Security and Privacy Issues in Analytics - Dumnonia Corporation

Running head: SECURITY AND PRIVACY ISSUES IN ANALYTICS
Security and Privacy Issues in Analytics
(Dumnonia Corporation)
Name of the student:
Name of the university:
Author Note

1SECURITY AND PRIVACY ISSUES IN ANALYTICS
Executive summary
Dumnonia is a leading insurance organization at Australia. They have been selling various kinds of
insurances. This has included death insurances, medical insurances and travel insurances. Thus it
holding various medical data regarding customers. This data has been highly confidential. Dumnonia
has needed to share-person to person records such that they determine individuals who are subjected
to data are not been determined. The following study has released records adhering to k-anonymity.
This indicates that the record released has been comprising of k-1 additional records to release the
values that are indistinct over the areas appearing in the external data. The following study
determined whether different k-anonymity methods has dealt with issue of privacy acquired by
Dumnonia situated at New Zealand and Australia. This is the K-anonymity method for assuring data
preservations and methods to be considered by Dumnonia. Next, the study is helpful to determine the
associated and operations issues and costs to deploy k-anonymity. It is also helpful to understand
whether the costs has been “once-off” or ongoing in nature. Lastly, the report also investigates where
the application of the anonymity method must help in sharing data sets between various government
and organization entities with Dumonia.

2SECURITY AND PRIVACY ISSUES IN ANALYTICS
1. Introduction:
The risks of security and privacy issues in analytics originates from storing, managing and
analyzing various information collected from various available and possible sources. The report
makes a discussion on organizational drivers related to implementing k-anonymity of Dumnonia.
Then a technology analysis is made and how to deploy k-anonymity as a model to protect privacy is
analyzed.
2. Organizational Drivers for Dumnonia:
There has been various cyber-security threats faced by big data systems of Dumnonia.
Particularly, any ransomware attack has been leaving the big data deployment subjected towards
ransom demands. Moreover, there are concerns about unauthorized users who have been gaining
access to big data collected by the organization and selling valuable data. Here, the vulnerability of
the system has been giving birth to fake data generation and attackers has been deliberately
undermining quality of big data analysis. This is done through fabricating data and putting those data
to big data systems (Clifton, Merill and Merill 2017). Corruption of medical information of
Dumnonia has been creating reports with erroneous trending information. Thus Dumnonia gas been
making mistaken strategic decisions in the basis of erroneous data. Further, there has been lack of
effective cyber security perimeter for the system of big data and assuring that every exit and entry
points for the big data systems has been secured. Any failure of perimeter based security has been
compromised of big data systems. It is also admitted that complicacy of cyber security and various
other people concerned has not been aware of those issues (Wang et al., 2016). Moreover, there are
concerns about being directed in implementing encryption under the big system of Dumnonia.

3SECURITY AND PRIVACY ISSUES IN ANALYTICS
Encryption under big systems has been including calculations and processing of huge quantity of
data. This has been slowing down system as the data required to be decrypted and encrypted.
3. Organizational drivers for Dumnonia relating to the implementation of k-
anonymity:
Data holders at, Dumnonia, has been holding collection of various field structures and person-
specific data. The data holders has needed to share versions of data with researches, Here, there has
been a rising query of how the holders has released a version of private data having scientific
guarantees where people are subjected to data that can never be re-determined as the data stays
practically useful. There has been a formal protection model known as k-anonymity and set of
accompanying policies regarding deployments (Niu et al. 2014). This release has provided protection
of k-anonymity as the data for every individual has remained in release and can never be
distinguished from minimum k-1 individuals. Here the information has also been appearing in that
release. There has been a rise in demand of re-identification of those attacks that are seen of releases
adhering to k-anonymity. This is unless those companying policies gets respected. Here the protection
model of k-anonymity is vital. This is because it has been forming on basis where real0world systems
are referred to a k-similar, μ-Argus and Datafly (Soria-Comas et al. 2014). Here, the concern of k-
anonymity has been with re-identification of single people in anomymized data-set. Here, there are
two re-identification cases for one individual.
Re-identification
of particular
people who are
Here, intruders such as prosecutors has been knowing that specific
individual like defendants has been currently staying in anonymized
databases and intending for find records that has been belonging to