The document discusses a proposed study on improving security in business economics through the use of big data analytics. It aims to identify gaps in previous methods and design a system to address these gaps. The study focuses on the unstructured nature of big data and issues of privacy and security. The proposed methodology includes encrypting data, implementing access controls, and using techniques like homomorphic encryption to enable queries on encrypted data while maintaining security and privacy. The goal is to balance utilizing big data with meeting legal requirements and protecting sensitive information.

1. A study on Impact of Big Data analytics in Business
Economics
Pakalapati Venkatapathi Raju
3rd
year B.Tech, Department of Information Technology,
Hindustan Institute of Technology and Science,
#1,IT Expressway, Bay Range Campus, Padur, Chennai–
603103, Tamil Nadu, India.
18132005@student.hindustanuniv.ac.in
Dr. C.V. Suresh Babu
Professor, Department of Information Technology,
Hindustan Institute of Technology and Science,
#1,IT Expressway, Bay Range Campus, Padur, Chennai–
603103, Tamil Nadu, India.
pt.cvsuresh@hindustanuniv.ac.in
Abstract— The proposed study focuses mainly of improving
the security threat in the domain of business economics by using
big data analytics. Its is an survey paper which intensively give
review many published research papers in this domain and
identified the gap which is there in the previous method and try
to design a system to fill the gap.
Keywords— Bigdata, Economics, Security, Technology
I. INTRODUCTION
Economics in simple words is a study of relation between
production, consumption, and transfer of wealth. In
economics there is huge usage of raw data sets and huge
theoretical information about the productions, estimations and
the analyzation. In order to organize and interpret that data, we
need big data analytics to get a perfect approach. In business
economics there will be a huge scope for interpretation of data.
Hence for analyzing that data we need big data analytics in
business economics.
II. RATIONALE BACKGROUND:
The basic need for this study is:
 Unstructured nature of big data available in raw form
 Privacy and security violations
III. OBJECTIVES
Primary objective: To bring a culture of sorted big data to
avoid confusion which leads to miss interpretation of data. To
reduce the risk of data breaches and cyber-attacks that leads to
the breaching of sensible data
Secondary Objectives: Keep on improving and updating our
research on privacy and data sorting for the better
understanding of the big data economics even by the normal
individual with minimal knowledge on big data
IV. REVIEW OF LITERATURE
 Big data from “Man, machine, and material” three
elements’ interaction and fusion in cyberspace has brought
tremendous opportunities and brought many scientific
problems and challenges to the prevail in IT infrastructure,
machine processing, and computing power. (Rivera & van
der Meulen, 2014).
 The present development of massive data is additionally
facing many problems, but security and privacy issues are
one of the key issues by people recognized in recent years.
Among them, privacy issues have existed an extended
time.(Rivera & van der Meulen, 2014).
 In computational sciences, Big Data is a critical issue that
requires serious attention.
 Thus far, the essential landscapes of Big Data have not
been unified. Furthermore, Big Data cannot be processed
using existing technologies and methods. (Rivera & van
der Meulen, 2014).
 Therefore, the generation of incalculable data by the fields
of science, business, and society is a global problem. With
respect to data analytics, for instance, procedures and
standard tools have not been designed to search and
analyze large datasets. (Rivera & van der Meulen, 2014).
 As a result, organizations encounter early challenges in
creating, managing, and manipulating large datasets.
 Systems of data replication have also displayed some
security weaknesses with respect to the generation of
multiple copies, data governance, and policy.
 These policies define the data that are stored, analyzed,
and accessed. They also determine the relevance of these
data. To process unstructured data sources in Big Data
projects, concerns regarding the scalability, low latency,
and performance of data infrastructures and their data
centers must be addressed (Rivera & van der Meulen,
2014).
 Researchers, agencies, and organizations integrate the
collected raw data and increase their value through input
from individual program offices and scientific research
projects.
 The data are transformed from their initial state and are
stored in a value-added state, including web services.
 Neither a benchmark nor a globally accepted standard has
been set with respect to storing raw data and minimizing
data. The code generates the data along with selected
parameters. (Rivera & van der Meulen, 2014).
 Organizations in the European Union (EU) can process
individual data even without the permission of the owner
based on the legitimate interests of the organizations as
weighed against individual rights to privacy. In such
situations, individuals have the right to refuse treatment
according to compelling grounds of legitimacy.
 Similarly, the doctrine analyzed by the Federal Trade
Commission (FTC) is unjust because it considers
organizational benefits.
 A major risk in Big Data is data leakage, which threatens
privacy. Recent controversies regarding leaked documents
reveal the scope of large data collected and analyzed over
a wide range by the National Security Agency (NSA), as
well as other national security agencies.
 This situation publicly exposed the problematic balance
between privacy and the risk of opportunistic data
exploitation [92, 93]. In consideration of privacy, the
evolution of ecosystem data may be affected. (Rivera &
van der Meulen, 2014).

2.  Moreover, the balance of power held by the government,
businesses, and individuals has been disturbed, thus
resulting in racial profiling and other forms of inequity,
criminalization, and limited freedom. (Rivera & van der
Meulen, 2014).
 Therefore, perfectly balancing compensation risks and the
maintenance of privacy in data is presently the greatest
challenge of public policy. (Rivera & van der Meulen,
2014).
 In decision-making regarding major policies, avoiding this
process induces progressive legal crises. (Rivera & van der
Meulen, 2014).
 Each cohort addresses concerns regarding privacy
differently. For example, civil liberties represent the
pursuit of absolute power by the government. (Rivera &
van der Meulen, 2014).
 These liberties blame privacy for pornography and plane
accidents. According to Hawks privacy, no advantage is
compelling enough to offset the cost of great privacy.
However, lovers of data no longer consider the risk of
privacy as they search comprehensively for information.
Existing studies on privacy [92, 93] explore the risks posed
by large-scale data and group them into private, corporate,
and governmental concerns; nonetheless, they fail to
identify the benefits. (Rivera & van der Merlen, 2014).
 Rubinstein [95] proposed many frameworks to clarify the
risks of privacy to decision makers and induce action. As
a result, commercial enterprises and the government are
increasingly influenced by feedback regarding privacy
[96]. (Rivera & van der Meulen, 2014).
 The privacy perspective on Big Data has been significantly
advantageous as per cost-benefit analysis with adequate
tools. These benefits have been quantified by privacy
experts. (Rivera & van der Meulen, 2014).
 However, the social values of the described benefits may
be uncertain given the nature of the data. Nonetheless, the
mainstream benefits in privacy analysis remain in line with
the existing privacy doctrine authorized by the FTC to
prohibit unfair trade practices in the United States and to
protect the legitimate interests of the responsible party as
per the clause in the EU directive on data protection.
(Rivera & van der Meulen, 2014).
 To concentrate on shoddy trade practice, the FTC has
cautiously delineated its Section 5 powers.
Confidentiality: Confidentiality refers to distorted data
from theft. Insurance can usually be claimed by encryption
technology [104]. If the databases contain Big Data, the
encryption can then be classified into table, disk, and data
encryption. (Rivera & van der Meulen, 2014).
 Data encryption is conducted to minimize the granularity
of encryption, as well as for high security, flexibility, and
applicability/relevance. (Rivera & van der Meulen, 2014).
 Therefore, it is applicable for existing data. However, this
technology is limited by the high number of keys and the
complexity of key management. (Rivera & van der
Meulen, 2014).
 Thus far, satisfactory results have been obtained in this
field in terms of two broad categories: discussion of the
security model and of the encryption and calculation
methods and the mechanism of distributed keys. (Rivera
& van der Meulen, 2014).
 A particular aspect of Big Data security and privacy must
be related with the rise of the Internet of Things (IoT).
‘IoT, defined by Oxford1 as “a proposed development of
the Internet in which everyday objects have network
connectivity, allowing them to send and receive data”, is
already a reality – Gartner estimates that 26 billions (about
824 years) of IoT devices will be installed by 2020,
generating an incremental revenue of $300 billion (about
$920 per person in the US) (Rivera & van der Meulen,
2014).
 The immense increase in the number of connected devices
(cars, lighting systems, refrigerators, telephones, glasses,
traffic control systems, health monitoring devices,
SCADA systems, TVs, home security systems, home
automation systems, and many more) has led to
manufacturers to push to the market, in a brief period, a
large set of devices, cloud systems and mobile applications
to exploit this opportunity.
 While it presents tremendous benefits and opportunities
for end-users it also is responsible for security challenges.
Summary of Review of Literature
From the review of literature, we want to show the related
works which were done previously and taking that as
reference we proceeded the research on the points what we
have selected.
V. PROPOSED METHODOLGY
 There is no single magical solution to solve the identified
Big Data security and privacy challenges and traditional
security solutions, which are dedicated to protect lesser
amounts of static data, are not ad equated to the novel
requisites imposed by Big Data services (Cloud Security
Alliance, 2013).
 There is the need to understand how the collection of
substantial amounts of complex structured and
unstructured data can be protected. Non-authorized
access to that data to create new relations, combine
different data sources and make it available to malicious
users is a serious risk for Big Data.
 The basic and more common solution for this includes
encrypting everything to make data secure regardless
where the data resides (data center, computer, mobile
device, or any other).
 As Big Data grows and it’s processing gets faster, then
encryption, masking and tokenization are critical
elements for protecting sensitive data.
 Due to its characteristics, Big Data projects need to take a
holistic vision at security (Tankard, 2012).
 Big Data projects need to take into consideration the
identification of the different data sources, the origin, and
creators of data, as well as who can access the data.
 It is also necessary to conduct a correct classification to
identify critical data, and align with the organization
information security policy in terms of enforcing access
control and data handling policies. (RA Popa & Redfield,
2012).
 As a recommendation, different security mechanisms

3. should be closer to the data sources and data itself, to
provide security right at the origin of data, and
mechanisms of control and prevention on archiving, data
leakage prevention and access control should work
together (Kindervag, Balaouras, Hill, & Mak, 2012).
 The new Big Data security solutions should extend the
secure perimeter from the enterprise to the public cloud
(Juels & Oprea, 2013). ❖In addition, similar mechanisms
to the ones used in (Luo, Lin, Zhang, & Zukerman, 2013)
can be used to mitigate distributed denial-of service
(DDoS) attacks launched against Big Data infrastructures.
 Also, a Big Data security and privacy is necessary to
ensure data trustworthiness throughout the entire data
lifecycle – from data collection to usage.
 The personalization feature of some Big Data services and
its impact on the user privacy is discussed in (Hasan,
Habegger, Brunie, Bennani, & Damiani, 2013).
 They discuss these issues in the backdrop of EEXCESS,
a concrete project aimed to both provide elevated level
recommendations and to respect user privacy. A recent
work describes proposed privacy extensions to UML to
help software engineers to quickly visualize privacy
requirements, and design them into Big Data applications
(Jutla, Bodorik, & Ali, 2013).
 While trying to take the most of Big Data, in terms of
security and privacy, it becomes mandatory that
mechanisms that address legal requirements about data
handling, need to be met.
 Secure encryption technology must be employed to
protect all the confidential data (Personally Identifiable
Information (PII), Protected Health Information (PHI)
and Intellectual Property (IP) and careful cryptographic
material (keys) access management policies, need to be
put in place, to ensure the correct locking and unlocking
of data – this is particularly important for data stored.
 In order to be successful these mechanisms need to be
transparent to the end-user and have minimal impact of
the performance and scalability of data (software and
hardware-based encryptions mechanisms are to be
considered) (Advantech, 2013).
 As previously referred, traditional encryption and
anonymization of data are not adequate to solve Big Data
problems. They are adequate to protect static information,
but are not adequate when data computation is involved
(MIT, 2014).
 Therefore, other techniques, allowing specific and
targeted data computation while keeping the data secret,
need to be used. Secure Function Evaluation (SFE)
(Lindell & Pinkas, 2002), Fully Homomorphic
Encryption (FHE) (Gentry, 2009) and Functional
Encryption (FE) (Goldwasser et al., 2014), and partition
of data on non-communicating data centers, can help
solving the limitations of traditional security techniques.
 Homomorphic encryption is a form of encryption which
allows specific types of computations (e.g. RSA public
key encryption algorithm) to be carried out on ciphertext
and generate an encrypted result which, when decrypted,
matches the result of operations performed on the
plaintext (Gentry, 2010).
 Fully homomorphic encryption has numerous
applications, as referred in (Van Dijk, Gentry, Halevi, &
Vaikuntanathan, 2010).
 The homomorphic encryption also enables searching on
encrypted data - a user stores encrypted files on a remote
file server and can later have the server retrieve only files
that (when decrypted) satisfy some Boolean constraint,
even though the server cannot decrypt the files on its own.
More broadly, the fully homomorphic encryption
improves the efficiency of secure multiparty computation.
 An important security and privacy challenge for Big Data
is related with the storage and processing of encrypted
data. Running queries against an encrypted database is a
basic security requirement for secure Big Data however it
is a challenging one.
 This raises questions such as a) is the database encrypted
with a single or multiple key; b) does the database needs
to be decrypted prior to running the query; c) do the
queries need to be also encrypted; d) who as the
permissions to decrypt the database; and many more.
 Recently a system that was developed at MIT, provides
answers to some of these questions. Crypt DB (Double
Bubble) allows researchers to run database queries over
encrypted data (Ra Popa & Redfield, 2011)
 Trustworthy applications that intent to query encrypted
data will pass those queries to a Crypt DB proxy (that sits
between the application and the database) that rewrites
those queries in a specific way so that they can be run
against the encrypted database.
 The database returns the encrypted results back to the
proxy, which holds a passkey and will decrypt the results,
sending the definitive answer back to the application.
Crypt DB supports numerous forms of encryption
schemes that allow diverse types of operations on the data
(RA Popa & Redfield, 2012).
 Based on Crypt DB, Google has developed the Encrypted
Big Query Client that will allow encrypted big queries
against their Big Query service that enables super, SQL-
like queries against append-only tables, using the
processing power of Google's infrastructure (Google,
2014).
 Apart from more specific security recommendations, it is
also important to consider the security of the IT
infrastructure itself. One of the common security practices
is to place security controls at the edge of the networks
however, if an attacker violates this security perimeter it
will have access to all the data within it. (RA Popa &
Redfield, 2012).
 Therefore, an innovative approach is necessary to move
those security controls about the data (or add additional
ones).
 Monitoring, analyzing, and learning from data usage and
access is also an important aspect to continuously
improve security of the data holding infrastructure and
leverage the already existing security solutions
(Kindervag et al., 2012; Kindervag, Wang, Balaouras, &
Coit, 2011).

4. VI. FUTURE SCOPE OF THE STUDY
In future we want to make this as reference for someone
who wants to find a better solution for the above problems
what we have mentioned.
VII. CONCLUSION
Big data can yield extremely useful information, it also
presents new challenges. especially, big data present a crucial
opportunity to extend value and performance for us. one
among the new challenges within the big data era is personal
privacy preserving. Big data privacy has become a crucial
issue since it's directly associated with customers. This paper
has described the concept of massive data, privacy preserving,
specific challenges issues, and a few big data privacy-
preserving techniques, also emphasized laws and regulations’
importance to unravel big data privacy-preserving problem.
ACKNOWLEDGMENT
We thank all our Faculty members of our Department and
our classmates and other anonymous reviewers for their
valuable comments on our draft paper.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the
authors.
REFERENCES
[1] Thomas.H. davenport, How big data is important fall 2012,
https://www.hbs.edu/ris/Publication%20Files/SMR-How-Big-Data-
Is-Different_782ad61f-8e5f-4b1e-b79f-83f33c903455.pdf
[2] Challenges and opportunities with big data,
https://dl.acm.org/doi/abs/10.14778/2367502.2367572, Alexandros
Labrinidis, University of Pittsburgh, Pittsburgh, PA, HV V Jagadish,
University of Michigan, Ann Arbor, MI 2012
[3] Big Data. Nature,
http://www.nature.com/news/specials/bigdata/index.html, Sep 2008.
[4] Data, data everywhere. The Economist
http://www.economist.com/node/15557443, Feb 2010.
[5] D. Agrawal, P. Bernstein, E. Bertino, S. Davidson, U. Dayal, M.
Franklin, J. Gehrke, L. Haas, A. Halevy, J. Han, H. V. Jagadish, A.
Labroids, S. Madden, Y. Papakonstantinou, J. M. Patel, R.
Ramakrishnan, K. Ross, C. Shahabi, D. Suciu, S. Vaithyanathan, and
J. Widom. Challenges and Opportunities with Big Data -- A
community white paper developed by leading researchers across the
United States. http://cra.org/ccc/docs/init/bigdatawhitepaper pdf, Mar
2012.
[6] Trends in big data analytics journal,
https://www.sciencedirect.com/science/article/pii/S074373151400005
7 , July 2014