scholarly journals The Impact of Big Data on People and Data Security Issues

2021 ◽  
Author(s):  
Xiaomeng Li
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Issues ◽  
The Impact

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.

Cryptography in Big Data Security

2018 ◽  
pp. 71-94
Author(s):  
Navin Jambhekar ◽  
Chitra Anil Dhawale
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Data Security ◽  
Computing System ◽  
Mobile Client ◽  
Security Issues ◽  
Cloud Computing System ◽  
Cloud Server ◽  
Encrypted Search

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

Data Security for Connected Governments and Organisations

2021 ◽  
pp. 229-251
Author(s):  
Heru Susanto ◽  
Leu Fang Yie ◽  
Didi Rosiyadi ◽  
Akbari Indra Basuki ◽  
Desi Setiana
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Data Security ◽  
Data Privacy ◽  
Government Agencies ◽  
Security Measures ◽  
Security Issues ◽  
The Impact ◽  
Distributed Technologies

Digital ecosystems have grown rapidly over the years, and governments are investing in digital provision for their processes and services. Despite the advantages of distributed technologies, there are many security issues as well that result in breaches of data privacy with serious impact including legal and reputational implications. To deal with such threats, government agencies need to thoughtfully improve their security defences to protect data and systems by using automation and artificial intelligence (AI), as well as easing the data security measures including early warning of threats and detection. This study provides a comprehensive view of AI and automaton to highlight challenges and issues concerning data security and suggests steps to combat the issues. The authors demonstrate the role of AI-driven security tools and automation to mitigate the impact of data breaches to also propose recommendations for government agencies to enhance their data security protection.

Network Big Data Security Issues

2019 ◽  
pp. 1178-1184
Author(s):  
Niranjan K. Ray ◽  
Biswaranjan Acharya ◽  
Anil Kumar Swain
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Issues ◽  
Network Big Data

scholarly journals Big Data Security Issues and Challenges

2021 ◽  
Vol 15 (2) ◽  
pp. 28-36
Author(s):  
Shahab KAREEM ◽  
Aesha HASAN ◽  
Roojwan HAWEZI ◽  
Karwan MUHEDEN ◽  
Farah KHOSHABA
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Issues

scholarly journals Authorization of Data In Hadoop Using Apache Sentry

2018 ◽  
Vol 7 (3.6) ◽  
pp. 234 ◽  
Author(s):  
N Sirisha ◽  
K V.D. Kiran
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Encryption ◽  
Security Model ◽  
Huge Amount ◽  
Sensitive Data ◽  
Major Drawback ◽  
Security Issues ◽  
On Demand ◽  
Increasing Trend

Big Data has become more popular, as it can provide on-demand, reliable and flexible services to users such as storage and its processing. The data security has become a major issue in the Big data. The open source HDFS software is used to store huge amount of data with high throughput and fault tolerance and Map Reduce is used for its computations and processing. However, it is a significant target in the Hadoop system, security model was not designed and became the major drawback of Hadoop software. In terms of storage, meta data security, sensitive data  and also the data security will be an serious issue in HDFS. With the importance of Hadoop in today's enterprises, there is also an increasing trend in providing a high security features in enterprises. Over recent years, only some level of security in Hadoop such as Kerberos and Transparent Data Encryption(TDE),Encryption techniques, hash techniques are shown for Hadoop. This paper, shows the efforts that are made to present Hadoop Authorization security issues using Apache Sentry in HDFS. 

Data Security for Connected Governments and Organisations

2022 ◽  
pp. 191-213
Author(s):  
Heru Susanto ◽  
Leu Fang Yie ◽  
Didi Rosiyadi ◽  
Akbari Indra Basuki ◽  
Desi Setiana
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Data Security ◽  
Data Privacy ◽  
Government Agencies ◽  
Security Measures ◽  
Security Issues ◽  
The Impact ◽  
Distributed Technologies

Digital ecosystems have grown rapidly over the years, and governments are investing in digital provision for their processes and services. Despite the advantages of distributed technologies, there are many security issues as well that result in breaches of data privacy with serious impact including legal and reputational implications. To deal with such threats, government agencies need to thoughtfully improve their security defences to protect data and systems by using automation and artificial intelligence (AI), as well as easing the data security measures including early warning of threats and detection. This study provides a comprehensive view of AI and automaton to highlight challenges and issues concerning data security and suggests steps to combat the issues. The authors demonstrate the role of AI-driven security tools and automation to mitigate the impact of data breaches to also propose recommendations for government agencies to enhance their data security protection.

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