Security and privacy in bigdata learning analytics: An affordable and modular solution

Jeremie Seanosky, Daniel Jacques, Vive Kumar, Kinshuk

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

4 Citations (Scopus)

Abstract

In a growing world of bigdata learning analytics, tremendous quantities of data streams are collected and analyzed by various analytics solutions. These data are crucial in providing the most accurate and reliable analysis results, but at the same time they constitute a risk and challenge from a security standpoint. As fire needs fuel to burn, so do hacking attacks need data in order to be “successful”. Data is the fuel for hackers, and as we protect wood from fire sources, so do we need to protect data from hackers. Learning analytics is all about data. This paper discusses a modular, affordable security model that can be implemented in any learning analytics platform to provide total privacy of learners’ data through encryption mechanisms and security policies and principles at the network level.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges, ISBCC 2016
EditorsV. Vijayakumar, V. Neelanarayanan
Pages43-55
Number of pages13
DOIs
Publication statusPublished - 2016
Event3rd International Symposium on Big Data and Cloud Computing, ISBCC 2016 - Chennai, India
Duration: 10 Mar. 201611 Mar. 2016

Publication series

NameSmart Innovation, Systems and Technologies
Volume49
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference3rd International Symposium on Big Data and Cloud Computing, ISBCC 2016
Country/TerritoryIndia
CityChennai
Period10/03/1611/03/16

Keywords

  • Analytics
  • Bigdata
  • Learning analytics
  • Privacy
  • Security

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