Big Data Privacy: Balancing Personalization and Privacy

This article discusses the challenges of balancing personalization and privacy in big data. It explores cyber risks, cyber terrorism, and cybercrime. The article also provides solutions to protect personal data. Topics covered include schema mapping, NoSQL databases, and NewSQL.

BIG DATA
PRIVACY 1 1
Big data privacy: Privacy versus personalization, customers want more customization when
frequently visiting their favorite online store but that comes at a price of privacy.
Name of student
Institution
Date of submission

BIG DATA PRIVACY 2
Table of Contents
Framing the problem........................................................................................................................3
Framing the solution........................................................................................................................4
Concept development......................................................................................................................8
Invention of computes and their effects to the modern society.................................................8
Commercialization Plan.................................................................................................................15
Bank fraud detection system..........................................................................................................17
References......................................................................................................................................18

BIG DATA PRIVACY 3
Framing the problem
Privacy vulnerabilities are regularly reported by media and research whereby sensitive
data leaks to the public domain. Most of these incidences occur as a result of data being
infiltrated maliciously by specific malware. Leaks in most cases do not happen because of
malicious intent by the application’s author but rather as a result of mis-configuration of these
particular applications or side effects that are unexpected. The problem is that applications can
have the ability to exfiltrate the private data of a user and supply or send it out to some different
server.
In the current world, smartphones have grown to be an integral part of most people's
daily lives, most if not all people use their smartphones on a regular basis. Heightened
technology use, therefore, has facilitated the efficiency with which primary and recurrent tasks
are carried out. Essential among these essential functions and necessities is security. More
specifically, personal protection at homes and private property is an imperative need (Oulasvirta,
2012). Most people have endeavored to acquire the best security mechanisms to safeguard their
properties. Principal among these security measures is locked. These locks are operated by keys
which have proven to be quite exhaustive, prone to break-ins and even misplacement hence the
significant need for more efficient locks.
Cyber risks are threats that come from a globally connected network like the internet.
Cybersecurity, therefore, can mitigate cyber risk to acceptable levels and limit the impact. The
safety of a nation is of great concern to the government and its citizens (Maillart, 2010). Threats
such as wars, terrorism, cyber attacks or espionage can significantly affect national security.

BIG DATA PRIVACY 4
It is the responsibility of the government to protect the state and citizens against all forms
threats or attacks. Governments employ some measures to ensure national security: use of the
armed forces, diplomacy, intelligence services to avoid risk and resilience of critical
infrastructure. The safety of a country is very vital to the peace, stability and economic growth of
a nation.
Cyber terrorism and attacks have negative implications for the security of the national
infrastructure. Cyber terrorism refers to use of computer networks to crumple critical
infrastructures such as transport, energy and government functions. High dependence on the
computer networks poses vulnerabilities to nations. The vulnerabilities can be exploited by
hostile groups to disrupt critical services (Taylor R. F., 2014). Cyber warfare against
infrastructure may be targeted to cause power outages, flight delays, and communication
disruptions.
Framing the solution
Computer attacks result to different effect on the computer. Physical attacks affect the
reliability of the machine and availability of data. The electronic attack erases the electronic
memory, upsets the software and permanently disables the electronic components. The computer
network attack disrupts the integrity of data through a malicious code.
The database management system offers solutions to cyber and national security in many
ways. A data management system is comprised of team members’ assigned different tasks. A
data collector collects different sets of data and stores data collected in a management data
storehouse. Data analyzer inspects, cleanses, models and transforms data to determine useful
information. Project director manages and oversees both the information technology project and

BIG DATA PRIVACY 5
project team members. Database designer is responsible for designing of a logical and physical
design of the database management system (O'Brien, 2015). The computing staff is responsible
for storing and backup of data.
The fundamental role of big data integration in Big Data has conceived the need for an
intensive research on some factors that may come as a challenge for this integration. One of
these factors is the Schema mapping where Schema mapping can be briefly described as a data
integration structure that aids in the establishment of a universal schema while assisting in the
location of the mappings that exist in between the universal schemas and the limited schemas
(Tankard, 2012). Thus determining which of the schemas mentioned above contain the same data
or information. A schema can be defined generally as a system that allows the user to structure
any piece of data received even the most unstructured amount of data which is a requirement
before one is able to use this data. There are two types of schemas, the "schema on read" and the
schema on write which is the most popular and efficient type of schema that is used.
The NoSQL which is an abbreviation of Not Only SQL database is advancement to a
database design that accommodates a wide array of information models that may include
columnar, document, key-value and graph formats (McCreary, 2014). This term can also be used
as a general depiction of almost all non-relational technologies adopted and used for data
management. The NoSQL is usually seen as a substitute for the traditionally interrelated
databases due to their differing approaches in some sectors. The traditional relational databases
involve a tabular placement of data, and a vigilant designing of data schema prior to the
construction of a database (Okman, 2011)

BIG DATA PRIVACY 6
The most current innovation that is seen to be intimidating this dominance is the NoSQL
database which has raised a rather controversial subject with many researchers quoting the
decline and a possible end in an era of relational models. Relational models in their development,
their initial function were the administration of structured data which required additional systems
that would first structure these unstructured tracts of data. This simplicity has been a source of
criticism for the whole model with critics advocating for the development of a model that will
eliminate this need of structuring data something that the NoSQL provides for the user (Zaki,
2014). These criticisms are one of the few reasons that some programmers are said to have
foreseen the abandonment of these relational database systems, which leads us to the NoSQL. A
brief insight into the NoSQL and how the system functions will help us to find out what edge it
has over the relational database systems.
.
The human resource sector is the other area that may be vastly affected by the proposed
change in the database management system. This is due to the fact that most of these companies
have based their present and intended skill set on these systems and thus an overhaul would
cause stagnation in the day to day activities, of most of these companies for a long time as they
look to change these systems.
In relation to this, there is also the NewSQL that has cemented the relational database
management systems in the modern field of technology. The NewSQL can be defined as a rank
of up to date relational database management systems whose primary function is the provision of
performance models that level up to those of the NoSQL systems. The NewSQL developers
aimed to achieve this by incorporating online transaction processing read and write workloads

BIG DATA PRIVACY 7
similar to those of the NoSQL while still involving the Atomicity, consistency, isolation and
durability guarantees that were offered in the traditional relational database models. The term
New SQL was first coined in 2011 where it was used to describe the modern class of database
systems that were said to challenge to the then established vendors Oracle, IBM, and Microsoft.
The figure below shows how Oracle’s NoSQL Database fits into a data-cycle ecosystem.
Source (McCreary, 2014)
To understand the improvements that have been done to the system and how the
system is different from the previous SQL the improvements have to be analyzed in three
categories. First is the novel system which majorly incorporates new architecture in the sense
that these novel systems which are the new DBMS models are built from scratch. A clear insight
on this reveals that developers opted to come up with new code bases which are very
distinguished from the ones used in the NoSQL systems and the SQL systems. This kind of
innovation has proved to have one core advantage over the other Database management systems;

BIG DATA PRIVACY 8
one of the very few features that distinguish NewSQL systems from the others is the fact that all
the segments of the model can be optimized for multi-node environments.
Concept development
Invention of computes and their effects to the modern society
Computers have evolved in functionality and design for the past 19th century. Since the
invention of the computer, there have been various uses that it has helped human beings to
perform better. For instance, the early supercomputers were used in the manipulation and storage
of data. They were mostly used by scientists to record relevant data and analyse it using
sophisticated algorithms. In the 21st century, there has been a tremendous growth of the
technology sector. Computers have continued to be redesigned and better programs designed to
TypesofDataScalingDatabases&the2PCProtocolTheCAPTheoremandtheBASEPropertiesNoSQLDatabases

BIG DATA PRIVACY 9
be installed on the computers. Concerning the design, it is clear that there has been a great
evolution in the size of computers availed for personal or commercial use. The use of Android
phones, tablets and laptop computers has gained popularity in the mid-90s and the entire 21st
century. The availability of computer gadgets in the society has helped many people to perform
tasks efficiently and effectively easily. However, there are various drawbacks that ace computer
developers and users. According to Carr “The Net’s interactivity gives us powerful new tools for
finding information, expressing ourselves, and conversing with others (Carr, 2010, p. 117). It
also turns us into lab rats constantly pressing levers to get tiny pellets of social or intellectual
nourishment.
Cybercrime is a primary problem that affects computer users globally. The rise of hackers
in the 21st century can be attributed to the high level of coding abilities by malicious individuals.
There are those individuals that only work on their computers to harass other computer users
(Taylor, 2014). Sometimes, users may be held ransom when a hacker accesses their important
information. The United States of America, Russia, Japan, and China are some of the major
countries that harbor a large number of professional hackers. Cybercrime is gaining popularity,
and some computer hackers have turned to online crimes as a way of making their daily
earnings. This paper will focus on discussing some of the cyber crimes that are existent in the
world of technology today. Also, the paper will give an insight into the various methods that
users can use to protect themselves from cyber crimes in the long run.
Cyberbullying: According to statistics presented by technological researchers, it is
indicated that most computer users in the world are subjected to cyberbullying. Usually, this is a
type of crime that involves one person who might present himself as being anonymous
continually harasses another computer user. The primary motive of cyberbullying is cited as to

BIG DATA PRIVACY 10
instill fear into the victim. Cyberbullying may be executed in for of sending texts to the user that
make them feel embarrassed or creates fear within them. Also, it could involve sharing
incriminating pictures of an individual (Slonje, 2009). This is an occurrence that has been
witnessed over the years. This is when the hackers threaten an individual that they would share
the important information about them in the form of pictures, texts or emails if they do not do
what they are requested to do. Cyberbullying is rampant among new computer users. The main
attributive facto to this is that the new computer users do not have the required skills to protect
their networks from hackers. This means that such users are prone to be harassed many times by
malicious hackers. Cyberbullying has also been cited as causing trauma and stress to many of the
victims.
Phishing: This is another major cybercrime that affects computer users. According to
research, it is indicated that phishing involves the use of fake emails with particular links aimed
at collecting personal information of a victim. The personal information collected through this
criminal act may include usernames and passwords (Wu, 2014). The hackers are smart in
creating catchy emails and sending them to victims. The unaware victims sometimes may get
tempted to open the malicious emails and to click on the links attached. Through this way, the
hackers are then able to collect information about the user in two shakes of a lamb’s tail. The
hackers may then use the information to ask for ransom from the victim’s failure to which the
information may be misused for other criminal activities.
Hacking: this is the most common of all types of cybercrimes. Hacking involves
unauthorized access to other people's computers and using them as one's own. Also, it may
include the stealing of websites and computer networks. When conducting hacking, it requires
that the hacker to be an expert in creating an algorithm that would surpass the security measures

BIG DATA PRIVACY 11
installed by the victims. Through this way, the hacker can secretly access the computers remotely
and operate them as they wish. This crime could lead to loss of finance among other important
contents of business of personal content.
Spreading hate and terrorism; In most countries, incitement is considered as being a
criminal act. There are well laid down rules and regulations by which every citizen of a country
is required to abide by. For this reason, any person that is found violating the constitution
through the spread of hate speech and promoting terrorist activities is considered as a cyber-
criminal. In some states, I may be punishable by the death sentence. The victims are presented
with information on hatred against a particular individual or a group of people.
Grooming: when children are still below the majority age, they are not allowed to access
appropriate materials. However, there are cyber criminals that introduce young children below
the majority age to sexual activities. They make sexual advances towards the children which may
make them feel embarrassed or violate their peace. This crime is rampant among the USA
citizens, and it calls for the government to work tirelessly in trying to curb such criminals.
Various methods may be used to protect one from cyber-crimes. Being a victim of cyber-
crime may be traumatizing, and thus, it would be important to be careful about it through the
implementation of mitigative measures in the long run.
Use of antivirus software: There exists much software designed to detect and eliminate
any external threats referred to as viruses. When a virus is sent to a person’s computer by a
malicious person, it could lead to loss of massive databases in the long run. Therefore, it is
important to have an up to date antivirus software on a computer, phone or tablet. Using this
would always make it easy to be protected from eminent cyber-attacks (Ross,2016).

BIG DATA PRIVACY 12
Another most recommended method of avoiding cyber-attacks is to be careful about what
we click. When one does not expect an email, it is important just to delete such emails to prevent
opening them accidentally. Also, before opening any emails, it is important to analyze the level
of security of the emails. This can be done by scanning any attachments within the emails.
Use different passwords: Hackers have the notion that, most people use passwords that are
almost similar to their online accounts. For this reason, it would be recommended that individual
use of a different unique password for every account online. Through this, it will make it difficult
for the cybercriminals to hack the passwords and usernames of the various online accounts.
Avoidance of public networks: Cybercriminals have specialized in getting personal
information of individuals through the use of public hotspots. It is cited that most public WIFI
networks do not guarantee security for personal information. When the data being shared in a
public network is not encrypted, packet sniffers may intercept the data and allow the hackers to
have access to it. In this case, it would be recommendable if individuals avoided the use of
public WIFI in sharing personal information.
Use two-step verifications: Emails are a main target for cybercriminals. When they hack
an individual's email, they could also gain access to other important information about an
individual such as the date of birth, residential address among others. With two-step
verifications, one is notified whenever there is an attempt to login. Usually, the notification
requires one to input a particular secret code that is not accessible to third parties. This means
that without the code, the hacker cannot access the emails or other online accounts. It is therefore
recommended that every individual should have two-step verifications on accounts that have
critical information.

BIG DATA PRIVACY 13
The introduction and the revolution of communication have led to the formulation of
distribution systems that require the carrying of information from the terminal user to the other
sets of computers. Network security, therefore, is fundamental in protecting the data during the
process of transmission. The various mechanisms established to meet such specification for
instance authentication or confidentiality proves to be quite difficult (Cho,2011). Therefore an
individual must consider developing specific measures when incorporating the security
mechanisms. These mechanisms include not only algorithms and protocols but the people
involved must have secret information hence extends doubts on the creation of the dissemination
and protection of this information. Therefore becoming important to create a model where the
security services may be viewed.
Conversely, for the management of an organization needs to understand the security
needs, there should be a systematic way for the system to be at a sufficient level. The approach
that may be used is to consider some aspects of information security that is the security service,
mechanism and security attacks. The security attack aims to identify the ways through which
intruders may get unauthorized data using several mechanisms in providing such services. The
information system is becoming more relevant in conducting activities thereby the electronic
information taking roles which were done on papers (Fong & Siu 2016).
Data warehousing impact banking industry by providing cost-effective decision making,
enhanced customer service, enhanced asset and liability management and better enterprise
intelligence. This paper will discuss the integration of banking data to produce format required
for data mining, data mining techniques and their pattern and how they solve frauds in banks.
This article will also talk about challenges of data warehousing of financial transactions in the
banking industry and their solutions through data mining algorithm.

BIG DATA PRIVACY 14
Financial transaction data from a warehouse is integrated to produce format required for
data mining depending on the ability to unite data across datasets, catalogs, domains to equip
data users with the ability to find, access, integrate and analyze the combination of datasets based
on their roots.
Different approaches are used depending on the type of data and integration approaches
available. Unstructured data such as images of debit card holders are organized for efficient
retrieval through traditional enterprise approaches by creating data warehouses which are
regularly updated and are analyzed carefully, For example, debit card holders are supposed to
renew their cards regularly after a specified period to update information on the debit cards.
Also, search engines such as Google, opera, and maxilla firebox can be used together with
Metadata based tools. Structured data from network platforms use a combination of languages
tools such as MySQL language and other tools to provide metadata mark up through controlled
vocabularies. For example, Information about withdrawal transactions from different bank's
branches is obtained from the network topology of the bank, and it is then integrated through
visualization system like Google visualize that extract information from tables and subcategories.
Also, integration of financial transaction data occurs through data model whereby data in a
database is organized and structured in a particular way to serve user's needs. Data models used
include relational database whereby information is held in the form of separate tables from which
bank’s data is accessed through different ways. (Agarwal & Tayal 2009)The object-oriented
database can also be used; whereby class, object, attributes, and methods are defined. For
example, a database can contain the name of account holder, age, private place and identification
number. Also, a hierarchical database is also used whereby bank’s records are linked together to

BIG DATA PRIVACY 15
form a hierarchical structure. Moreover, a network diagram can also be used in which each file
can have multiple owners, for example, several people can own a joint bank account.
Commercialization Plan
Banks adopt different techniques of data mining to manipulate a large quantity of data stored
in the data warehouse to create models that create valuable assumption. Those techniques include
association whereby a pattern is discovered from a relationship between items in the same
transaction. For example, a bank manager determines that a particular customer withdraws,
deposit transfers money at a specific branch of the bank. Therefore the manager will always
associate the customer with that particular branch, and in case a transaction is conducted in
another department, the system will detect, and a lot of precaution measures will be used to
ascertain whether it is the original owner of the debit card performing transaction.
Banks also use classification technique, whereby it applies mathematical techniques such
as linear programming, network diagrams, and statistics to classify data items into a predefined
set of groups (Berson & Smith 2010). For example, using an application software a prediction of
transition of junior bank accounts to either current or fixed reports can be made because age is a
determinant variable. Banks can also predict outcomes of advertisement of its services, for
example, whether new customers will be enlisted in the bank. In addition to that, banks can also
detect fraud through the use of probability techniques for evaluating the probability of a
customer using another branch of the bank.
Moreover, bank's fraud detection system uses prediction technique which applies both
independent and dependent variables to discover the relationship between them given one
independent variable. For instance, prediction technique is used in anomaly detection while

BIG DATA PRIVACY 16
investigating cases of fraud on manipulated data whereby financial data is the independent
variable. For example, a bank manager can detect fraud if a debit a holder conducts the
transaction in different branches of the bank within a time interval which is not even enough to
reach the location. This can indicate the use of a duplicate debit card.
Bank fraud detection system
Data mining plays significant roles in different industries; for example in marketing, it assists
retailers to predict purchase patterns of their customers and thus ensures there are enough
inventories. It also helps the government to detect cases of money laundering and siphoning
basing its argument on the data that is fed in exchequer account and even the pattern of
TRANSACTION
ACCOUNT
BALANCE
BEFORE
DEPOSIT WITHDRAWAL TRANSFER
55
DOLLAR
S
45
DOLLARS 2 PM
VIRGIN
IA
5
DOL
LAR
VIRG
INIA 7
PM
NEW
YOR
K
CITY
1. OOAM
95
DO
LL
AR
S

BIG DATA PRIVACY 17
withdrawal. Data mining also assist banks in ensuring the security of their customers' accounts;
by detecting a pattern of retreat and location of resignation basing their arguments on site and
frequency of removal.
References
Carr, N. "What the Internet is doing to our brains–the Shallows. Chapter 07: The Jugglers Brain,
132." (2010).
Cho, Kwan-Hwi, and Nam-Mi Kim. "Portable computer on which a communication device can
be mounted." U.S. Patent No. 6,049,450. 11 Apr. 2011.
Fong, J., & Siu, B. (2016). Data mining, data warehousing & client/server databases. Springer.
Agarwal, B., & Tayal, S. (2009). Data mining and data warehousing. New Delhi: University
Science Press. (Berson & Smith, 2001)
Berson, A., & Smith, S. (2010). Data warehousing, data mining, and OLAP. New York:
McGraw-Hill.
Marakas, G. (2017). Modern data warehousing, mining, and visualization. Upper Saddle River,
N.J: Prentice Hall.
Maillart, T., & Sornette, D. (2010). Heavy-tailed distribution of cyber-risks. The European
Physical Journal B, 75(3), 357-364.

BIG DATA PRIVACY 18
Slonje, Robert, and Peter K. Smith. "Cyberbullying: Another main type of
bullying?." Scandinavian journal of psychology49.2 (2009): 147-154.
Stallings, W., Brown, L., Bauer, M.D. and Bhattacharjee, A.K., 2012. Computer security:
principles and practice (pp. 978-0). Pearson Education.
Taylor, Robert W., Eric J. Fritsch, and John Liederbach. Digital crime and digital terrorism.
Prentice Hall Press, 2014.
Ross, Ron. "Computer program protection method." U.S. Patent No. 4,462,078. 24 Jul. 2016.
Wu, Min, Robert C. Miller, and Simson L. Garfinkel. "Do security toolbars actually prevent
phishing attacks?." Proceedings of the SIGCHI conference on Human Factors in
computing systems. ACM, 2014.
Elmasri, R. and Navathe, S., 2010. Fundamentals of database systems. Addison-Wesley
Publishing Company.
McCreary, D. and Kelly, A., 2014. Making sense of NoSQL. Shelter Island: Manning, pp.19-20.
Okman, L., Gal-Oz, N., Gonen, Y., Gudes, E. and Abramov, J., 2011, November. Security issues
in nosql databases. In Trust, Security and Privacy in Computing and Communications
(TrustCom), 2011 IEEE 10th International Conference on (pp. 541-547). IEEE.
O'Brien, J.A. and Marakas, G.M., 2005. Introduction to information systems (Vol. 13). New
York City, USA: McGraw-Hill/Irwin
Oulasvirta, A., Rattenbury, T., Ma, L. and Raita, E., 2012. Habits make smartphone use more
pervasive. Personal and Ubiquitous Computing, 16(1), pp.105-114.
Tankard, C., 2012. Big data security. Network security, 2012(7), pp.5-8.

BIG DATA PRIVACY 19
Taylor, R.W., Fritsch, E.J. and Liederbach, J., 2014. Digital crime and digital terrorism. Prentice
Hall Press
Zaki, A.K., 2014. NoSQL databases: new millennium database for big data, big users, cloud
computing and its security challenges. International Journal of Research in Engineering
and Technology (IJRET), 3(15), pp.403-409.