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A Review on Security and Privacy Challenges of Big Data

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Cognitive Computing for Big Data Systems Over IoT

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 14 ))

Abstract

Big data has a growing number of confidentiality and security issues. New technology doubtlessly brings people benefits, privileges, convenience and efficiencies, with confidentiality issues. Additionally, technological advances are accompanied with threats that can pose dangerous privacy risks that can be more detrimental than expected. Privacy of data is a source of much concern to researchers throughout the globe. A question that remains unanswered is the question that “what exactly can be done” to resolve confidentially and privacy issues of big data? To answer some questions, data collected for this chapter has been through the analyze of 58 peer reviewed articles collated from 2007 to 2016 in order to find some resolutions for Big Data confidentiality issues. The articles range from different industries that include healthcare , finance , robotics, web applications , social media , and mobile communication . The selected journal articles are aimed used to make comparative analysis of security issues in different areas to cast solutions. This chapter consists of four main parts: introduction, materials and method, results, discussion and conclusion. This inquiry aimed to find different security issues of big data in various areas and gives solutions by analyzing results. The results of the content analysis suggest that the internet applications and financial institutions are dealing with specific security problems, whereas social media and other industries deal with confidentiality issues of sensitive information which have heightened privacy concerns. Both these issues are addressed in this study, as retrieved results from the data, highlights the gaps that can be further researched for development. The method used to gather data for this chapter is through the analysis of studies that deal with a particular confidentiality issue. After the analysis and evaluations, the suggestions of confidentiality issues are displayed by using a different algorithm method. This research has addressed gaps in the literature by highlighting security and privacy issues that big companies face with recent technological advancements in corporate societies. By doing this, the research could shed revolutionary light on issues of big data and provide futuristic research directions to solve them.

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Acknowledgements

The authors acknowledge the data collection support provided by Mariia Talalaeva.

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Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Melbourne, VIC, 3000, Australia

    Manbir Singh, Malka N. Halgamuge, Gullu Ekici & Charitha S. Jayasekara

Authors
  1. Manbir Singh
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  2. Malka N. Halgamuge
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  3. Gullu Ekici
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  4. Charitha S. Jayasekara
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Contributions

Manbir Singh and Malka N. Halgamuge conceived the study idea and developed the analysis plan. Manbir Singh analyzed the data and wrote the initial chapter. Malka N. Halgamuge helped to prepare the figures and tables, and finalizing the manuscript. All authors read the manuscript.

Corresponding author

Correspondence to Malka N. Halgamuge .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    Arun Kumar Sangaiah

  2. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    Arunkumar Thangavelu

  3. Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Mangalore, Karnataka, India

    Venkatesan Meenakshi Sundaram

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Singh, M., Halgamuge, M.N., Ekici, G., Jayasekara, C.S. (2018). A Review on Security and Privacy Challenges of Big Data. In: Sangaiah, A., Thangavelu, A., Meenakshi Sundaram, V. (eds) Cognitive Computing for Big Data Systems Over IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 14 . Springer, Cham. https://doi.org/10.1007/978-3-319-70688-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-70688-7_8

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  • Print ISBN: 978-3-319-70687-0

  • Online ISBN: 978-3-319-70688-7

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