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

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 Survey of Security and Privacy Issues in Big Data Analytics

2019 ◽  
Vol 8 (S1) ◽  
pp. 33-35
Author(s):  
G. A. Mylavathi ◽  
N. M. Mallika ◽  
K. Mohanraj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Security And Privacy ◽  
Network Services ◽  
Privacy And Security ◽  
Security Issues ◽  
Massive Information ◽  
Privacy Issues

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost each trade is making an attempt to address this large information. Big data phenomenon has begun to gain importance. However, it’s not solely terribly tough to store massive information and analyses them with ancient applications, however conjointly it’s difficult privacy and security issues. For this reason, this paper discusses the massive information, its scheme, considerations on massive information and presents comparative read of massive information privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications associate overall perspective of security and privacy problems in massive information is usually recommended

Big Data

2020 ◽  
pp. 24-65
Author(s):  
P. Lalitha Surya Kumari
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Collection ◽  
Operating Systems ◽  
Data Security ◽  
Map Reduce ◽  
Security Issues ◽  
Big Data Applications

This chapter gives information about the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by big data applications. Big data is one area where we can store, extract, and process a large amount of data. All these data are very often unstructured. Using big data, security functions are required to work over the heterogeneous composition of diverse hardware, operating systems, and network domains. A clearly defined security boundary like firewalls and demilitarized zones (DMZs), conventional security solutions, are not effective for big data as it expands with the help of public clouds. This chapter discusses the different concepts like characteristics, risks, life cycle, and data collection of big data, map reduce components, issues and challenges in big data, cloud secure alliance, approaches to solve security issues, introduction of cybercrime, YARN, and Hadoop components.

Impact of Big Data on Security

2018 ◽  
pp. 326-350
Author(s):  
Kasarapu Ramani
Keyword(s):  
Big Data ◽  
Defense Mechanisms ◽  
Security And Privacy ◽  
End User ◽  
Sensitive Data ◽  
Insider Threats ◽  
Security Issues ◽  
Commercial Importance ◽  
Multi Level ◽  
Access Privileges

Big data has great commercial importance to major businesses, but security and privacy challenges are also daunting this storage, processing, and communication. Big data encapsulate organizations' most important and sensitive data with multi-level complex implementation. The challenge for any organization is securing access to the data while allowing end user to extract valuable insights. Unregulated access privileges to the big data leads to loss or theft of valuable and sensitive. Privilege escalation leads to insider threats. Also, the computing architecture of big data is not focusing on session recording; therefore, it is becoming a challenge to identify potential security issues and to take remedial and mitigation mechanisms. Therefore, various big data security issues and their defense mechanisms are discussed in this chapter.

Public Administration Curriculum-Based Big Data Policy-Analytic Epistemology

2019 ◽  
pp. 467-488
Author(s):  
Emmanuel N. A. Tetteh
Keyword(s):  
Big Data ◽  
Action Learning ◽  
Real Life ◽  
Big Data Analytics ◽  
Social Challenges ◽  
Political Policy ◽  
New Information ◽  
Data Policy ◽  
The Internet Of Things

The equilibration that underscores the internet of things (IoT) and big data analytics (BDA) cannot be underestimated at the behest of real-life social challenges and significant policy data generated to redress the concerns of epistemic communities, such as political policy actors, stakeholders, and the citizenry. The cognitive balancing of new information gathered by BDA and assimilated across the IoT is at the crossroads of ascertaining how the growing increases of such BDA can be better managed to transition from the big data state of disequilibration to reach a more stable equilibrium of policy data usefulness. In the quest for explicating the equilibration of policy data usefulness, an account of the curriculum-based MPA policy analysis and analytics concentration program at Norwich University is described as a case example of big data policy-analytic epistemology. The case study offers a symbolic ideology of an IoT action-learning solution model as a recommendation for fostering the stable equilibration of policy data usefulness.

Importance of Big Data and Hadoop in E-Servicing

2018 ◽  
pp. 39-52
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Importance of Big Data and Hadoop in E-Servicing

2022 ◽  
pp. 1634-1644
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

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.

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