scholarly journals An Improved Security Threat Model for Big Data Life Cycle

2018 ◽  
Vol 7 (1) ◽  
pp. 33-39
Author(s):  
Kanika . ◽  
Alka . ◽  
R. A. Khan
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Social Networking Sites ◽  
Point Of View ◽  
Security And Privacy ◽  
Security Threat ◽  
Threat Model ◽  
Data Life Cycle ◽  
Security Challenges ◽  
Data Lifecycle

Big data is a huge amount of data created by individuals related to their medical, internet activity, social networking sites, energy usage communication patterns etc. From these sources, data is being collected and processed by various survey organizations, national statistical agencies, medical centres, and other companies etc. There are many security challenges which occur during data transactions, such as un-authentication, phishing, Vishing, data mining based attacks, etc. From a security point of view the biggest challenge for big data is the protection of user’s privacy. Yazan et.al, have presented big data lifecycle threat model. This paper does a critical review of the work. An Improved Security Threat Model for Big Data Life Cycle has been proposed as a main contribution of the paper. A new phase i.e. data creation phase has been added to the life cycle and it is claimed that the phase is very important one with respect to security and privacy. To justify the claim theoretical and statistical evidences have been provided.

scholarly journals Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020 ◽  
Vol 12 (24) ◽  
pp. 10571
Author(s):  
Jahoon Koo ◽  
Giluk Kang ◽  
Young-Gab Kim
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Big Data Analytics ◽  
Life Cycles ◽  
Security And Privacy ◽  
Personal Privacy ◽  
Security Issues ◽  
New Information ◽  
Data Life Cycle ◽  
Data Market

The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

scholarly journals Data Life Cycle Management in Big Data Analytics

2020 ◽  
Vol 173 ◽  
pp. 364-371
Author(s):  
Kumar Rahul ◽  
Rohitash Kumar Banyal
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Life Cycle Management ◽  
Data Life Cycle

Big Data information governance by accountants

2018 ◽  
Vol 26 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Emily M. Coyne ◽  
Joshua G. Coyne ◽  
Kenton B. Walker
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Design Methodology ◽  
Decision Makers ◽  
Accounting Profession ◽  
Content Type ◽  
Data Life Cycle ◽  
Information Governance ◽  
And Control ◽  
Strong Ability

Purpose Big Data has become increasingly important to multiple facets of the accounting profession, but accountants have little understanding of the steps necessary to convert Big Data into useful information. This limited understanding creates a gap between what accountants can do and what accountants should do to assist in Big Data information governance. The study aims to bridge this gap in two ways. Design/methodology/approach First, the study introduces a model of the Big Data life cycle to explain the process of converting Big Data into information. Knowledge of this life cycle is a first step toward enabling accountants to engage in Big Data information governance. Second, it highlights informational and control risks inherent to this life cycle, and identifies information governance activities and agents that can minimize these risks. Findings Because accountants have a strong ability to identify the informational and control needs of internal and external decision-makers, they should play a significant role in Big Data information governance. Originality/value This model of the Big Data life cycle and information governance provides a first attempt to formalize knowledge that accountants need in a new field of the accounting profession.

scholarly journals German-Russian Astroparticle Data Life Cycle Initiative to foster Big Data Infrastructure for Multi-Messenger Astronomy

2021 ◽  
Author(s):  
Victoria Tokareva ◽  
Igor Bychkov ◽  
Andrey Demichev ◽  
Julia Dubenskaya ◽  
Oleg Fedorov ◽  
...  
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Life Cycle Initiative ◽  
Data Infrastructure ◽  
Data Life Cycle

Security Threats for Big Data

2018 ◽  
Vol 10 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Nazrul Islam ◽  
Tarannum Zaki ◽  
Md. Sami Uddin ◽  
Md. Mahedi Hasan
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Modern Science ◽  
Security Threats ◽  
It Security ◽  
Security Threat ◽  
Email Communication ◽  
Security Challenges ◽  
Business Sectors ◽  
E Mail

With the advancement of modern science and technology, data emerging from different fields are escalating gradually. Recently, with this huge amount of data, Big Data has become a source of immense opportunities for large scale organizations related to various business sectors as well as to information technology (IT) professionals. Hence, one of the biggest challenges of this context is the security of this big set of data in different ICT based organizations. The fundamental objective of this article is to explore how big data may create security challenges in email communication. As an outcome, this article first shows that big data analysis helps to understand the behavior or interest of email users, which in turn can help phishers to create the phishing sites or emails that result in IT security threat; second, the article finds that phishing e-mail generation based on the (email) users' behavior can break an organization's IT security; and finally, a framework was proposed that would help to enhance the security of email communication.

Big Data Management System Security Threat Model

2019 ◽  
Vol 53 (8) ◽  
pp. 903-913
Author(s):  
M. A. Poltavtseva ◽  
D. P. Zegzhda ◽  
M. O. Kalinin
Keyword(s):  
Big Data ◽  
Data Management ◽  
Management System ◽  
System Security ◽  
Data Management System ◽  
Security Threat ◽  
Threat Model

scholarly journals The Model Design of Medical Data Life Cycle Based on Big Data Platform

2021 ◽  
Vol 1865 (4) ◽  
pp. 042088
Author(s):  
Ziqing Li ◽  
Shenglei Pei ◽  
Guiliang Feng
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Medical Data ◽  
Model Design ◽  
Data Life Cycle ◽  
Data Platform

scholarly journals A Cross-Domain Comparative Study of Big Data Architectures

2020 ◽  
Vol 29 (04) ◽  
pp. 2030001
Author(s):  
Martin Macak ◽  
Mouzhi Ge ◽  
Barbora Buhnova
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Smart Cities ◽  
Application Domain ◽  
Domain Specific ◽  
Cross Domain ◽  
Data Life Cycle ◽  
Data Architecture ◽  
Practical Guidelines ◽  
Similarities And Differences

Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

Security Integration in Big Data Life Cycle

2017 ◽  
pp. 192-200 ◽  
Author(s):  
Kanika ◽  
Alka Agrawal ◽  
R. A. Khan
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Life Cycle

Big Data Security

2015 ◽  
pp. 301-313 ◽  
Author(s):  
Fatima-Zahra Benjelloun ◽  
Ayoub Ait Lahcen
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security And Privacy ◽  
Economic Sectors ◽  
Data Intensive ◽  
New Information ◽  
Maximum Benefit ◽  
Security Challenges ◽  
Data Context ◽  
Privacy Issues

The value of Big Data is now being recognized by many industries and governments. The efficient mining of Big Data enables to improve the competitive advantage of companies and to add value for many social and economic sectors. In fact, important projects with huge investments were launched by several governments to extract the maximum benefit from Big Data. The private sector has also deployed important efforts to maximize profits and optimize resources. However, Big Data sharing brings new information security and privacy issues. Traditional technologies and methods are no longer appropriate and lack of performance when applied in Big Data context. This chapter presents Big Data security challenges and a state of the art in methods, mechanisms and solutions used to protect data-intensive information systems.

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