Security and Privacy Issues in Analytics - Dumnonia Company Report

This report delves into the critical security and privacy issues within data analytics, using Dumnonia, a UK-based financial company, as a case study. It examines organizational drivers, particularly the need for robust data protection and the implementation of k-anonymity to safeguard customer information. The report provides a detailed assessment of technology solutions, including the challenges of big data security, cyber threats, and the importance of encryption. It explores the implementation guide for k-anonymity, compares different implementations, and offers recommendations for Dumnonia to enhance its data privacy measures. The study emphasizes the importance of data provenance, cloud server security, and regular audits to protect against data breaches and ensure the confidentiality of sensitive information. The report highlights the need for proactive security measures, including encryption and strict access controls, to mitigate risks and maintain customer trust. The report concludes with a call for continuous vigilance and adaptation to address evolving security threats in the ever-changing landscape of big data analytics.

Security and Privacy Issues in Analytics 1
SECURITY AND PRIVACY ISSUES IN ANALYTICS
Student ID Number
Module Code
Year Module Run
Assessment Title: Security and Privacy Issues in Analytics

Security and Privacy Issues in Analytics 2
TABLE OF CONTENTS
1. Executive Summary 3
2. Introduction 4
2. Organisational Drivers for Dumnonia 4
3. Discussion of organisational drivers for Dumnonia and implementation of k anonymity 5
4. Technology Solution Assessment 6
5. Detailed assessment of the technologies for k-anonymity as a model for protecting privacy 7
6. K-anonymity Implementation Guide 9
7. Comparison of two publically available implementations 13
8. A guide for Dumnonia and k-anonymity to protect the privacy of organisational data 15
9. Conclusion 16
10. References 17

Security and Privacy Issues in Analytics 3
Executive Summary
Dumnonia is largest financial private company based in the streets of Meadow
Avomouth in the United Kingdom (UK). It is the leading company in the whole of Australia
offering insurance services to their customers and provides huge data information of various
types of insurance. Arguably, the company offers large data concerning the security threats of
leakage information that contains the analysis and security of the company. However, the
company also offers services such as the medical and other related issues problems with their
clients and their employees’ concerning the security of the company. The emerging issues in the
company include traditional security operations i.e. the firewalls, IDS, technological issues such
as the passwords, security corporations, the encryption, and software protection.
The study shows that the issues concerning the security protection of the company with
their data systems and management solutions by applying various mechanisms for large data
security. Consequently, big data tend to enlarge with the security of the unrestricted cloud that
are meant for offering the private infrastructures for the firewall perimeters and the marginalized
zones that are not significant in handling the security aspects of the company. The current
organization drivers for Dumnonia drivers have the basis on the cloud information technologies
that are using the set standards of initial piloting concerning the clouds AWS services. The
studies also show that Dumnonia shift happens in the cloud data system that requires other
means concerning the security aspects of the organization. The CEO corporate in the Dumnonia
Company makes use the applications k-anonymity in the IT systems on the basis of the mobile
gadgets.

Security and Privacy Issues in Analytics 4
Introduction
The security agencies of Dumnonia regarding the privacy issues of their huge data
analysis and privacy is mainly based on the challenges pertaining to the personal data. The
company should improve its systems for supporting huge data (Pearson, 2013). The SDN is
regarded as the emerging solution to the recent management with the mechanisms put in place
for addressing the dematerialized zone that aids in large data security systems. Therefore, dataset
management is beyond the powers of applying software systems for the purpose of capturing the
timely analytic data set. Dumnonia Company has implemented various methods of anonymity
with their data by maintaining they're statistical analytic concerning the data mining systems that
enable proper developments of the personalized models. Dumnonia Company concerns itself
regarding the privacy of the customers on the medical services, data records and offers a
guarantee to the assurance of their personal information records (Bygrave, 2014).
Organizational Drivers for Dumnonia
Dumnonia Company has the following drivers that make its operations effective:
strategy, support decision data operation, processes, administration/leadership, and Customer
response. Organizational drivers in Dumnonia Company are used as the measures in the business
processes of the organization. These drivers are termed for achieving the company goals.
According, to Shin (2010) the Company should run its premises Caradoc (Chief Executive
Officer-CEO) for proper implementation of the K- anonymity operations. The organization is
required to draw all the available data information and the management of the company has been
a success (Takabi, Joshi and Ahn, 2010). The CEO of the company has a greater insight
regarding the data Dumnonia company holds that enables the sharing with other organizations

Security and Privacy Issues in Analytics 5
and incorporating the government agencies. The company strategies are set to give the
organization direction and the focus for better implementation (Wang et.al. 2010).
The support and decision in the company are analyzed through a methodical process for
the management for determining the problems, how the data is gathered before making the
decisions the customer responses and feedback in the Company. The various activities that are
conducted by the company should be well coordinated and reported subject for action and the
discipline of the organization. Caradoc the CEO manages the customers, the workers and how
the information is delivered for quality implementations of the company. Leadership is the key
driver in the company due to its support it provides for the success of the company working
strategies, improved processes and working as a team for the success of the company. Caradoc is
basically concerned on the privacy assurances of Dumnonia Company for the overall, success of
their projects and the system management (Chen and Zhao, 2012).
Discussion of organizational drivers for Dumnonia and implementation of k anonymity
The study shows that Guinevere discussion on the aspects concerning k-anonymity and
its implementations for Dumnonia Company. CIO interview supports the new organizational
drives on the strategies of the k-anonymity system approach. However, the challenge exists on
the private data where personal information of the individuals is identified by the use of key
drivers for Dumnonia huge data strategy and the privacy of the organization. Guinevere
discussed various issues that are held by the customer’s individual medical data concerning
security and personal privacy with their security. It was discussed that k-anonymity approach
method was the quality system for Dumnonia implementation. A wide variety of the k-

Security and Privacy Issues in Analytics 6
anonymity models are significant techniques for application of the algorithms and targeting
solving the existing solutions (Pearson and Benameur, 2010).
Some of the major issues that K-anonymity discussed include privacy models, data
management and quality metric systems for Dumnonia Company. K-anonymity was the best
method for the implementation of the data set for the fields where the value of k=10 and other
records attributing to the anonymization customer relation and the feedback drive. The concern
on the anonym sings the aims of the personal security attributed to the data storage and how
other factors contribute to re-identification of the user's privacy. Administrative/leadership of the
Dumnonia relates with k-anonymity through maintaining the privacy of the client's information
and securities of their data nit to leaked away and ensure the problems concerning the platforms
are safe (Bennett and Raab, 2017).
Technology Solution Assessment
The challenges facing the company include; securing data and proper safeguarding of the
personal information and privacy of the clients. Big data requires high security of the client’s
information’s hence the need for the provision of security and the following technological
solutions are important. It is worth noting that when the information of the company is produced
concerning the individual information of the customers, the company is required to take
necessary measures to maintain the balance among the privacy and the individual information.
However, the Comprehensive assessment concerning the technological aspects for k-anonymity
was regarded as the model providing the security and protection of the privacy of the customer’s
big data. The study involved Constantine in developing some of the starting huge data aspects for
Dumnonia Company system as the CSO of the organization (Jain Gyanchandani and Khare,

Security and Privacy Issues in Analytics 7
2016). The study investigates how the organization understands the use of technology such as U
based UDP transfer of data records, the performance of the designed UDP for transferring big
datasets. the company should come up with new ways of storing their information, the
encryption performs various operations on the information (Hashzume et.al. 2013). However,
Constantine is much concerned about the attacks on cyber security that challenges Dumnonia
Company regarding its huge data systems. Arguably, for a single attack on the random wares that
might cause a great loss on Dumnonia Company data systems in relation to the subject demands
from the customers. The threat on the accessibility of the information by the unauthorized users
might cause a big problem on the gathered information in the company (Svantesson and Clarke,
2010).
Consequently, the vulnerability of Dumnonia company information to the frauds
generations, the attackers might cause great danger to the big data systems. For instance, the
employee’s information and their clients should be updated to date to generate accurate medical
information/data so that the organization is provide their systems with proper information hence
avoiding incorrect data processing. Wrong information regarding the client’s data could be
generated due to the incorrect information provided by the client in the first place. When the
organization information is stored, it is evident that the encryption might facilitate the stored data
in the cloud operations over the newly encrypted results that were formed. As a result, the data is
decrypted through the process by taking the observations on the functions that were carried
during the plaintext data (Aldeen, Salleh and Razzaque, 2015).
The problem underlies on the aspects of using huge dataset and how to own the
information fully with maximum privacy and protection. Dumnonia organization requires very
strict data protection on the access points and modifies the mechanisms for controlling their

Security and Privacy Issues in Analytics 8
systems. For instance, hacking of the information from the company website should be
controlled and operated from the controlling rooms by restricting the fraudulent access to the
data of the customers. Shaikh and Haider (2011) assert that one of the decent ways of dealing
with such issues is to apply the defense mechanism to the data of the clients through encryption
techniques that are sensitive for fraud cases. Therefore encryption and decryption key technology
should be improvised for securing the information of the customers all the time the transactions
are made.
The problem facing Dumnonia Company is the software that is in use for storing large
data information that is always accessible for their customers to use authentication through
default. This now worsens the situation of accessibility since the default installer would precede
the information available for unauthorized user’s accounts. However, the main concern on data
privacy is for the protection of clients from cases of fraudulences. The Company should
technically ensure that all of their parameters are in good security and their systems properly
functioning well, the entry point and exit points should be tightly secured. Implementation of the
encryption with the dataset aspect in the company should be properly directed with serious
measures on the operations done on the Dumnonia systems (Mowbray and Pearson, 2009).
Operations on the system such as calculation, process for large size of customers data
systems in the company. Arguably, when the data exists for various systems and enters to
unsecured data source the information should be under data constant view of the operation and
monitored by Dumnonia technology mechanism s that are put in place. Big data should be taken
as a new concept sufficient for the practice and needs [roper security recommendations that can
be implemented for guarding the information of Dumnonia Company safely (Grunwald, 2009).
Data provenance should start from the security management of Dumnonia Company by taking

Security and Privacy Issues in Analytics 9
actions on the viewpoints that are the sources of cyber-attacks, the organization should keep the
past records data of the company as documentation source for data and keep in check of any
manipulations done on the personal accounts of the owners for the purposes of ensuring privacy
and security within the company (Danezis and Gürses 2010) Regular checkup on the cloud
servers is important for data stored has got sufficient protection/security devices. Gedic and Liu
assert that the servers in the cloud should run non varying scrutiny auditing and any case of
irregularities the penalties should be imposed for those who forfeit the standards of the company.
Adequate accessibility mechanisms policy should always be implemented to provide access
(Gedik and Liu, 2008). At the same time, K-anonymity model should work in such a way to
mitigate the requirements that encrypts data/information for anonymous administering of the
Company. Therefore, unauthorized users are immediately blocked from reaching the customers
metadata results to the hands of the strange attackers’ data sets. However, untraceable data
informants who work in big data field to find out the sources of the protection breaches make the
operations difficult for the strangers from accessing (Pearson, 2013).
Data protection is important right from the raw state for the outcome of the analysis to
ensure sensitive information does not leak away with the encryptions of the Dumnonia
Company. Adequate, protection against the communication of the client data with the intruders
in the process of transit for ensuring confidential and the integrity of Dumnonia Company. The
company should take full control by ensuring the customer’s information is safe so as to prevent
accessibility of their data. Most important the data should be anonym zed for the purpose of
privacy concerns by checking the record provided by the customers and the changes that have
been made. The threat intelligence should be put in place for securing the organization
information from the attacks and quick responses to the fraudulent reported (Zhou, et.al. 2010).

Security and Privacy Issues in Analytics 10
Despite the challenges facing huge data information of Dumnonia Company, the
applications and innovations assist in securing the company data. According to Federici and
Scherer (2017) big data analytics has assisted the organization in tracking security threats
towards large volumes of data. With innovation in technology, the company should structure
various sources such the emails, surveillance video monitors, online traffic, and the e-commerce
transaction. The analytical devices would assist in securing the personal information of the
clients by creating law reinforcement in fighting against cybercriminals. The significance of
these technological instrument’s assist in the inspection of the clients data and the provide
protection of their information (Federici and Scherer, 2017). Dumnonia Company should adopt
the data analytics devices for computer crimes, terrorism, cyber protection, crime analysis and
informatics for the safety of the client’s information.
Technologies Incorporated for implementing k-anonymity for securing data of customers.
Dumnonia Company has incorporated several technologies for proper implementations for k-
anonymization for example Computational Based Dataset Transfer Protocol (UDP) designed to
offer high performances on the huge data transfer in the company. The use of this technology is
effective in data recording and transfer of the volumetric data systems for a wide range of
network coverage with fast speed. Arguably for implementation purposed the use of cloud web
technology systems have resulted in the implementations for securing the personal information
of the clients. However, the concern on the use of technologies in safeguarding the personal
information of the clients, Dumnonia Company considers securing their clients against cyber-
attacks that are rampant worldwide (Federici and Scherer, 2017). K-.anonymity model has made
it difficult for the attacker accessing the Dumnonia data through the fabrications of data systems
guarding the information of the clients. Implementation of encryption technological models in

Security and Privacy Issues in Analytics 11
the organization secures the company larges size data sets by slowing down the operations for
proper encryption and decryption of the information.
The K-anonymity Technique and Implementations Guide
K-anonymity model make use of known technology for various huge data anonym zing.
The estimated value for k- represents the original data set information that contains every
individual’s personal information that are either transferrable for making the operations become
difficult for the fraudulent to access. The study shows that k-anonym zed as a model for keeping
the records k of similar values of another taken from different value taken as k=1 (Gharibi and
Shaabi 2012). However, the research study chose the estimate value for K to k=10 according to
the potential of identifying various variables such as gender-male/female, age difference for
adult, youth and children , and even the data sets for overall combination. Arguably, the guided
value for k-anonymity make use of k-anonymity techniques examples include the following i.e.
suppression, generalizing techniques, and global recordings. Probably the recordings applied
with the K-anonymity for large datasets comprises of maximum probability sets of re-
identification of the data and information (Aldeen, Salleh and Razzaque, 2015).
Large information stored in various selections for different values of k corresponds in a
similar manner such as the re-identification measures that are threshold risk. Arguably, for
measured values for k are maximum implies that a low probability is expected for the purpose of
re-identification processes; it is however noting that the greater the distortion of the information
lost the low the value of k anonymization. The company compiles their information by ensuring
the present algorithms are well, implemented for security purpose. The extreme anonymization
might result to denial of the information for clients .Although, according to Lyon, (2009)

Security and Privacy Issues in Analytics 12
effective measures should be encouraged in the organization for sufficient privacy of the
personal information of the clients. However, the challenge results for loss of information and
big distortions on the results for k-anonymization. Addressing some challenges, it is worth
noting to address the three modifications towards k-anonymity (Svantesson and Clarke, 2010).
The guidelines provided for the provision of the baseline for k-anonymity modification
procedures for controlling the risk using k-anonymity. However the major concern of the k-
anonymity used to identify the individual data set of the clients for the re-identification where the
information existing in the data sets is for a particular person no one else (Shin, 2010).Medical
records for Dumnonia are anonimyzed earlier before the files are presented for the purpose of
mapping of the personal information of the customers. Re-identification for k-anonymity will
ensure there is similar threshold risk as intended by the individual information and data records.
It is evident from table 1.0 that the databases for the three are estimated to be the private key for
the customer’s information through the use of various techniques for re-identification
(Svantesson and Clarke, 2010).
Table 1.0

Security and Privacy Issues in Analytics 13
Arguably the similarity of the data is assumed as where
represents the sample size and Z equal to the equivalence value. Considerably, the anonym
zed files show how the age converts into k=10 for the given intervals. (Danezis and Gürses
2010). However, the data used for the custodian represents the threshold risk roughly as 0.5 of
the actual re-identification. Despite f the negative the quality is considered for the results of the
information stored. Therefore, the anonym zed data results analyzed for the conversion of
suppressions for the personal use. Majority of the datasets get lost in the hands of the attackers
who falsely access the individual information of the clients. However, technology improvements
and transformations are required for regular checkup of the customers (Chen and Zhao, 2012).
K-
anonymity

Security and Privacy Issues in Analytics 14
The baselines (DI) apply the techniques of the k-anonymization by using the algorithms
for the purpose of recording the client’s information. Equally, the generalizations of the
Dumnonia company data are transformed for the equivalent classes for the suppression of the
data suing k-anonymization (Pearson, 2013). The actual re-identification of the individual risk
can be estimated directly for the subsequent algorithms so that the equivalence of the classes is
similar. The estimates are then incorporated extensively for the purpose of statistical agencies by
using the Margus devices for disclosure control. The device is used for personal risk estimation;
however, it is accurate in the data protection of the individual’s information. The study shows the
performance of the k-anonymity technique that is performed by simulation of k-anonymized with
the re-identification measures (Bygrave, 2014).
Comparison of two publically available implementations
The value of k was estimated as k=5 by the use scatter graphs for making the comparison of the
dependent and independent value. Different approaches applied include the K anonimized,
despite the applied methods in securing the records of the customers. From FigD3 the figures
show K- anonymization for maintaining data below the approximated threshold implying that the
technique is quite unsuitable for the purpose of practical use. (Stacey and William, 2012).
Consequently, the approach D4 maintains the real value of the k-anonym zed regarding the
actual security threat concerning the threshold in comparison to D3 that is better working and
consistent. However, then technique is one of the best too for the practical’s in the field for
storing data and information of the company (Lyon, 2009).

Security and Privacy Issues in Analytics 15
A guide for Dumnonia and k-anonymity for securing organizational data
The organization has networked itself properly to a global level for t higher security of
the private information of their clients. Statistical information of the clients requires detailed
privacy when making any transaction or transfer of their information. However, the information
of the customers is electronically stored; the data requires protection from accessibility from the
attackers (Bennett and Raab, 2017). The company improvised k-anonymization for securing
information of their potential clients against cyber-attacks. Identification of the individuals
should be done by the use of the fingerprints to ensure the right way of accessing the information
such as the use of mobile numbers and the correct names. Dumnonia has put in place k-
anonymity for protecting the personal information of the individuals through encrypts identifiers
of the data holders with their personal addresses. However, the combinations of k-anonymity and
distinctive data have assisted in the identification of the individuals with securing their data in
the company (Yang, Yang, and Zhang 2011).
k-anonymity

Security and Privacy Issues in Analytics 16
Although the challenge on safeguarding the personal information of the individuals
remains a threat in the technical aspects, the company has innovative approaches that provide
specific outcomes of each account holders in the organization (Zhang et.al. 2015). K-Anonymity
provides the specified links that contain the individual’s information for the k-identities provided
through generalization methods and suppression approaches. The processes provide full details
of the data holder’s between the various lowered generalizations in the process to avid data
distortion of the clients. Dumnonia Company has put on place strict policies for guarding the
information of the clients inclusive of the reduced generalizations. The company makes its
reports to the clients when the implementations ns are done and presents the algorithms that are
used for procures of securing their client's information. Quality and effective measures that put in
place in the organization through the use of k-anonymity has provided sufficient privacy of the
personal information of the clients (Bygrave, 2014).
Conclusion
In conclusion, huge data privacy is an essential issue in any company due to the direct
relation to the account owners and the employees of Dumnonia Company. Arguably, it worth
noting that the organization should put in place the measures for protecting the privacy and
personal information of the clients from cyber attackers. However, providing such privacy and
security of the large information requires Dumnonia Company to guard the large data by
applying powerful devices that are friendly to the users. Anonymization techniques have limited
the fraudulent from accessing the individual’s information hence creating the privacy of their
customers protected. In general, protecting their personal information of the clients requires the
organization to combine various methods such as differential privacy off data with k-anonymity
as a solution to large data privacy (Pearson, 2013).

Security and Privacy Issues in Analytics 17
References
Aldeen, Y. A. A. S., Salleh, M., & Razzaque, M. A. (2015). A comprehensive review on privacy
preserving data mining. SpringerPlus. 4, 1-36.
Bennett, C.J. and Raab, C.D., 2017. The governance of privacy: Policy instruments in global
perspective.
Chen, D. and Zhao, H., 2012, March. Data security and privacy protection issues in cloud
computing. In 2012 International Conference on Computer Science and Electronics
Engineering (Vol. 1, pp. 647-651). IEEE.
Danezis, G. and Gürses, S., 2010. A critical review of 10 years of privacy
technology. Proceedings of surveillance cultures: a global surveillance society, pp.1-16.
Federici, S. and Scherer, M., 2017. Assistive technology assessment handbook. CRC Press.
Gharibi, W. and Shaabi, M., 2012. Cyber threats in social networking websites. arXiv preprint
arXiv:1202.2420.
Griffin, P. and Care, E. eds., 2014. Assessment and teaching of 21st century skills: Methods and
approach. Springer.
Grunwald, A., 2009. Technology assessment: concepts and methods. In Philosophy of
technology and engineering sciences (pp. 1103-1146). North-Holland.
Hadavi, M. A., Jalili, R., Damiani, E., & Cimato, S. (2015). Security and searchability in secret
sharing-based data outsourcing. International Journal of Information Security. 14, 513-529.

Security and Privacy Issues in Analytics 18
Hashizume, K., Rosado, D.G., Fernández-Medina, E. and Fernandez, E.B., 2013. An analysis of
security issues for cloud computing. Journal of internet services and applications, 4(1), p.5.
Jain, P., Gyanchandani, M. and Khare, N., 2016. Big data privacy: a technological perspective
and review. Journal of Big Data, 3(1), p.25.
Krutz, R.L. and Vines, R.D., 2010. Cloud security: A comprehensive guide to secure cloud
computing. Wiley Publishing.
Lyon, G.F., 2009. Nmap network scanning: The official Nmap project guide to network discovery and
security scanning. Insecure.
Mowbray, M. and Pearson, S., 2009, June. A client-based privacy manager for cloud computing.
In Proceedings of the fourth international ICST conference on COMmunication system softWAre
and middlewaRE (p. 5). ACM.
Pearson, S. and Benameur, A., 2010, November. Privacy, security and trust issues arising from
cloud computing. In 2010 IEEE Second International Conference on Cloud Computing
Technology and Science (pp. 693-702). IEEE.
Pearson, S., 2013. Privacy, security and trust in cloud computing. In Privacy and security for
cloud computing (pp. 3-42). Springer, London.
Shaikh, F.B. and Haider, S., 2011, December. Security threats in cloud computing. In 2011
International Conference for Internet Technology and Secured Transactions (pp. 214-219).
IEEE.
Shin, D.H., 2010. The effects of trust, security and privacy in social networking: A security-based
approach to understand the pattern of adoption. Interacting with computers, 22(5), pp.428-438.

Security and Privacy Issues in Analytics 19
Stacey, K. and Wiliam, D., 2012. Technology and assessment in mathematics. In Third
international handbook of mathematics education (pp. 721-751). Springer, New York, NY.
Svantesson, D. and Clarke, R., 2010. Privacy and consumer risks in cloud computing. Computer
law & security review, 26(4), pp.391-397.
Takabi, H., Joshi, J.B. and Ahn, G.J., 2010. Security and privacy challenges in cloud computing
environments. IEEE Security & Privacy, 8(6), pp.24-31.IS (pp. 63-80). CRC press.versity
Press.ent advances.
Wang, C., Wang, Q., Ren, K. and Lou, W., 2010, March. Privacy-preserving public auditing for
data storage security in cloud computing. In 2010 proceedings ieee infocom (pp. 1-9). Ieee.
Yang, G.M., Yang, J. and Zhang, J.P., 2011. Achieving( alpha, k)-anonymity via clustering in
data publishing. Dianzi Xuebao(Acta Electronica Sinica), 39(8), pp.1941-1946.
Zhou, M., Zhang, R., Xie, W., Qian, W. and Zhou, A., 2010, November. Security and privacy in
cloud computing: A survey. In 2010 Sixth International Conference on Semantics, Knowledge
and Grids (pp. 105-112). IEEE.