A Review on Big Data: Privacy and Security Challenges

2021 ◽  
Author(s):  
Parth Goel ◽  
Radhika Patel ◽  
Dweepna Garg ◽  
Amit Ganatra
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy And Security ◽  
Security Challenges ◽  
Big Data Privacy

Security and Privacy in Big Data Computing

2020 ◽  
pp. 236-256
Author(s):  
Kiritkumar J. Modi ◽  
Prachi Devangbhai Shah ◽  
Zalak Prajapati
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Cloud Data ◽  
Big Data Privacy ◽  
Cloud Data Storage ◽  
Type Data ◽  
Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

A Study of the Cryptographic Technologies on Big Data Privacy and Security in E-Commerce

2019 ◽  
Vol 16 (8) ◽  
pp. 3587-3590
Author(s):  
Raheem Mafas ◽  
Manoj Jayabalan
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
State Of The Art ◽  
Not Given ◽  
Privacy And Security ◽  
Comprehensive Review ◽  
Goods And Services ◽  
Big Data Privacy ◽  
Reasonable Cost ◽  
Challenges And Opportunities

In this era, big data is the most common buzzword across different industries due to its capabilities of collecting, processing, storing and analysing data. The advancement of the E-Commerce paved the way for merchants and customers to meet online to satisfy their requirements by exchanging goods and services at a reasonable cost. The challenges and opportunities for big data on the emphasis of data privacy and security is a widely discussed topic among businesses especially E-Commerce merchants. There are several reviews available on emphasizing big data opportunities and challenges with regard to privacy and security. However, a comprehensive review on E-Commerce highlighting thematically on the tools and technologies is not given enough consideration. Therefore, the purpose of this study is to review the state-of-the-art technologies towards privacy and security in the E-Commerce platforms. The identified cryptographic technologies were also discussed with the rational standpoint to understand the viability to apply in the E-Commerce operations. The study concludes with an enlightening path from which the E-Commerce merchants can be vigilant on data privacy and security in future.

scholarly journals Review: Big Data Privacy and Security Risk and Solutions

2021 ◽  
pp. 85-94
Author(s):  
Madhavi Tota
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
High Efficiency ◽  
Data Access ◽  
Database Systems ◽  
Mobile Internet ◽  
Security Measures ◽  
Security Risk ◽  
Privacy And Security ◽  
Big Data Privacy

Big Data is very dynamic issues in the current year, enables computing resources as a data to be provided as Information Technology services with high efficiency and effectiveness. The high amount of data in world is growing day by day. Data is growing very rapidly because of use of internet, smart phone and social network. Now size of the data is in Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyze this large amount of data. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the limits. However, the current scenario the growth rate of such large data creates number of challenges, such as the fast growth of data, access speed, diverse data, and security. This paper shows the fundamental concepts of Big Data. Privacy threats and security methods used in Big Data. With the development of various research application and recourses of Internet/Mobile Internet, social networks, Internet of Things, big data has become the very important topic of research across the world, at the same time, big data has security risks and privacy protection during different stages such as collecting, storing, analyzing and utilizing. This paper introduces security measures of big data, then proposes the technology to solve the security threats.

scholarly journals Enhanced Secured Map Reduce layer for Big Data privacy and security

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Priyank Jain ◽  
Manasi Gyanchandani ◽  
Nilay Khare
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Map Reduce ◽  
Privacy And Security ◽  
Big Data Privacy

Security and Privacy in Big Data Computing

2021 ◽  
pp. 287-303
Author(s):  
Kiritkumar J. Modi ◽  
Prachi Devangbhai Shah ◽  
Zalak Prajapati
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Security And Privacy ◽  
Comprehensive Overview ◽  
Privacy And Security ◽  
Cloud Data ◽  
Big Data Privacy ◽  
Cloud Data Storage ◽  
Type Data ◽  
Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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