Exploratory Analysis on Data Privacy and Access

This study explores the opinions of consumers regarding data privacy and their personal awareness about the crisis. The survey was conducted using the Survey Monkey online survey facility. Consumers were concerned about the privacy and access of their personal and financial information. The hazard of loss of financial and identity data was the primary concern. People were also found to be enthusiastic about providing access to personal database for better application and surfing experience.

An Exploratory Analysis on Data Privacy and Access

Introduction
Most people opine that data privacy is an important factor, but the interpretation of security is
entirely personal and is hard to define. The continued dominance of the Internet and mobile
applications assess the understanding of privacy. The incessant commercial and social
engagement of the society in the online environment amplifies the risk of data theft. Awareness
about online piracy of personal information during online activities is utmost essential,
particularly while purchasing goods and services.
The present survey was conducted using the Survey Monkey online survey facility. The scholar
intended to interview consumers about their views regarding data privacy and their personal
awareness about the crisis (Aggarwal, and Philip, 2008).
Data Collection
A questionnaire was prepared utilizing the interactive interface of Survey Monkey. Along with
the demographical information (age, and gender), opinions about the present issue was sought.
Questions were arranged in order of convenience of answering. The survey link was shared
through emails, Facebook, and Whatsapp, and was open for two consecutive days. Total 64
responses were obtained in two days, and the scholar closed the survey. One of the responses
was deleted for missing value, and another reply was unreliable due to choice the second option
for all the questions. The data exploration was conducted with 62 valid responses.

Participants’ Demographic
The data collected from the survey comprised of 58.1% males and 40.3% females. One of the
respondents preferred not to reveal the information regarding gender.
Table 1: Frequency Distribution of Gender of the Respondents
Q1 Frequency Percent Valid Percent Cumulative
Percent
Male 36 58.1 58.1 58.1
Female 25 40.3 40.3 98.4
Prefer not to say 1 1.6 1.6 100.0
Total 62 100.0 100.0
Note: Q1: Please specify your gender
i reF gu 1: Distribution of Gender of the Respondents

The age distribution was skewed towards the younger age groups, where fifty percent answers
came from the school and college students. It was probably an indicator of the fact that young
generation is more inclined towards online purchases, and also aware of the menace of data theft.
Table 2: Frequency Distribution of Age Group of the Respondents
Q2 Frequency Percent Valid Percent Cumulative
Percent
10-25 31 50.0 50.0 50.0
25-40 19 30.6 30.6 80.6
40-60 11 17.7 17.7 98.4
60+ 1 1.6 1.6 100.0
Total 62 100.0 100.0
Note: Q2: Please choose your age group (in years)
i reF gu 2: Distribution of Age Group of the Respondents

Data Exploration
Effect of online brands and widespread social media was assessed through the third question. A
mass of respondents were found to have confidence on the online sites regarding the security of
personal information. The pattern reflected towards the fact that consumers were insistent
towards online operation. Presence of young age people probably inflicted the pattern of the
frequency distribution (Idreos, Papaemmanouil, and Chaudhuri, 2015).
Table 3: Frequency Distribution of Trust on Online Brands and Media Sites
Q3 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 7 11.3 11.3 11.3
Agree 20 32.3 32.3 43.5
Neutral 23 37.1 37.1 80.6
Disagree 7 11.3 11.3 91.9
Strongly Disagree 5 8.1 8.1 100.0
Total 62 100.0 100.0
Note: Q3: I trust online brands and social media sites to handle my personal information appropriately
i reF gu 3: Distribution of Trust on Online Brands and Media Sites

More than 37% respondents were found to be satisfied with security of personal information
shared during online transactions or social media interactions. But, 56.5% consumers were also
not very confident about the safety of their shared information.
Table 4: Frequency Distribution of Satisfaction level of the Private Data in Internet
Q4 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 2 3.2 3.2 3.2
Agree 21 33.9 33.9 37.1
Neutral 20 32.3 32.3 69.4
Disagree 15 24.2 24.2 93.5
Strongly Disagree 4 6.5 6.5 100.0
Total 62 100.0 100.0
Note: Q4: I am satisfied with the level of security of my private data on the internet
i reF gu 4: Distribution of Satisfaction level of the Private Data in Internet

Consumers were in agreement regarding the responsibility of the government in data protection
and access to proper stake holder. A minority of response were found to conflict with the sole
responsibility of the government for data security.
Table 5: Frequency Distribution of Government Role in Data Protection
Q5 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 10 16.1 16.1 16.1
Agree 21 33.9 33.9 50.0
Neutral 17 27.4 27.4 77.4
Disagree 10 16.1 16.1 93.5
Strongly Disagree 4 6.5 6.5 100.0
Total 62 100.0 100.0
Note: Q5: I believe that the government bears maximum responsibility for protecting my data
i reF gu 5: Distribution of Government Role in Data Protection

Consumers were ready to share their data for express service from the government or competent
authority. People were either not very sure about their opinion or they were in support of the
access to their personal information. Objection of 27.4% of respondents was also an important
observation.
Table 6: Frequency Distribution of Views about Access of Private Data
Q6 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 5 8.1 8.1 8.1
Agree 18 29.0 29.0 37.1
Neutral 22 35.5 35.5 72.6
Disagree 10 16.1 16.1 88.7
Strongly Disagree 7 11.3 11.3 100.0
Total 62 100.0 100.0
Note: Q6: I don't mind government sectors having access to my personal details for fast service
i reF gu 6: Distribution of Views about Access of Private Data

Nowadays people are more inclined towards compact gadgets for online experience. Consumers
were found to be poised between agree and disagree grouping. For better experience people
generally allow different permissions to the mobile applications without even noticing the terms
and policies. Here 33.9% percent response revealed awareness about data theft and access,
whereas 29% were in favor of compromising their personal information against utilization of
mobile applications.
Table 7: Frequency Distribution of Opinions about Permission to Mobile Applications
Q7 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 2 3.2 3.2 3.2
Agree 16 25.8 25.8 29.0
Neutral 23 37.1 37.1 66.1
Disagree 15 24.2 24.2 90.3
Strongly Disagree 6 9.7 9.7 100.0
Total 62 100.0 100.0
Note: Q7: I use mobile/tab as a primary gadget for communication and do not mind granting permission to
applications for better usage experience
i reF gu 7: Distribution of Opinions about Permission to Mobile Applications

Awareness about data privacy and security was visible from the distribution of the responses. A
mass of 59.7% response disclosed that consumers were concerned about their private
information shared during online interactions.
Table 8: Frequency Distribution of Data Security Practices
Q8 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 9 14.5 14.5 14.5
Agree 28 45.2 45.2 59.7
Neutral 13 21.0 21.0 80.6
Disagree 10 16.1 16.1 96.8
Strongly Disagree 2 3.2 3.2 100.0
Total 62 100.0 100.0
Note: Q8: I follow good online security practices, e.g. changing passwords regularly, reading privacy policies
i reF gu 8: Distribution of Data Security Practices

This question was the most important issue regarding the access to the database of consumers.
People were very skeptical about the issue of breach in personal information records and 80.6%
of the response agreed about the immediate notification in case of data theft. A mere 3.2%
disagreement indicated presence of few people in the society who were ignorant about the
hazards.
Table 9: Frequency Distribution of Opinions about Notification of Data Breach
Q9 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 33 53.2 53.2 53.2
Agree 17 27.4 27.4 80.6
Neutral 10 16.1 16.1 96.8
Strongly Disagree 2 3.2 3.2 100.0
Total 62 100.0 100.0
Note: Q9: I believe that I should be immediately notified of any data breach of my personal data
i reF gu 9: Distribution of Opinions about Notification of Data Breach

Consumers were found to be more aware about their financial data compared to demographical
information or any other personal information. About 58.1% opined that information regarding
financial issues was more sensitive compared to personal information.
Table 10: Frequency Distribution of Opinions about Privacy of Data
Q10 Frequency Percent Valid Percent Cumulative
Percent
Strongly Agree 12 19.4 19.4 19.4
Agree 24 38.7 38.7 58.1
Neutral 13 21.0 21.0 79.0
Disagree 9 14.5 14.5 93.5
Strongly Disagree 4 6.5 6.5 100.0
Total 62 100.0 100.0
Note: Q10: I am more careful of privacy for financial data but less for private data
i reF gu 10: Distribution of Opinions about Privacy of Data

Factor Analysis
Other than the demographic information, all other attributes were analyzed for clustering.
Reliability measure with Cronbach alpha value of 0.55 with eight factors was strong enough for
cluster analysis. Principal component analysis was performed for component extraction with
Varimax rotation and Kaiser Normalization.
Ta leb 11 otated omponent Matri: R C x
Cluster Analysis
Component
Trust on
online
security
Access
to
Protected
Data
Awareness
about Data
Privacy
I trust online brands and
social media sites to handle
my personal information
appropriately
.808
I am satisfied with the level of
security of my private data on
the internet.
.885
I use mobile/tab as a primary
gadget for communication
and do not mind granting
permission to applications for
better usage experience.
.665
I don't mind government
sectors having access to my
personal details for fast
service.
.593
I follow good online security
practices, e.g. changing
passwords regularly, reading
privacy policies.
.740
I believe that I should be
immediately notified of any
data breach of my personal
data.
.602
I believe that the government
bears maximum
responsibility for protecting
my data.
.615
I am more careful of privacy
for financial data but less for
private data.
.878

Three factors were extracted with 62.95% explanation of the variance of the model. The rotations
converged in 6 iterations, and the matrix in Table 11 was constructed from the rotated
component matrix. The three factors were identified as Trust on Online Security, Access to
Protected Data, and Awareness about Data Privacy.
Cluster Analysis
Discriminant classification revealed that null hypothesis of equal population of male and female
covariance failed to get rejected (Box’s M = 63.40, p = 0.3) (Mori et al., 2017). The canonical
correlation was 0.5, and prediction model was statistically significant (Wilk’s Lambda = 0.74, p
< 0.05). Male was the worst predictor of the model and female was the only significant predictor
with un-standardized canonical function value of 0.70. The important functions from the
structure matrix were identified as “I am more careful of privacy for financial data but less for
private data”, “I follow good online security practices, e.g. changing passwords regularly”, “I
believe that the government bears maximum responsibility for protecting my data”, and “I trust
online brands and social media sites to handle my personal information appropriately”.
Conclusion
Consumers were concerned about the privacy and access of their personal and financial
information. The hazard of loss of financial and identity data was the primary concern. People
were also found to be enthusiastic about providing access to personal database for better
application and surfing experience. Older age groups were found to be incredulous about the

reliability of private as well as government sources regarding data theft (Ruj, Stojmenovic, and
Nayak, 2012).
Majority of the response wanted the government to regulate the privacy issue and inform in case
of breach of security of data. They believed on the security level of banking applications and
websites as the best privacy protected online resources, followed by government sector. Faith on
ecommerce websites and social media was a distant last (Fuster, 2014).
The current research was based on semi quantitative data analysis. Effect size of the sample was
not estimated. For superior reliability of the factors and consistency of the components larger
sample data was required.

Reference List
Aggarwal, C.C. and Philip, S.Y., 2008. A general survey of privacy-preserving data mining
models and algorithms. In Privacy-preserving data mining (pp. 11-52). Springer, Boston, MA.
Fuster, G.G., 2014. The emergence of personal data protection as a fundamental right of the
EU (Vol. 16). Springer Science & Business.
Idreos, S., Papaemmanouil, O. and Chaudhuri, S., 2015, May. Overview of data exploration
techniques. In Proceedings of the 2015 ACM SIGMOD International Conference on
Management of Data (pp. 277-281). ACM.
Mori, U., Mendiburu, A., Keogh, E. and Lozano, J.A., 2017. Clustering analysis aims to group a
set of similar data objects into the same cluster. Topic models, which belong to the soft
clustering methods, are powerful tools to discover latent clusters/topics behind large data sets.
Due to the dynamic nature of temporal data, clusters often exhibit complicated patterns such as
birth, branch and death. However, most existing temporal clustering models assume... Data
Mining and Knowledge Discovery, 31(1), pp.233-263.
Ruj, S., Stojmenovic, M. and Nayak, A., 2012, May. Privacy preserving access control with
authentication for securing data in clouds. In Cluster, Cloud and Grid Computing (CCGrid),
2012 12th IEEE/ACM International Symposium on (pp. 556-563). IEEE.

Appendix
SPSS Raw Frequency Tables
Table 12: Please specify your gender
Table 13: Please choose your age group (in years)

Table 14: I trust online brands and social media sites to handle my personal information appropriately
Table 15: I am satisfied with the level of security of my private data on the internet

Table 16: I believe that the government bears maximum responsibility for protecting my data
Table 17: I don't mind government sectors having access to my personal details for fast service

Table 18: I use mobile/tab as a primary gadget for communication and do not mind granting permission to
applications for better usage experience
Table 19: I follow good online security practices, e.g. changing passwords regularly, reading privacy policies

Table 20: I believe that I should be immediately notified of any data breach of my personal data
Table 21: I am more careful of privacy for financial data but less for private data

Cluster Analysis SPSS Raw Outputs