Data Classification: Overview, Challenges, And Advantages And Disadvantages Essay.

Select one of these sections to research and find 4 relevant papers which assist someone developing a policy.
Compose an annotated bibliography of research papers that relate to the issue so your annotated bibliography will help you (and others) to have a good resource to evaluate and think about the relevance and quality of material on the topic.

Write a concise annotation that summarises the central theme and scope of the book or article.

Include one or more sentences that
• evaluate the authority or background of the author,
• comment on the intended audience
• compare or contrast this work with another you have cited,
or
• explain how this work illuminates your bibliography topic.

Overview of Data Classification

Data classification is a kind of process which is used for shorting and dividing data or information into different types, forms, and other separate class. It enables the classification and separation of data according to the requirement of data sets for different organizations and it is also called as data management method (Chen, et al., 2016). The main purpose of this report is to understand the fundamental concept of data classification and their strength and weakness. This process uses the database system to divide data of any organization into numbers of data sets and it has the ability to scan and identify any kind of data. This report is categorized into four parts for example overview of data classification, challenges faced by this technology and their advantages and disadvantages.

A data calcification is an advanced technology which is used to secure data of an organization and it is the first step to secure information of any consumer. The main concept of this process is that it classified data based on the nominal values for example; data can be divided as internal, confidential and public (Deng, et al., 2017). A large amount of data is also increased various kinds of security threats and risks and many organizations are suffering from the issue of data conflict. This type of process can be used to control the unauthorized signals and protect data or information of any user. Data breach is the very common problem of any computer network and data classification technique has the capability to address the issue of the data breach. Highly confidential is the first step of data classification and it is used to label all data of an organization that cloud cause serious harm if it is hacked by attackers (Grinblat, Gilichinsky, and Benenson, 2016). After that, these label applies to all datasets which are used in the process of sensitive and at the end of this method data divided into numbers of data sets according to their requirement. It is a part of the classification process that improve the efficiency and effectiveness of data. Data classification is also very important for legal discovery, compliance and risk management. The main problem of this process is that if hacker enters into organization servers then they can easily block employees private details like login ID and passwords. In the sector of data management process data classification is a part of an information lifecycle management technique which can be utilized as a method for categorization of organization data (Morente, et al., 2017). 

Challenges Faced by Data Classification

There are many security challenges and issue faced by data classifications which are described below:

• Labeling critical assets and resources
• Standardize and classify
• Privilege management
• To maintain compliance
• Disaster recovery
• Data can be lost at any step due to which many organizations can lose their private details
• Awareness (Nguyen, et al., 2015).

There are many organizations that have data classification policies to reduce security threats and risk and there is a government policy which is unenforced to the community’s consumers. It is very difficult for any company to make the trust between their employees and customers and it is observed that lack of security is a very common problem by which employees can lose their personal data. A data classification policy is defined as a protection process to manage the information of any business industry. It is also used to ensure that sensitive information or data is controlled with a security plan and it can be used to gather data of employees. There are following steps are used to handle risks and threats of data classification:

• Investigate the most important data or information
• Set enforceable programmes and policies
• Produce a weekly report on security-related issues
• Trust and verify data from authorized servers and networks (Powell, et al., 2015).

A data classification maps out the numbers of components and equipment in an organization. The main use of this technology is to categorized data or information into different types of data sets and it also classifies the documents according to the requirement of any organization. These data may be divided into three parts for example, sensitive confidential and the public. This technology is adopted by many industries and communities because it has the ability to detect any security regarding issues and also resolve them (Varatharajan, Manogaran, and Priyan, 2018).

• This process helps an association to understand the concept of data sets and their availability
• This is very effective and more efficient system to protect data from hackers
• It can help companies to meet regulatory compliance as well as consumer’s expectation
• Can be used to optimize the security threats and risks
• Very simple and easy to understand
• Take very less time to categorized data into different parts
• Employees can enhance their performance and productivity (Nguyen, et al., 2015).

This technology is very complex to design and implement

• Very costly
• Limited memory
• It cannot be used to detect malware and other types of issues
• It is less secure by which consumers can lose their personal information
• Data can be lost at any classification step (Nguyen, et al., 2015).

Conclusion

Data classification is a very common technology which is used by many bossiness industries to avoid the problem of a data breach. The main benefit of this process is that it can categorize the data into numbers of sub-data according to the requirement of the organization. This report described the working principle of data classification and issues faced by this technology during the classification of data. There are many applications of this technique, for example, as a security plan, consumers can save their personal information, used a detection process, used for target marketing and used for medical diagnosis. In this report, the researcher identified the advantage and disadvantages of data classification and also provided an overview of this technique. Employees should ensure that they use only authorized networks and websites because hackers developed many their party websites and server by which they can block their personal information. Moreover, consumer scan adopts the various type of security plans like firewall, antivirus and encryption method.

Advantages and Disadvantages of Data Classification

Jang, S.I., Yoon, S.K., Park, K., Park, G.H. and Kim, S.D., 2014. Data classification management with its interfacing structure for hybrid SLC/MLC PRAM main memory. The Computer Journal, 58(11), pp.2852-2863. 

The title of this paper is Data classification management with its interfacing structure for hybrid SLC/MLC PRAM main memory that was printed by Jang, S.I., Yoon, S.K., Park, K., Park, G.H. and Kim, S.D. according to this article to replace the DRAM memory with non-volatile PRAM is very common method to improve the efficiency of data classification process. The main objective of this paper is to design a new PRAM based memory structure and analysis of the concept of data classification policies. In which author identify the importance of data classification in an organization and interfacing structure for PRAM. The array of hybrid PRAM is defined as a combination of MLC and SLC to increase the lifetime of the MLC PRAM. According to the writer, there are few drawbacks of this process, for example, excessive superblock fetching and increase the rate of buffer space. To reduce these types of problems researcher suggested an optimization buffer structure due to which consumers can improve the efficiency of the data classification process. There are many types of memory system produced by information technology, for example, DRAM convertor, CMT, SLC, and MLC, PRAM structure. In this paper author used qualitative data analysis and quantitative method to understand the concept of data classification. Qualitative data provide the theoretical information about the research topic and the researcher also conducted a survey by which they can improve the effectiveness of this investigation. Therefore, in this article writer explained the process of data classification and different types of steps to improve the efficiency of this technology. The main benefit of this article is that it is completely based on the data classification management by using hybrid PRAM memory.

Carneiro, M.G. and Zhao, L., 2018. Organizational Data Classification Based on theImportanceConcept of Complex Networks. IEEE transactions on neural networks and learning systems, 29(8), pp.3361-3373.

The title of this article is Organizational Data Classification Based on theImportanceConcept of Complex Networks which is based on the threats and issues of data classification. The main aim of this paper is to describe the challenges faced by data classification method and optimization step to improve the security of this technology. According to the author the data classification is a very common process that can be performed with the help of computer devices and human beings. The main difference between both methods is that computer-based approach defines only physical features and but human beings can control both physical features and organizational features.in this paper, the researcher explains the data organizational system for the classification of data by using complex networks. In data classification process first data is divided into numbers of data sets and test instance is transferred into the computer network and optimization is the very best process to identify the potential threats and risk of data classification. In this article author used only qualitative research design to gather knowledge in the field of data classification and data is collected from both primary and secondary method. They also collect relevant information from the literature review and in which data is analyzed from various resources like journal papers, books, and online websites. therefore, this journal paper defined a new approach that is an optimization technique to avoid the drawback of the data classification process.

Data Classification Policies and Their Importance

Shaikh, R. and Sasikumar, M., 2015. Data Classification for achieving Security in cloud computing. Procedia computer science, 45, pp.493-498.

The title of this journal paper is Data Classification for achieving Security in cloud computing which was developed by Shaikh, R. and Sasikumar, M., in the year 2015. Data is the very important key element for an organization and this paper explains the cloud computing technology to improve the security of data classification. The aim of this journal is to detect security issues of data classification and methods to enhance the efficiency and performance of this technology. Cloud computing is a modern technique to control and monitor security-related issues and many organizations are using this technology to improve their efficiency. The author used both quantitative and qualitative approach to enhance their knowledge in the field of data classification and a survey is conducted to achieve the objective of this investigation. The main advantage of this paper is that the writer provided complete information regarding issues of data classification technique. Therefore, with the help of cloud-based services consumers can resolve the problem of data breach and conflict and it has the ability to control the classification of data.

Barik, R.K., Priyadarshini, R. and Dash, N., 2017. A Meta-Heuristic Model for Data Classification Using Target Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 8(3), pp.24-36.

This paper is written by Barik, R.K., Priyadarshini, R. and Dash, N in year 2017b and they identified that lack of security is the very common issue for any business industry due to which users can lose their personal data. The goal of this research paper is to describe the working principle of target optimization to increase the privacy of data classification. It is observed that at the time of training programmes of data classification output is computed from the main two key elements such as target and input. In which the author used primary and secondary research method to collect data and researcher also conducted a literature review to improve the efficiency of this research.

References

Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., (2014) Deep learning-based classification of hyperspectral data. IEEE Journal of Selected topics in applied earth observations and remote sensing, 7(6), pp.2094-2107.

Deng, Y., Ren, Z., Kong, Y., Bao, F. and Dai, Q., (2017) A hierarchical fused fuzzy deep neural network for data classification. IEEE Transactions on Fuzzy Systems, 25(4), pp.1006-1012.

Grinblat, Y., Gilichinsky, M. and Benenson, I., (2016) Cellular automata modeling of land-Use/Land-Cover Dynamics: questioning the reliability of data sources and classification methods. Annals of the American Association of Geographers, 106(6), pp.1299-1320.

Morente-Molinera, J.A., Mezei, J., Carlsson, C. and Herrera-Viedma, E., (2017) Improving supervised learning classification methods using multi-granular linguistic modeling and fuzzy entropy. IEEE transactions on fuzzy systems, 25(5), pp.1078-1089.

Nguyen, T., Khosravi, A., Creighton, D. and Nahavandi, S., (2015) Medical data classification using interval type-2 fuzzy logic system and wavelets. Applied Soft Computing, 30(5), pp.812-822.

Powell, J., Torres-Forné, A., Lynch, R., Trifirò, D., Cuoco, E., Cavaglià, M., Heng, I.S. and Font, J.A., (2017) Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data. Classical and Quantum Gravity, 34(3), p.034002.

Varatharajan, R., Manogaran, G. and Priyan, M.K., (2018) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 77(8), pp.10195-10215.