SIT719: Report on Primary and Secondary Analytics Issues at Dumnonia

This report provides a comprehensive technology assessment for the Dumnonia Corporation, an Australian insurance provider facing challenges in securing its big data systems. The report addresses the organization's concerns regarding data security, particularly the vulnerability of sensitive customer data and the need for a robust security management strategy. It explores the implementation of k-anonymity as a potential solution, analyzing the organization's drivers for adopting this approach, conducting a technology assessment, and providing an implementation guide. The assessment highlights the significance of k-anonymity in enhancing system security and privacy, especially in the context of cloud-based big data systems. The report also examines the organization's current security measures and discusses the advantages and disadvantages of various k-anonymity extensions, emphasizing the importance of addressing security and privacy risks associated with big data analytics. Ultimately, the report recommends the adoption of k-anonymity to improve data security and protect customer information, especially as Dumnonia seeks to expand its operations and data volume.

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A report Secondary and Primary Issues in Analytics - Dumnonia Corporation
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Executive summary
Big data has become a subject of concern in business organizations today. It could also be
noted that this technology is associated with security issues. Dumnonia is one of the
organizations known Australia wide for insurance services. The organization serve massive
population including the Australia citizens. It has also extended its operation oversea.
Considering the population served by Dumnonia, the organization is holding a considerable
amount of data. And these data have to be kept as big data hosted in their information system.
Dumnonia’s CEO is worried about how they can secure their users’ data. Being that the
organization mainly offer medical insurance services, it holds sensitive data that need maximum
protection.
There have been traditional ways of protecting data in such circumstances. However, the
methods have become outdated and also considering the fact that there have been a considerable
increase in data, there is need for security management strategy that is up to date. K-anonymity is
a promising security management approach and would be the recommendable approach for the
organization. This approach involves anonymizing data making it difficult for cyber criminals to
access. It works best with big data hence the move to implement the approach as Dumnonia
corporate wish to do would be a wise decision being that the organization also wish to expand
their system and hence the amount of data they will be holding massive data. In this document,
we discuss some aspects involving data analytics. This paper examine the implantation of the k-
anonymity as a model to ensure security for the organization’s big data system. We have
explored the organization drivers in relation to the implantation of the k-anonymity approach, we
have also done technology assessment solutions and k-anonymity implementation guide. A
comparison for two publicly available implementations is also done. Based on these analysis, it

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must be noticed that the k-anonymity approach promise a considerable significance as far as a
system security is concerned, thus implementing it would be a good move for the organization.
The organization drivers
The key drivers for the organization is the cloud based technology which they currently
use as the case study states that the organization undertakes initial pilots including the use of
AWS and cloud services, thus a lot depend the pilot studies outcome. After the studies, the
organization may shift to other platforms based on the company’s requirements such as big data
cloud platform.
As far as security issues are concerned, the current key driver for the organization is
focusing on the perimeter security. The organization still uses security systems used in the past
like IDS, firewall, they also employ security management technologies including the use of
malware protection, encryption, and password. The organization is however not very sure on the
security facet for securing the big data system.
About the organization’s database and data handling, the company depends on the
traditional data storage i.e. an organization data warehouse as a driver. They however noticed the
potential significance of a cloud-based approach but they are afraid of the operational draw backs
of cloud based big data system and privacy as well as security issues associated with cloud based
systems.
In regards to customers, the organization currently have a policy that should be fit its
customers. This is essential as it provides the organization with an opportunity for gaining a
competitive advantage of the customers’ data that they possess.
The organization drivers in relation to the deployment of k-anonymity

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Dumnonia is an insurance agency serving wide ranging population. According to the case
study, the corporate’s customers population is inclining. Additionally, due to the reason that the
organization is big and operates in many countries, it holds massive data. Currently, the
organization’s data volume is swelling at a considerable rate. And the factors concerning big data
requires the utmost attention. As such, the changes that are experienced in the company needs to
be explored in a proper manner. We can consider that the organization need to know various
factors so as to understand the significance, relationships, importance as well as improvements
within the company which could result from the use of big data. Thus the significance of the
study could be determined and will be better for Dumnonia corporate in future hence this drives
the corporate towards the adoption of the k-anonymity technology (Khoshgozaran, Shahabi and
Shirani-Mehr, 2011, pp.435-465).
The k-anonymity refers to a method that can be used to anonymize data in a company’s
big data system so that the organization’s private information are not identified in their system.
According to the scenario script, the corporate uses a traditional-based system. They have an
organizational warehouse. They also have controls that should be relevant to its customers in
order to keep the customers happy. However, achieving this will be just but a dream if the
organization customers’ details are under risk. Besides, they also need to gain a competitive
advantage due to the big data they are holding. Providing security to their customer’s data should
therefore be a priority. For instance, on the off chance that their consumers are in a certain poste
code-when consumers experience some problems concerning data breach-the organization
should launch new security management approaches such as a new big data security approach
that would offer maximum protection to their consumers’ data. This way, Dumnonia’s customers

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will be happy since security concerns would be no more. Hence, this is another need that would
drive the organization towards the adoption of the k-anonymity.
The technology solution assessment
Staying forecast as far as market trends are concerned is one of the critical aspects of big
data tools like k-anonymity. These tools can significantly boost an organization in the current
competitive business environment as they reduce security concerns. The Dumnonia corporate
significantly invest in its current information technology system. The company has applications
which are mainly used to improve customer services. Given the continuous increase in the
corporate’s data, it goes without saying that the organization has made a crucial step towards
their mission. This is in the sense that cloud based systems has demonstrated to be the perfect
approach which many organizations look forward to adopt in the world of ever growing data and
many business have succeeded with it. However, the use of big data is associated with security
risks.
Cloud based big data system is accompanied by security and privacy risks which may
cause a significant impact to a business. As a result, using k-anonymity could lead to a
considerable contribution regarding system security (Roy et al., 2010, pp. 297-312). Moreover,
the organization has a dedicated support from a third party from India as shown in the
assessment task. Dumnonia, however needs to understand that security and privacy is an
essential aspect of big data systems and should be considered before their big data is made
available to the third party. While an organization’s big data is anonymized, the business is set to
achieve two objectives. The objectives include the closure of unique identity and the closure of
sensitive identity (Memon, 2015, pp.1585-1600). As such, coupling the big data technology and

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cloud computing with k-anonymity could be the way for Dumnonia to achieve security and
privacy protection for its clients.
The organization is however not up to date regarding the implementation of the k-
anonymity. According to the analysis involving the organizations who finds it difficult to
implement the big data system, it is shown that the organization shares its data with other
organizations for insurance reasons. As the organization look forward to implementing the k-
anonymity approach, the approach promise the maximum security to its system (Stegelmann and
Kesdogan, 2012, pp. 419-426; Shokri et al. 2010, pp. 115-118). Moreover, it would be the most
appropriate for the organization if they expand their operations as they wish. The approach does
not allow for randomization which might be used by cyber criminals to gain access into the
system. The analysis in the approach includes encryption with slowing down system operations
to provide room for calculating and storing a large volume of information. The k-anonymity
system approach involves networking, storage as well as an effective data collection. With
autonomous services, it becomes easy to deal with the distributed and decentralized control
systems to identify relationships that evolve among datasets.
As in the second interview k-anonymity is reported to ensure that security risks involving
data breach are reduced. In the big data systems, it is difficult to recognize users’ data which is
the key driver for the big data strategy by the organization. According to the interview,
Guinevere is concerned about the privacy and security issues related to the adoption of the big
data strategy. He opt that deployment of the k-anonymity, there will be no issues regarding
information security thus securing the customers data from unauthorized access. Some
algorithms regarding k-anonymity such as p-value are also discussed in the interview. P-sensitive

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is the simple version of the k-anonymity algorithm extensions (Shin, Vaidya, Atluri and Choi,
2010, pp. 224-226; Yin, Zhang, Xi, and Wang, 2017, pp. 3902).
Randomization can get set by knowing how the sensitive data loads which can eventually
be used through other records. However, with the approach, the security standards is involves
dealing with various issues regarding computational sets by use of distributed methods. The
inference strategy majorly works with data, as such, it is essential that privacy is preserved even
during data mining (Abouelmehdi, Beni-Hessane and Khaloufi, 2018, p.1). Through this,
calculations which depends on the aggregation of statistics can be done without compromising
any body’s privacy.
Detailed assessment of the technology Dumnonia have selected for privacy
Dumnonia is showing interest for k-anonymity but they are concerned about security
issues associated with cloud. The third interview shows a discussion involving policies and
supportive documents related to the approaches for privacy. Constantine, the Dumnonian CSO
primarily focus on ensuring a secure system and preventing all possible security breaches.
The fundamental stress for outfitting the security is with dealing with the enormous
information and moving it beginning with one spot then onto the following so the guideline
feature of this portion is to keep up or to add a couple of features to the methodology so it might
be overhauled further and can give full confirmations towards the security issues.
The second interview is on the arrangement measures with respect to empowering the
security and the quality estimations effectively. The p-sensitive k-anonymity method is for
perception and concentrating on the information qualities. They are generally focusing on re-
perceiving the customers and a short time later dealing with the potential break of assurance. The

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methodology is to get composed to complete encryption inside the enormous information
arrangement of the association. In any case, the stress that the association CSE have is to execute
any encryption inside the enormous information structures would block the exercises as the
encryption technique will require some interest in their huge information system, for instance
getting ready and calculations of massive data would slack the framework. This is credited to the
reason that the data ought to be ceaselessly get encoded and unscrambled.
The encryption methodology should happen when data leaves or before it enters the
framework for enormous information. There are moreover issues that realizing the approach
could truly moderate the need to get information encoded as an incredible piece of the
information will be secretive, yet this is a view that the senior organization of the association
don't have when they are executing the k-anonymity approach (Boroojeni, Amini and Iyengar,
2017, pp. 13; Mohammed, Fung and Debbabi, 2011, pp.567-588). Another issue which is raised
in this section includes data provenance and which is terms to the data warehouse hosted in the
organization machine and which makes the meta information and in case it isn't dealt with, by
then it will modify the instructive gathering which will be of no utilization as unauthorized
changes in metadata can lead you to an unseemly data, which will make it difficult to find
required information and besides Untraceable data sources can be a major impediment to finding
the establishments of security breaks.
Because security breach would considerably affect the organization, protection of the
organization’s data from cyber criminals is the most critical thing the organization should
consider. Moreover, Dumnonia still uses the traditional security methods including firewalls and
passwords. However, due to the rapidly increasing volume of data, the organization has to go for
more advanced system protection strategies (Gkoulalas-Divanis, Kalnis and Verykios, 2010,

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pp.3-10). Also, it would be relevant for the corporate to consider security management controls
for the system access; this would boost security further.
The security strategy is especially for the use case involving the approaches to do with
handling data which are social media generated through the applications for big data like a web
based system. The organization was having a concern is with regards to anonymizing the
targeting data. However, storing the targeting data attributes in a browser cookies on the client’s
web browser could be dangerous. The cookies can be used to identify users which could be a
potential breach of security and each browser automatically implements it. Guinevere at that
point examined her worries about zones that information spillage could happen and the worries
about the various stages, however, she let it be known that it was a stress that she was not
completely mindful of the issue or the genuine extent of the issue.
K-anonymity implantation guide
In this section, we review the guide for implementing the k-anonymity. This guide is
based on the technology solution assessment of the organizations explored in the previous parts
of this document and the interviews of the organization’s personnel.
So as to anticipate the security assaults that may prompt information rupture, the k-
anonymity must get updated every time, the microdata must be done by changing the datasets k-
anonymization technique. In view of the potential augmentation of the volume of data, a ground-
breaking strategy for anonymization of the data might challenge. In any case, this part will
propose an unrivaled estimation after a movement of trails and exact relationships like it was
discussed in the underlying pieces of this paper. This can be accomplished nearby its adequacy
and effectiveness.

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Studies have help researchers to find the relationship that exists between k value, the
picking of a semi identifier, the degree of anonymization similarly as the accentuation on the
hour of execution where k is seen as a subjective impetus since it has been taken as p or
something else in the past fragment. Additionally, a few calculations for anonymization must be
utilized. Notwithstanding, the stress is the framework's activity viewpoint with the framework
information over the whole corporate. Selection of the Big Data framework guarantees the
capacity to share information with other corporates. There is stress among the affiliation staff
with respect to how the k-namelessness will help in overseeing security and insurance to ensure
data protection and tackle the operational costing systems (Caballero-Gil et al. 2016, pp. 482-
494; Williams, 2010, pp. 39-49). It is fundamental to fathom what is suggested by holding and
allowing the basic sharing of the affiliation's instructive lists. The k-namelessness is referenced
in this circumstance, where the MapReduce system could in proper working with improvement
along these lines dealing with the circumstances including the non-disseminated data (Wang,
Ma, Shi, and Wang, 2012, pp. 181-188). This calculation ought to be combined with an activity
that need to get proposed and chipped away at so as to fix up the issues in regards to adaptability.
UDT is an efficient approach in data transfer for big data systems with massive datasets
in high speed networks. However, the approach may be accompanied by various issues when
used in a web or cloud-based big data system as well as when the encryption technology is used
(Abouelmehdi, Beni-Hssane, Khaloufi and Saadi, 2017, pp.73-80).
Moreover, it is basic to concentrate on the progressions that are not approved in the
metadata in which the untraceable wellsprings of information might be a soft spot for
distinguishing proof of the reasons for security with the assurance of the cases which are
connected fakes. The procedure of encryption should happen before the information enter or

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leaves a huge information framework (Sicari et al. 2015, pp.146-164). As talked about in the past
areas of this archive, there are additionally a few issues that embracing the k-anonymity can
diminish the need to encode information because of the way that a significant part of the data
will get anonymized.
Comparison of publicly available implementation
There are two algorithms proposed for this project. The algorithms include Datafly and
Mondrian algorithms. The datafly algorithm refers to the algorithm that allow for anonymity in a
medical data This algorithm is capable of generating attributes having the most discrete values
until the k-anonymity is filled. The Mondrian algorithm, on the other hand involves a
multidimensional algorithm that can be used to position domain space into different regions as
long as it at least have a k record.
The proposed publicly available deployment involves ARX and UTD anonymization
tools. These tools are essential as they help in determining the factors affecting the performance
of various algorithms which may further help in finding out the best algorithm for the
organization (Prasser, Kohlmayer, Lautenschlaeger and Kuhn, 2014, 2014, p. 984). From the
ARX too anonymization, the adult datasets can be accessed then the NCP percentage is
determined for each dataset for k=10 then studied. The experiment demonstrate that the Datafly
showed a higher sensitivity compared to Mondrian. Additionally, the algorithm performance for
adults were much better as far as efficiency is concerned. The UTD tool on the other hand used
informs and adults as datasets.
When all algorithms are executed through one framework, it makes it easier to compare
for a fair performance in a system. During the implantation, intermediate anonimization datasets

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in UTD anonymization tool got hosted in the system database. The application carry out the
implementation through selection of all attributes. Regarding datafly and Mondrian, Mondrian
performs better than datafly. As such, according to the group-size based metrics, it can be
concluded that Mondrian’s performance is quite better than the performance of the datafly.
Conclusion
In summary, this document has shown an analysis on security and privacy issues in
analytics for Dumnonia organization. We have explored the Dumnonia’s information system as
well as the relevance for implementing the k-anonymity for the big data system in the
organization. A comparison of publicly available implementations have also been done. During
the analysis conducted for the two publicly available implementations, various scenarios were
determined for example when the algorithms did well as well as when they performed poorly by
use of metrics of interest. The results demonstrates that there is no excellently performing
algorithm; an algorithm’s performance is determined by two factors involving the needs for the
desired privacy and the characteristics of the datasets.
In the last remarks, it is worth noting that k-anonymity big for data security solutions can
revolutionize the organization environment. Moreover, the technology it can help the
organization to improve its productivity by ensuring a maximum security to the big data system.
However, besides its significance regarding security assurance, the solution is also accompanied
by its limitations in terms of implementation. Be that as it may, the technology is improving on a
regular basis to overcome the challenges.

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References
Abouelmehdi, K., Beni-Hessane, A. and Khaloufi, H., 2018. Big healthcare data: preserving
security and privacy. Journal of Big Data, 5(1), (p.1).
Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H. and Saadi, M., 2017. Big data security and
privacy in healthcare: A Review. Procedia Computer Science, 113, pp.73-80.
Boroojeni, K. G., Amini, M. H., & Iyengar, S. S., 2017, Smart grids: security and privacy issues.
Springer International Publishing, (pp. 13).
Caballero-Gil, C., Molina-Gil, J., Hernández-Serrano, J., León, O., & Soriano-Ibañez, M., 2016,
Providing k-anonymity and revocation in ubiquitous VANETs. Ad Hoc Networks, 36, pp. 482-
494.
Gkoulalas-Divanis, A., Kalnis, P. and Verykios, V.S., 2010. Providing k-anonymity in location
based services. ACM SIGKDD explorations newsletter, 12(1), pp.3-10.
Khoshgozaran, A., Shahabi, C. and Shirani-Mehr, H., 2011. Location privacy: going beyond K-
anonymity, cloaking and anonymizers. Knowledge and Information Systems, 26(3), pp.435-465.
Memon, I., 2015. Authentication user’s privacy: An integrating location privacy protection
algorithm for secure moving objects in location based services. Wireless Personal
Communications, 82(3), pp.1585-1600.
Mohammed, N., Fung, B.C. and Debbabi, M., 2011. Anonymity meets game theory: secure data
integration with malicious participants. The VLDB Journal, 20(4), pp.567-588.

A report Secondary and Primary Issues in Analytics - Dumnonia Organization 14
Prasser, F., Kohlmayer, F., Lautenschlaeger, R. and Kuhn, K.A., 2014. Arx-a comprehensive
tool for anonymizing biomedical data. In AMIA Annual Symposium Proceedings (Vol. 2014, p.
984). American Medical Informatics Association.
Roy, I., Setty, S.T., Kilzer, A., Shmatikov, V. and Witchel, E., 2010, April. Airavat: Security and
privacy for MapReduce. In NSDI (Vol. 10, pp. 297-312).
Shin, H., Vaidya, J., Atluri, V., & Choi, S., 2010, May, Ensuring privacy and security for LBS
through trajectory partitioning. In 2010 Eleventh International Conference on Mobile Data
Management (pp. 224-226). IEEE.
Shokri, R., Troncoso, C., Diaz, C., Freudiger, J. and Hubaux, J.P., 2010, October. Unraveling an
old cloak: k-anonymity for location privacy. In Proceedings of the 9th annual ACM workshop on
Privacy in the electronic society (pp. 115-118). ACM.
Sicari, S., Rizzardi, A., Grieco, L.A. and Coen-Porisini, A., 2015. Security, privacy and trust in
Internet of Things: The road ahead. Computer networks, 76, pp.146-164.
Stegelmann, M. and Kesdogan, D., 2012, June. Gridpriv: A smart metering architecture offering
k-anonymity. In 2012 IEEE 11th International Conference on Trust, Security and Privacy in
Computing and Communications (pp. 419-426). IEEE.
Wang, D., Ma, C.G., Shi, L. and Wang, Y.H., 2012, September. On the security of an improved
password authentication scheme based on ECC. In International Conference on Information
Computing and Applications (pp. 181-188). Springer, Berlin, Heidelberg.

A report Secondary and Primary Issues in Analytics - Dumnonia Organization 15
Williams, J., 2010, May. Social networking applications in health care: threats to the privacy and
security of health information. In Proceedings of the 2010 ICSE workshop on software
engineering in health care (pp. 39-49). ACM.
Yin, C., Zhang, S., Xi, J. and Wang, J., 2017. An improved anonymity model for big data
security based on clustering algorithm. Concurrency and Computation: Practice and
Experience, 29(7), pp. 3902.