SIT719 Assignment 1: Security and Privacy Issues in Data Analytics

This report comprehensively examines the security and privacy issues inherent in data analytics. It begins by highlighting the exponential growth of big data analytics and the corresponding rise in privacy and ethical concerns. The report delves into privacy issues, including data protection, user control, and the potential for re-identification of individuals from anonymized datasets, referencing the Netflix data challenge as a case study. Ethical issues are explored, focusing on the role of machine learning, bias in results, and the lack of free choice for users. The report discusses the ethical implications of biased data use, surveillance, and the influence of algorithms on individual decisions. The report also examines the impact of machine learning algorithms, the potential for biased outcomes, and the erosion of user autonomy. The conclusion underscores the importance of addressing these issues to ensure responsible and ethical data analytics practices. The report emphasizes the need for organizations to be transparent and accountable in their data handling practices, protecting individual rights and fostering trust in the digital ecosystem.

Running head: SECURITY AND PRIVACY ISSUES IN ANALYTICS
Security and Privacy Issues in Analytics
Name of the student
Name of the University
Authors note

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Executive Summary
With the boom of the use of data analytics in different fields in order to make
predictions or experts decision depending on the collected past data. People or the users
oof different services share information with the different organizations whether government
or non-government organization in different ways. Often the data is collected through
different social media and databases of the different services are analysed using different
statistical tools that helps in identification of the different patterns for different user groups.
These data or the information are helpful in connecting, monitoring people and this
trend is growing tremendously. This growing trend is creating security and privacy concerns
for the users who are providing the data. With the ability to analyse and find insights from the
collected datasets available through the highly disparate contexts. With the new and
unanticipated knowledge, the organizations get the power and peril of analytics.
With the emergence of the big data analytics the space for ethical and privacy issues
are also growing. For the analysis of the implications of the privacy and ethical issues the
Netflix data challenge and different face recognition case studies are considered in the
different segments of the report. The following paper contributes to the different ethical and
privacy issues that may act as obstacles to the realization of expected opportunities and
growth of analytics.

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Table of Contents
Introduction................................................................................................................................3
Privacy issues associated with the use of Data analytics...........................................................3
Ethical issues and Data analytics...............................................................................................6
Role of machine learning...........................................................................................6
Biasness in results......................................................................................................7
Lack of free choice.....................................................................................................7
Conclusion..................................................................................................................................8
References..................................................................................................................................9

3SECURITY AND PRIVACY ISSUES IN ANALYTICS
Introduction
With the exponential growth of use of big data analytics the privacy and ethical
issues related to the use of the datasets and insights from the data are also raising concerns.
One of the best online streaming services released their viewer dataset as the part of the data
science challenge in order to improve its recommendation engine (Papernot & Goodfellow,
2018). This challenges raised privacy and ethical concerns as according to some researchers
it contains micro-data which is helpful in determining or specifying a user/individual.
This kind of data about specific individuals, are gradually becoming available in the
public domain due to the “open government” legislation or data mining research field. This
kind of dataset often includes individual preferences and transactions (such as movie genre
preferences in case of Netflix) that some people may consider as private/ sensitive. The
following report contributes to the analysis and discussion about the different privacy and
ethical issues related to the data analytics on the datasets that are publically available.
Privacy issues associated with the use of Data analytics
Privacy of data is concerned with the protection of it as well as access to any
individual’s information. This definition also includes the independence from the surveillance
or unwanted attention by any government or business organization (Sankar & Parker, 2017).
Privacy also implies that individuals or the users are the governor of their personal data and
must be informed about the use, update and deletion of their data. Even after that only few
people have actual control over their data which includes what data is stored about them, its
accuracy and use of their data for different intents.

4SECURITY AND PRIVACY ISSUES IN ANALYTICS
Privacy of data in analytics is dependent on the organization that is collecting the
data. It is intended that that the data must be used solely for the purpose it is collected stated
to user and nothing else from that particular.
It is also desired that the data should not be shared between different business for
generating profits or penetrate in a market. On the other hand, there are too many tools,
techniques are used in the big data analytics that are helpful in the extraction of the user
private data that violates the privacy rights of the user’s (Papernot & Goodfellow, 2018). In
addition to that, the security policies need to be enforced with privacy policy in order to
protect all the entities involved in the process.
From the different researches in the field of the dataset it is evident that, with the
datasets that does not include the personal identifiers such as name, unique customer or
identification number it is possible to find out the preferences if the attacker had knowledge
about a specific individual and their likes and dislikes for different genre of movies.
According to the authors Vayena et al.(2015), in case any analyst previously knows
someone and their likes and dislikes for the movies then using the available Netflix data set
the researcher/attacker gain knowledge about their viewing history until the year 2005.
The researchers also answered some of the questions that encounters the possibility
of identification of the user only through available dataset provided by Netflix. In the
response of this this is argued that there are other data sources that may help in matching the
user preferences on multiple platforms such as IMdb and MovieLens. Even though the users
on this platform may not use their name and actual identity on these sites but with the help of
cross co-relation mechanisms it is possible to find out identity of the user (Sankar & Parker,
2017). The complete process depicts that any attacker can find out links with any anonymous
Netflix data record to any external publically available data of an individual such as their

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IMDb ratings that are associated with identity of a specific person. While Netflix's exposed
data sources excludes user /subscriber names, and used anonymous identifier for their
subscribers/users but it is found by the researcher’s that collection of movie ratings
collective with available public database of ratings can easily detect or help in identifying
people. Hajian, Bonchi and Castillo (2016), described the privacy issues that can be raised by
using public reviews published by a different common user (who uses both Netflix and
IMDb) on the Internet Movie Database (IMDb).
Exposing movie ratings posted by the user’s /reviewer wo though those data will be
private could expose significant details about the person when analysed with other platform.
For example, the researchers found that some people had strong and private opinions for the
liberal and gay-themed films that are loved by some other groups.
In different other sectors, there are example of use of the analytics and breaches that
harms the privacy the owner/ individuals about whom the data was collected. The impact
also includes different national privacy regulations and policies. Even though the use of the
analytics may be helpful in providing the social benefits with the culturally acceptable
practices but with the exposure of any individual’s orientation and medical history may lead
to the discrimination towards those specific individual (Vayena et al., 2015). For example,
different researches revealed that countries/locations with tighter privacy regulations
experience fewer privacy issues related to the analytics. Again with the greater control can
have a downside and lead to lower advertising effectiveness for the organizations as well as
consumer marketing outcomes. It is also found that that in circumstances of higher perceived
privacy control, consumers or the users are most likely to click on personalized ads generated
using the analytics (Papernot & Goodfellow, 2018).

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With this technique it is possible to expose the identity of an individual through liking
the different sources of data that in turn breaches the privacy of the individuals.
Ethical issues and Data analytics
Even though individuals contribute their information to the different organizations
related to the different industries but ultimately the individuals do not have ownership over
their data. The right of retaining one’s data implies that individuals should be benefited from
the contributed data. In this way the individuals using digital services from the organization
such as google location based services or the services from the Facebook are often tracked by
service providers against the usage of their services. In this way the users lose their privacy
without their consent that leads to the ethical implication of use of the insights by the
organizations.
Role of machine learning
Data analytics depends on different machine learning algorithms in order to support
human decisions based on the huge amount of data. The algorithmic decision-making takes
input large set of data that are collected and combined from multiple data sources in order to
predict some decision for the individual’s behaviour depending on their collected data about
their past behaviour (Papernot & Goodfellow, 2018). This decision making process focuses
on identification of relationships in the different attributes of the collected data. In case of the
bias in the facial recognition system it is observed that, the error rates are higher while
detecting the darker skinned individual.
Again most of the machine learning systems/algorithms requires continuous input in
order to train, improve and enhance the accuracy of the results of their algorithms. This
process can be analogized to the process of quality improvement researches. In this cases
informed consent are considered as not necessary (Sankar & Parker, 2017). It is also desired

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to involve the stakeholders such as the citizens or the patients (in case of medical processes).
Such as for research in the genetic diagnosis the algorithms require inputs the or images of
individuals who have specific genetic disorders to improve the accuracy of the results. In
order to maintain trust as well as transparency with all the stake holders (in this scenario
patients), organizations should be involving applicable community stakeholders in
implementation process of FRT with their consent. Furthermore, Ethical issues arise at every
phase of the processing and value creation through the analytics (Vayena et al., 2015). At the
end of the process final owner of processed data and insights available from it may use the
data insights for numerous purposes that are very different from the initial one (for which the
data collected at the very first place).
Biasness in results
Furthermore, the ethical impacts of the biased use of the data analytics can be
multifaceted. Such an example can be stated as depending on the analysis any service
provided by an organization can use filtered approach in order to hide pieces of information
from different segment of users. In this way, the organization imposes a bias of which the
users are unaware.
Again with the use of facial recognition systems it is possible for the organization to
monitor and carry out surveillance on the individual trough the analysis of big data sets.
Through the analysis of the behaviours can offer personalized services/ products (Sankar &
Parker, 2017). At this stage this stated that these monitored individuals would not have
exposure to all the available options/choices available in market places.
Lack of free choice
Ultimately, this leads to the fact that individuals are no longer subject to the basic
rights related free choice for any product services. In this scenario they get under the control

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of the surveillance and the algorithms that are developed in order to influence the decisions
of the individuals (Hajian, Bonchi & Castillo 2016). In this way total process enforces the
individuals under the surveillance capitalism with the impact of new drivers that are resulted
from the insights available from big data insights rather than the traditional market-based
capitalism.
In case of the big data analytics due to monetization of the available data of the users by the
organizations without the consent of the owner or the users (Vayena et al., 2015). With the
available insights from the data (which are not collected from the users for the analysis) the
organizations are conducting analytics with ease and huge scale for influencing the
individuals behaviour.
Conclusion
The insights from the analytics ay consist of considerable biases or errors as all the
individuals not conforming to the same characteristics of a certain group. Additionally, when
implications are made from the different data sets for specific individual may lead to poor
quality. With the used algorithms, result may lead to the complex ethical issues. Both the
government and private organizations are presently capable of combining diverse and
multiple digital datasets available publically. Using this data sources, they can analyse them
in order to find out the statistics and extract hidden insights/information and surprising
correlation among them. This insight can lead to exploitation of the privacy of the users
about whom the data set contains information. The decision making process assumes that
detected relationships are meaningful in the light of the cause and effect scenario in a social
phenomenon. One such example is to predict the matches of the human faces based on
correlations in a collected and processed data set, which overlooks small precisions and
inherent error and biases present in the collected data.

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References
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and
predictive analytics in retailing. Journal of Retailing, 93(1), 79-95.
Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare
informatics research, 22(3), 156-163.
Hajian, S., Bonchi, F., & Castillo, C. (2016, August). Algorithmic bias: From discrimination
discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD
international conference on knowledge discovery and data mining (pp. 2125-2126).
ACM.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the
Academy of Marketing Science, 45(2), 135-155.
Martin, K. E. (2015). Ethical issues in the big data industry. MIS Quarterly Executive, 14, 2.
Papernot, N., & Goodfellow, I. (2018). Privacy and machine learning: two unexpected
allies?.
Sankar, P. L., & Parker, L. S. (2017). The Precision Medicine Initiative’s All of Us Research
Program: an agenda for research on its ethical, legal, and social issues. Genetics in
Medicine, 19(7), 743.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big
Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Taylor, L. (2016). No place to hide? The ethics and analytics of tracking mobility using
mobile phone data. Environment and Planning D: Society and Space, 34(2), 319-336.

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Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015). Ethical challenges of big
data in public health.
Vitak, J., Shilton, K., & Ashktorab, Z. (2016, February). Beyond the Belmont principles:
Ethical challenges, practices, and beliefs in the online data research community.
In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative
Work & Social Computing (pp. 941-953). ACM.