4DATA001C.2 Statistical Modelling and Analysis
BDA B1
4DATA001C.2 Statistical Modelling and
Analysis
COURSEWORK
GROUP B1
`
Lecturer: Ms. Alqa Husni
UoW ID: w2120494
IIT ID: 20240376
Name: Marasingha Dheemantha
UoW ID: w2120513
IIT ID: 20240888
Name: Wickramaarachchi Athukorala
UoW ID: w2120527
IIT ID: 20241159
Name: Dunusinghe Dharmappriya
UoW ID: w2120565
IIT ID: 20233061
Name: Yalinda Jayawardena
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Table of Contents
Part 1 – Data Ethics....................................................................................................... 3
Essay 1 – Marasinghe Dheemantha ............................................................................ 3
Essay 2 – Wickramaarachchi Athukorala ..................................................................... 6
Essay 3 – Dunusinghe Dharmappriya .......................................................................... 9
Essay 4 – Yalinda Jayawardena ................................................................................. 14
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Part 1 – Data Ethics
Essay 1 – Marasinghe Dheemantha
In the current digital economy, data is a very valuable resource. Data influences social and
economic advancement and drives innovation and decision making. But the moral issues
surrounding the gathering, handling and sharing of data have grown increasingly difficult.
The study of data ethics looks at these issues with an emphasis on the values of justice,
openness, responsibility and respect for people's rights. The need to maintain ethical
norms grows along with the extent of data use.
Data collecting is one of the most critical data ethical challenges. Without complete
transparency or informed agreement, organizations gather enormous volumes of personal
data via websites, social media, mobile applications and Internet of Things devices. For
example, the Cambridge Analytica controversy raised concerns about the use of Facebook
user data collected without their agreement to control political outcomes (Cadwalladr &
Graham-Harrison, 2018). This incident highlights the need for increased responsibility and
informed consent procedures, as well as the ethical dangers of secret data gathering.
The problem of individual reporting, in which algorithms analyze data to create
comprehensive user profiles, goes hand in hand with this. Personalized recommendations
on Netflix or Spotify are an example of how reporting can improve user experience, but it
can also result in bias and discrimination. For instance, studies have found that when
algorithmic reporting is trained using biased data, it might spread innate social
discrimination in loan acceptance or job engaging (O'Neil, 2016). Both the opportunity for
appeal of automated outcomes and transparency in algorithmic design should be
guaranteed by ethical reporting.
Another urgent ethical issue is the effects of data leaks. Large-scale breaks, like the one
that hit Equifax in 2017 and impacted over 140 million people, put private data at risk of
identity theft, economic loss, and psychological suffering. Data breaches destroy public
faith in institutions in addition to causing harm to individuals. Therefore, strict
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cybersecurity measures and the data minimization principle which states that only
necessary data should be gathered and stored are necessary for ethical data management.
Furthermore, there are important ethical issues surrounding the interpretation and
publication of data analytics findings. Cherry picking or incorrect data interpretation can
mislead the public and result in harmful outcomes. For instance, the misuse of statistical
models during the COVID-19 pandemic occasionally led to policy errors or public
confusion (Saltelli et al., 2020). Sensationalism must be avoided, technique transparency
must be maintained, and doubts must be appropriately communicated.
Ethical data practices require legal and regulatory frameworks as fundamental
foundations. The Human Rights Act of 1998 and the General Data Protection Regulation
(GDPR) are the two most important laws in the UK and the EU. Principles including fairness,
justice, transparency, and the right to be forgotten are enshrined in the GDPR, which went
into effect in 2018 (European Parliament and Council, 2016). The GDPR establishes strict
requirements for gaining consent and get ahead data protection by design. On the other
hand the Human Rights Act supports the right to privacy (Article 8), allowing people to have
control over their personal information.
However, following the law does not ensure moral conduct on its own. Beyond only
following to the law, ethical data governance must encourage responsibility within the
company culture. This covers open data rules when applicable, ethical review boards for
data projects, and encouraging public participation in data governance.
To sum up, data ethics is essential to ensuring that technological advancement supports
societal norms and human self-respect. Ethical questioning is crucial for everything from
data collection and reporting concerns to break consequences and interpreting analytical
findings. Although the legal frameworks of the UK and the EU offer a solid basis, states,
organizations and individuals must all actively participate in sustainable ethical conduct.
Setting ethics as a top priority will be essential to creating a just transparent and
trustworthy digital environment since data will continue to be the driving force of the future.
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References
i.
Cadwalladr, C. and Graham-Harrison, E., 2018. Revealed: 50 million Facebook
profiles harvested for Cambridge Analytica in major data breach. [online] The
Guardian.
Available
at:
https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebookinfluence-us-election [Accessed 22 Apr. 2025].
ii.
European Parliament and Council, 2016. Regulation (EU) 2016/679 (General Data
Protection Regulation). Official Journal of the European Union. Available at:
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679
[Accessed 22 Apr. 2025].
iii.
Floridi, L. and Taddeo, M., 2016. What is data ethics?. Philosophical Transactions of
the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083),
p.20160360. Available at: https://doi.org/10.1098/rsta.2016.0360 [Accessed 22 Apr.
2025].
iv.
ICO (Information Commissioner's Office), 2023. AI and Data Protection. [online]
Available at: https://ico.org.uk/for-organisations/ai/ [Accessed 22 Apr. 2025].
v.
O'Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality
and Threatens Democracy. New York: Crown Publishing Group.
vi.
Saltelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., et al., 2020.
Five ways to ensure that models serve society: a manifesto. Nature, 582(7813),
pp.482–484. Available at: https://doi.org/10.1038/d41586-020-01812-9 [Accessed
22 Apr. 2025].
vii.
UK Government, 1998. Human Rights Act 1998. [online] legislation.gov.uk. Available
at: https://www.legislation.gov.uk/ukpga/1998/42/contents [Accessed 22 Apr.
2025].
viii.
Zuboff, S., 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at
the New Frontier of Power. New York: PublicAffairs.
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Essay 2 – Wickramaarachchi Athukorala
There has been a very robust debate, in the recent past, about the role social media plays
in mental health. Those who support social media contend that it allows people to keep in
touch, garner help and speak their minds. They underscore the impact social media has on
bringing awareness to serious issues and building excitement for movements. But critics
point out the negatives of social media like feelings of loneliness, anxiety, and depression.
They question social media platforms being addictive and cyberbullying directly leading to
mental distress. An understanding of the balance between gains and sacrifices from using
social media is critical to maintaining a healthy relationship with digital age technology.
Data collection must be public, legal, and respect personal rights. Legal questions arise
when data is obtained without proper consent, when personal data is gathered for vague
or unspecified purposes, or when such information is used for purposes other than those
for which it was provided. It can lead to biased or harmful outputs, especially if misuse
population data, as has been known through unethical data collection. One famous
example is the Facebook-Cambridge Analytica scandal, in which millions of user data were
harvested without informed consent and used
Profiling is the so-called analysis of personal information in order to divide people into
categories, with common applications including marketing, risk assessment, and
predictive policing. While profiling is sometimes tightly integrated into company practices
and policies, improving company strategies and services, or tailoring individual
approaches, it appears in some cases as an unfair treatment and in other cases lead to the
reinforcement of prejudices, if the profiling is based on bad or unfair data. As an example,
algorithms for predictive policing may unduly target minority demographics due to
datasets of arrests and criminal activities containing systemic bias against these
demographics in historical law enforcement practices (O'Neil, 2016). The risk of bias
causes ethical concerns as well as ethical issues stemming from.jobs taking jobs, ethical
principles leading to death, a judgmental attitude leading to violence, and so on.
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Gain of access or disclosure or stealing of private or personal information without the
consent of the user or their information is called as data breach. Such breaches harm the
identity theft of financial fraud, reputation loss and emotional distress to those affected.
User data must be protected, not just out of ethics, but also as a legal obligation. A case in
point is the 2017 Equifax breach that compromised the personal and financial data of 147
million individuals due to substandard cybersecurity controls (Federal Trade Commission,
2019). And this event, sirens blaring, shows the organization peak ethical issue
Ethical surveillance implicates data collection and how results are shared and
interpreted. Without meaningful accountability, data can easily be distorted or selectively
reported, resulting in misleading information for both policymakers and the public. For
instance, the use and misuse of infection rate figures throughout the COVID-19 pandemic
often played an important role in misinformation and public uncertainty (Ioannidis, 2020).
To forestall harm, ethical dissemination demands lucidity, honesty, and transparency,
especially when the ultimate verdict will impact the public on health, policy, or social
justice.
Generally, the main applicable legislation in the EU on this topic is the General Data
Protection Regulation (GDPR). The GDPR embodies principles Ⅲ like data minimization,
accountability, consent, and individuals' rights to access and rectify their personal
information (ICO, 2018). In the United Kingdom these protections are reinforced by the
Human Rights Act 1998, which guarantees the right to privacy under Article 8. These
legislative frameworks ensure ethical norms are not voluntary, but rather requisite, holding
organizations accountable
In our data-driven culture, ethical data use sustains human dignity, rights, and autonomy.
In simple terms, data ethics promotes fairness, transparency and respect during all stages
of data usage, from collection to dissemination. Such ethics are emphasized in legal
frameworks, such as GDPR, but proper accountability is as much about the attitude of
those involved, from organizations to data scientists, legislators and the public. Lacking a
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firm moral basis, data can quickly turn from a source of benefits into a cause of serious
public and private damage.
References
i.
Cadwalladr, C. and Graham-Harrison, E., 2018. Revealed: 50 million Facebook
profiles harvested for Cambridge Analytica in major data breach. The Guardian.
[online] Available at: https://www.theguardian.com/news/2018/mar/17/cambridgeanalytica-facebook-influence-us-election [Accessed 15 April 2025].
ii.
Federal Trade Commission, 2019. Equifax Data Breach Settlement. [online]
Available at: https://www.ftc.gov/enforcement/cases-proceedings/refunds/equifaxdata-breach-settlement [Accessed 16 April 2025].
iii.
Information Commissioner’s Office (ICO), 2018. Guide to the General Data
Protection Regulation (GDPR). [online] Available at: https://ico.org.uk/ [Accessed 19
April 2025].
iv.
Ioannidis, J.P.A., 2020. Coronavirus disease 2019: the harms of exaggerated
information and non-evidence-based measures. European Journal of Clinical
Investigation,
50(4),
p.e13222.
[online]
Available
at:
https://onlinelibrary.wiley.com/doi/full/10.1111/eci.13222 [Accessed 19 April 2025].
v.
O’Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality
and Threatens Democracy. New York: Crown Publishing. [Accessed 18 April 2025].
vi.
The
UK
Human
Rights
Act
(2023)
Pinsent
Masons.
Available
at:
https://www.pinsentmasons.com/out-law/guides/uk-human-rights-act (Accessed:
20 April 2025).
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Essay 3 – Dunusinghe Dharmappriya
Data ethics refers to the moral principles that guide the collection, use, analysis, and
dissemination of data. As defined in the lecture material, it is “concerned with the
principles of right and wrong behavior” in relation to data (Worrall, 2020). Data ethics is
moral principles that govern a person's behavior, or the conducting of an activity
concerned with the principles of right and wrong behavior. Data ethics is essential to
ensuring that analytical procedures are fair, accountable, and considerate of individual
rights in a time when decision-making based on data has become commonplace.
To collect data, we need to be able to define the goal of our research and what we are trying
to learn from it. For example, if we need to find out student satisfaction with online
learning, we can generate a survey with a list of questions, like their age, what they are
studying, etc.
The rise of data ethics began with the increase in data collected from surveys over the
years. People demanded to know what was happening to the data collected and whether
they were secure or not. People questioned the transparency, accountability, privacy, and
security of their data. With the collection of personal information in vast amounts, people's
privacy concerns have begun to grow. As the solution to these issues, we could increase
the transparency of data to avoid the misuse of data. With transparency, people will be
able to understand what was happening to their data and where the data ended up.
Because of this, people will be able to trust surveys and not be feared to attend surveys.
And it solves many data collection problems modern collectors face. We can solve more
issues relating to data collection if we follow data ethics in every possible way
Individual profiling is “any form of automated processing of personal data consisting of the
use of personal data to evaluate certain aspects relating to a natural person, in particular,
to analyze or predict aspects concerning that natural person’s performance at work,
economic situation, health, personal preferences, interests, reliability, behavior location or
movements” Profiling consists of using an individual's data to analyze and predict their
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behavior, such as determining peplos performance at work, financial situation, health,
preferences, lifestyle, etc.
With individual profiling, people may face problems such as having their personas created
without their knowledge, being scammed through stolen identities, being influenced or
manipulated into certain actions, and losing the freedom to make their own choices.
Ethical data use demands that scammers and individuals who misuse data must not
exploit vulnerabilities or manipulate others without their awareness.
Data profiling requires huge pools of data to ensure the accuracy of results. When large
companies gain a majority share in a market, they gain a large consumer base and
therefore also gain a monopoly on consumer data. This allows them to conduct data
profiling more successfully, boosting their performance which further increases their
power as a dominant market player. If the European Competition Authorities perceive this
as anti-competitive behavior, such companies could face potential sanctions.
A data breach is any security incident in which unauthorized parties access sensitive or
confidential information, including personal data (Social Security numbers, bank account
numbers, healthcare data) and corporate data (customer records, intellectual property,
financial information).
When a data breach happens, areas like privacy, trust, and Societal Consequences are
heavily impacted. When we look at the privacy area, data breaches expose all of our
personal and sensitive data without consent. This can result in identity theft, financial
fraud, and other forms of personal harm. For the trust area, once their data is breached to
dark websites or in the hands of unethical hackers, they lose their customers’ trust. Even
small incidents can significantly damage relationships, and customers less likely to engage
in future online activities. Because of this long-term loss of trust, This long-term loss of
trust may hinder digital participation and slow down innovation in major companies.
For example, Microsoft had a huge data breach in 2021 and it impacted 30000 companies
in the United States and 60000 companies worldwide because they followed ethics they
were able to reduce the harm caused by hackers, and for a local example, we can tell
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which happened recently in Cargils Bank and it breached a lot of sensitive data from both
customers and employees.
Data dissemination is the process of making data accessible to users, while interpretation
of results involves understanding and drawing conclusions from the data. Dissemination
ensures the data is shared widely, while interpretation provides meaning and insights.
Ethical dissemination requires sharing all results positive, negative, or inconclusive to
avoid publication bias and misinforming stakeholders. Transparency about methods,
limitations, and uncertainties is essential to prevent misleading interpretations Results
should be disseminated promptly to maximize their benefit, especially in urgent contexts
like public health emergencies. Making key information publicly available, including
protocols and analysis plans, supports transparency and reproducibility.
For example After surveying student satisfaction at a university, the results are published in
a report, shared through email, and presented at a meeting for the academic staff and
student union. That’s data dissemination. From the student satisfaction survey, 80% of
students say they’re happy with the library services, but only 40% are satisfied with online
learning platforms. That helps us to get the interpretation result.
The Human Rights Act 1998(HRA) and the UK/EU legal framework play an important role in
shaping data ethics by establishing legal boundaries, safeguarding individual rights, and
promoting responsible data practices. In the Human Rights Act 1998, article 8 shows that
HRA guarantees the right to respect for private and family life, home, and correspondence.
This directly ties to data ethics by requiring researchers and organizations to minimize
intrusions into personal privacy when collecting or sharing data.
When we look at the legal framework, in 2018, GDPR and DPA came from the backbone of
data ethics and enhanced the protection of sensitive data and automated decisionmaking.
Every phase of the data lifecycle must consider ethical issues because data continues to
influence modern society. The examples given demonstrate how unfairness, bias, and a
loss of autonomy can result from profiling, biased algorithms, inadequate data procedures,
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and a lack of transparency. Respecting the values of justice, human rights, and privacy is
not just required by law; it is also a social need. Organizations can utilize data ethically and
responsibly for the sake of all by adopting strategies like anonymization and transparency
and coordinating their operations with the GDPR and the Human Rights Act.
References
i.
Bidault, P.-E. (2022) Profiling. https://www.dastra.eu/en/guide/definition-profilinggdpr/56328 (Accessed: April 18, 2025).
ii.
Blog, D.P.B. (2020a) 'The legal ethics of data profiling,' Durham Pro Bono Blog, 3
March.
https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling
(Accessed: April 18, 2025).
iii.
Blog, D.P.B. (2020b) 'The legal ethics of data profiling,' Durham Pro Bono Blog, 3
March.
https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling
(Accessed: April 18, 2025).
iv.
Chin, K. (2024) Biggest data breaches in US history (Updated 2025).
https://www.upguard.com/blog/biggest-data-breaches-us (Accessed: April 19, 2025).
v.
Data
Collection:
Process
&
Challenges
|
SafetyCulture
(2024).
https://safetyculture.com/topics/data-collection/#common-challenges (Accessed: April
18, 2025).
vi.
Directory,
S.
(2024)
Data
Breaches:
ethical
implications?
→
Question.
https://sustainability-directory.com/question/data-breaches-ethical-implications/
(Accessed: April 19, 2025).
vii.
Dissemination of research (no date). https://unisa.edu.au/research/integrity/responsibleresearch-practice/dissemination-of-research/ (Accessed: April 19, 2025).
viii.
Government Digital Service (2025) Data protection. https://www.gov.uk/dataprotection (Accessed: April 18, 2025).
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ix.
Jayan,
J.
(2024)
BDA B1
Importance
of
ethical
data
collection.
https://www.promptcloud.com/blog/importance-of-ethical-data-collection/
(Accessed:
April 18, 2025).
x.
Participation,
E.
(no
date)
Human
Rights
Act
1998.
https://www.legislation.gov.uk/ukpga/1998/42/contents (Accessed: April 18, 2025).
xi.
Privacy Analytics (2024) Privacy Analytics - Understanding Re-identification Risk
when
Linking
Multiple
Datasets.
https://privacy-
analytics.com/resources/articles/managing-re-identification-risk-when-linking-multipledatasets/ (Accessed: April 18, 2025).
xii.
Ravinetto, R. and Singh, J.A. (2022) 'Responsible dissemination of health and
Medical Research: Some guidance points,' BMJ Evidence-based Medicine, 28(3), pp.
144–147. https://doi.org/10.1136/bmjebm-2022-111967.
xiii.
UK
Data
Service
(2021)
Human
Rights
Act
-
UK
Data
Service.
https://ukdataservice.ac.uk/learning-hub/research-data-management/dataprotection/data-protection-legislation/human-rights-act/
2025).
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(Accessed:
April
19,
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Essay 4 – Yalinda Jayawardena
Data can be used for insightful analysis and decision-making. Yet, this powerful resource
challenges how organisations collect, store, and use data ethically. Therefore, these
organisations must uphold a certain level of ethics that relate to data. Data ethics are
moral obligations of gathering, protecting, and using personally identifiable information,
and they affect individuals. “Data ethics asks, ‘Is this the right thing to do?’ and ‘Can we do
better?” (Harvard Business School, 2021).
Issues related to data collection. “Ethics in data collection, also known as what data ethics
is, refer to the principles that businesses should follow when it comes to how they collect,
use, and protect data from any number of sources.” (University of the Cumberlands, 2024).
Data transparency is crucial for data ethics, the ability for the customer to understand what
kind of data is being collected, how it is collected, and how it is potentially being used.
Transparency in data collection is critical to building customer trust and preventing the
misuse of information after it is collected. Data privacy and consent often go together
because if a person provides their information to a business, they assume the data will be
kept secure. Therefore, data privacy means that the data will be protected from public
exposure. Further, lack of relevance is an issue related to data collection. Organisations
should ensure that data collection spans the relevant demographics. But, if they aren’t
going to contribute to the relevant study, they should be avoided.
Individual profiling, is defined under the general data protection regulation as, “any form of
automated processing of personal data consisting of the use of personal data to evaluate
certain personal aspects relating to a natural person, in particular to analyse or predict
aspects concerning that natural person’s performance at work, economic situation, health,
personal preferences, interests, reliability, behaviour, location or movements” (Ghant
University, 2024).
Legal profiling issues are as follows: e-commerce companies can create data profiles of
customers based on their search and purchase history for targeted advertising. Some
customers argue that it is an invasion of their privacy. Article 22(1) of the EU, GDPA restricts
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organizations from making solely automated decisions. Furthermore, under Article 17 of
the GDPR, all personal data must be removed immediately when they are no longer needed
for its original purpose.
In addition, Facebook – Cambridge Analytica (CA) data scandal highlights the dangers of
using data profiling in political advertising. In 2018, it was revealed that Cambridge
Analytica harvested millions of personal data from Facebook profiles without consent and
used it for political advertising purposes. Which triggered calls for higher regulation of tech
companies’ use of personal data.
A data breach is any security incident related to unauthorised parties' access to sensitive
or confidential information such as personal data (Social Security numbers, bank account
numbers, healthcare data). Often, the term “Data breach” is їnterchangable with
“cyberattacks”.
According to the IBM cost of data breach report, the average global cost of a data breach is
USD 4.88 million. At the same time, organisations of every type and size are vulnerable to
breaches, but the severity and cost could vary. For example, the average cost of breaches
in the USA is 9.36 million, about 4 times that of India (USD 2.35 million).
In 2007, the TJX Corporation, the parent company of TJ Maxx and Marshalls, recorded the
largest and the costliest data breach in US history. More than 94 million customer records
were compromised and the company suffered more than 256 million in financial losses. In
2013, Yahoo suffered a data breach where hackers exploited the weakness of the
company’s cookie system to access the names, birthdays, email addresses, and
passwords of all 3 billion Yahoo users.
Data dissemination is the targeted distribution of information and intervention materials to
a specific audience. For instance, if we take the effective Health Care Program to facilitate
the use of Health-Related Evidence, these strategies are used to spread evidence-based
knowledge interventions across geographic locations, practice settings, or social or other
networks of end-users like patients and health care providers.
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Data interpretation involves making sense of numerical, textual, and visual data. It consists
in analysing, evaluating, and drawing conclusions from data sets for decision-making. It is
important because it helps to identify trends, forecast future outcomes, and make
informed decisions. Key data interpretation techniques are descriptive statistics,
inferential statistics, and data visualisation.
The legal framework plays a crutial role when it comes to enforcing rights and
responsibilities when handling data. It makes organisations accountable for their conduct,
therefore encouraging them to be ethical when handling data. Article 8 of the Human
Rights Act, the Data Protection Act, and the GDPR are examples of the legal framework.
Several approaches can be made when following the legal framework for ethical data
usage. This includes the steps of recognising the issue, obtaining the facts, evaluating
possible actions, deciding on the course of action, and reflecting on the decision. In
addition, two more techniques can be followed when handling data. Data anonymisation
and the POTOMAC guidelines.
In conclusion, the use of data is highly beneficial for organisations. However, it is the
responsibility of the organisations and businesses to take ethical considerations when
handling data, safeguarding the individuals, and being authentic.
References
i.
Cheong (2020) 'The Legal Ethics of Data Profiling,' Durham Pro Bono Blog, 3 March.
https://www.durhamprobonoblog.co.uk/post/the-legal-ethics-of-data-profiling
(Accessed: April 14, 2025).
ii.
Communication and Dissemination Strategies To Facilitate the Use of HealthRelated Evidence (2012) Agency for Healthcare Research and Quality.
https://effectivehealthcare.ahrq.gov/products/medical-evidencecommunication/research-protocol (Accessed: April 14, 2025).
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iii.
Cote,
C.
(2021)
5
Principles
BDA B1
of
data
Ethics
for
business.
https://online.hbs.edu/blog/post/data-ethics (Accessed: April 14, 2025).
iv.
Directorate-General for Communication (no date) Legal framework of EU data
protection.
https://commission.europa.eu/law/law-topic/data-protection/legal-
framework-eu-data-protection_en (Accessed: April 14, 2025).
v.
Ghent University (2024) GDPR: when do I engage in 'profiling'; what should I think
about? And what is 'exclusively automated individual decision-making'?
https://onderzoektips.ugent.be/en/tips/00002169/# (Accessed: April 14, 2025).
vi.
Hulatt,
L.
(2022)
Ethical
Issues
In
Data
Collection.
https://www.studysmarter.co.uk/explanations/english/research-and-composition/ethicalissues-in-data-collection/ (Accessed: April 14, 2025).
vii.
Kosinski, M. (2024) What is a data breach? https://www.ibm.com/think/topics/databreach (Accessed: April 14, 2025).
viii.
Mildebrath, H. (2025) Understanding EU data protection policy, European
Parliament.
PE
698.898.
European
Parliament.
https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/698898/EPRS_BRI(202
2)698898_EN.pdf (Accessed: April 14, 2025).
ix.
Nawaz, S. (2024) Mastering Data Interpretation: Techniques, Insights, and
Strategies.
https://www.linkedin.com/pulse/mastering-data-interpretation-
techniques-insights-strategies-nawaz-wkjyf (Accessed: April 14, 2025).
x.
Parliament of the UK (2019) The Right to Privacy (Article 8) and the Digital
Revolution.
https://publications.parliament.uk/pa/jt201919/jtselect/jtrights/122/12204.htm
(Accessed: April 14, 2025).
xi.
Shelley, S. (2024) Understanding the Ethics of Data Collection and Responsible Data
Usage.
https://www.ucumberlands.edu/blog/understanding-the-ethics-of-data-
collection (Accessed: April 14, 2025).
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