A survey on security and privacy issues in big data

Terzi, Duygu Sinanç; Terzı, Ramazan; Sağıroğlu, Şeref

doi:10.1109/icitst.2015.7412089

Cited by 121 publications

(62 citation statements)

References 17 publications

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“…Big data in a Hadoop contains sensitive information related to financial data, corporate information, personal information and many such confidential data of clients, customers and employees. Hence, optimum protection of such data and ensuring that it remains free from any encroachment or tampering is of utmost significance [6][7][8][9][10][11].…”

Section: Related Work A) Hadoop Securitymentioning

confidence: 99%

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

2020

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Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user's information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.

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Section: Related Work A) Hadoop Securitymentioning

confidence: 99%

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

2020

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“…Work-based on Data Security is discussed in this section. According to the Terzi [32] work, network traffic should be expressed in code with suitable standards, access to devices should be checked, employees should be authorized to access systems, analysis should be done on anonymized data sending and receiving should be made for the secure channel to prevent data drip, and network should be observed for threats. Kacha [33] work describes principal issues related to data security that is raised by cloud environment are classified into three categories: (1) data security issues raised by single cloud characteristics, (2) data security issues raised by data lifecycle in cloud computing, (3) data security issues associated with data security attributes (confidentiality, integrity, and availability).…”

Section: Data Securitymentioning

confidence: 99%

Enhanced Secured Map Reduce layer for Big Data privacy and security

2019

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Data security, according to the common definition is confidentiality, integrity, and availability of data. It is the act of guaranteeing that the information is safe from unauthorized access, ensures that the information is reliable and accurate which is accessible whenever it is required. An information security design incorporates features, for example, gathering the required data, protecting it, and obliterating any data that is never again required [1]. Privacy, on the other hand, is the appropriate use of information. In other words, merchants and companies should use the data provided to them only for the intended purpose. For example, if an individual purchase a product from XYZ Company and provides them with their personal information like address, card number, etc. then this company cannot sell that information to the third party. Companies need to enact a data security policy for the sole purpose of ensuring data privacy of their consumer personal information. Moreover, companies must ensure data privacy because the information is an asset to the company. However, no data security policy can overcome the

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“…Additionally, data can be manipulated and misinterpreted according to the predispositions of those who are handling and manipulating data for making predictive intelligence (Katwala, 2018;Terzi, Terzi, & Sagiroglu, 2015). An example of such bias is given by Hacker (2018) regarding the use of SIS in predictive medical intelligence.…”

Section: The Wonga Case: Use Of Leaky Datamentioning

confidence: 99%

Ethical Implications of Predictive Risk Intelligence

Jiya

2019

The ORBIT Journal

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This paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. Thepaper covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews.Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelligence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as social media sites. Also, there are issues relating to the transparency and accountability of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In response to these issues,the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Responsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees.This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society.ORBIT Journal DOI: https://doi.org/10.29297/orbit.v2i2.112 2

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A survey on security and privacy issues in big data

Cited by 121 publications

References 17 publications

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

Peer Review #2 of "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective (v0.1)"

Enhanced Secured Map Reduce layer for Big Data privacy and security

Ethical Implications of Predictive Risk Intelligence

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