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

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.