University Data Classification Policy Research Report

This report delves into the intricacies of data classification policies, a crucial aspect of data security and management. It begins with an introduction to data classification, defining its purpose and importance in organizing and securing data within organizations. The report then explores the advantages and disadvantages of implementing such policies, highlighting their role in mitigating data breaches and ensuring regulatory compliance. A significant portion of the report is dedicated to addressing the challenges associated with data classification, such as labeling critical assets, privilege management, and maintaining compliance. The report includes an annotated bibliography of relevant research papers, providing an in-depth analysis of each source, its methodology, and key findings. The annotated bibliography includes papers focusing on data classification management using hybrid PRAM memory, organizational data classification based on complex networks, data classification for achieving security in cloud computing, and a meta-heuristic model for data classification using target optimization. The conclusion summarizes the key findings, reiterating the importance of data classification in modern business environments and the need for continuous adaptation to evolving security threats.

Running Head: DATA CLASSIFICATION POLICY
0
Data classification policy

DATA CLASSIFICATION POLICY
1
Table of Contents
Introduction...........................................................................................................................................2
Overview of data classification policies.................................................................................................2
Challenges faced by a data classification...............................................................................................3
Working off data classification policies..................................................................................................4
Advantages of data classification..........................................................................................................4
Disadvantages of data classification......................................................................................................4
Conclusion.............................................................................................................................................4
Annotated bibliography.........................................................................................................................6
References.............................................................................................................................................9

DATA CLASSIFICATION POLICY
2
Introduction
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.
Overview of data classification policies
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

DATA CLASSIFICATION POLICY
3
management technique which can be utilized as a method for categorization of
organization data (Morente, et al., 2017).
Challenges faced by a 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).

DATA CLASSIFICATION POLICY
4
Working off data classification policies
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).
Advantages of data classification
 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).
Disadvantages of data classification
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

DATA CLASSIFICATION POLICY
5
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.

DATA CLASSIFICATION POLICY
6
Annotated bibliography
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.

DATA CLASSIFICATION POLICY
7
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.
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

DATA CLASSIFICATION POLICY
8
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.

DATA CLASSIFICATION POLICY
9
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.