 240 –244). Suji Pramila, R., Shajin Nargunam, A., & Affairs, A. (2010). A study on data confidentiality in early detection of Alzheimer’s disease. Proceedings of the 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) (pp. 1004-1008). Svantesson, D., & Clarke, R. (2010). Privacy and consumer risks in cloud computing. Computer Law & Security Report, 26(4), 391–397. doi:10.1016/j.clsr.2010.05.005 Thuraisingham, D. B. (2015). Big data security and privacy. Proceedings of the 5th ACM Conference on Data and Application Security and Privacy CODASPY ‘15 (pp. 279-280). Thuraisingham, D. B. (2015). Big Data Security and Privacy. National Science Foundation. Wang, L. J. Z. (2014). BigData Bench: a Big Data Benchmark Suite from Internet Services. State Key Laboratory of Computer Architecture. Institute of Computing Technology, Chinese Academy of Sciences. Wang, Q. C. W. (2009). Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing. ESORICS. doi:10.1007/978-3-642-04444-1_22 Waseem, M., Lakhan, A., & Jamali, I. (2016). Data Security of Mobile Cloud Computing on Cloud Server. OALib, 3(4), 1–11. doi:10.4236/oalib.1102377 Zhang, T. (2010). The design of information security protection framework to support Smart Grid. Proceedings of the 2010 International Conference on Power System Technology (POWERCON) (pp. 1-5). Zhang, W., Han, S., He, H., & Chen, H. (2016). Network-aware virtual machine migration in an overcommitted cloud. Future Generation Computer Systems. doi:10.1016/j.future.2016.03.009 Zhang, X., Dou, W., Pei, J., Nepal, S., Yang, C., Liu, C., & Chen, J. (2015). Proximity-Aware Local- Recoding Anonymization with MapReduce for Scalable Big Data Privacy Preservation in Cloud. IEEE transactions on computers, 64(8), 2293–2307.

Leave a Reply

Your email address will not be published. Required fields are marked *