The Dark Side of Big Data: Personal Privacy, Data Security, and Price Discrimination

Author(s):  
Yang Liu ◽  
Connor Greene
2018 ◽  
Vol 26 (3) ◽  
pp. 463-482 ◽  
Author(s):  
Matteo La Torre ◽  
John Dumay ◽  
Michele Antonio Rea

PurposeReflecting on Big Data’s assumed benefits, this study aims to identify the risks and challenges of data security underpinning Big Data’s socio-economic value and intellectual capital (IC).Design/methodology/approachThe study reviews academic literature, professional documents and public information to provide insights, critique and projections for IC and Big Data research and practice.FindingsThe “voracity” for data represents a further “V” of Big Data, which results in a continuous hunt for data beyond legal and ethical boundaries. Cybercrimes, data security breaches and privacy violations reflect voracity and represent the dark side of the Big Data ecosystem. Losing the confidentiality, integrity or availability of data because of a data security breach poses threat to IC and value creation. Thus, cyberthreats compromise the social value of Big Data, impacting on stakeholders’ and society’s interests.Research limitations/implicationsBecause of the interpretative nature of this study, other researchers may not draw the same conclusions from the evidence provided. It leaves some open questions for a wide research agenda about the societal, ethical and managerial implications of Big Data.Originality/valueThis paper introduces the risks of data security and the challenges of Big Data to stimulate new research paths for IC and accounting research.


2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.


2019 ◽  
Vol 6 (2) ◽  
pp. 1628-1635 ◽  
Author(s):  
Karen R. Sollins

2018 ◽  
Vol 1 (4) ◽  
pp. e13 ◽  
Author(s):  
Rongxin Bao ◽  
Zhikui Chen ◽  
Mohammad S. Obaidat

Big Data ◽  
2021 ◽  
Author(s):  
R. Thenmozhi ◽  
S. Shridevi ◽  
Vicente García Díaz ◽  
Deepak Gupta ◽  
Prayag Tiwari ◽  
...  

Author(s):  
Abir EL. Azzaoui ◽  
Pradip Kumar Sharma ◽  
Jong Hyuk Park

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