Kratos: A secure, authenticated and publicly verifiable system for educational data using the blockchain

Privacy-Driven Learning Analytics

“…Data privacy has been identified as one of the main concerns for learning analytics research and practice as well as advancing educational research in general (Ferguson, 2012;Selwyn, 2019). With recent developments in learning analytics and online learning, we are able to collect immense amounts of data about learners and the knowledge processes they engage with (Hillman & Ganesh, 2019). However, this also raises concerns regarding data ownership and privacy.…”

“…As the use of large-scale data in educational research became more prominent in recent years, there is an associated increase in concerns linked to the privacy of learner data and ethics of their collection for research and practice (Dawson et al, 2019). Early works in privacy and ethics, in educational research and learning analytics, primarily focus on developing policies and frameworks that would allow for exploiting data collected by various learning environments, while still respecting student privacy and building accountability into educational technology (Haythornthwaite, 2017;Hillman & Ganesh, 2019;Reidenberg & Schaub, 2018). Regardless of how the educational landscape evolves, privacy safeguards are necessary to build end-user trust and enable the broader adoption of learning analytics into practice (Reidenberg & Schaub, 2018).…”

“…Several approaches have been proposed recently as feasible solutions to the pressing need for rethinking how learning analytics systems and practices are designed with privacy requirements in mind (Hoel et al, 2017). The existing approaches apply different models for protecting learners privacy, from providing platform-as-aservice architecture (Gardner et al, 2018) and utilising blockchain publicly verifiable system for educational data (Hillman & Ganesh, 2019), to the application of various anonymization and provably private mechanisms (Gursoy et al, 2017). Some examples of the former two approaches are discussed below, while they provide some benefits they do not formally define the offered privacy guarantee.…”

“…As Hillman and Ganesh (2019) further argue, despite the existence of open educational data standards, there is necessarily no incentive for vendors in K-12 space to adopt any of those. Moreover, data privacy terms and policies are not always clear (Hillman & Ganesh, 2019;Kelly et al, 2019). The solution proposed by Hillman and Ganesh (2019) relies on a defined set of smart contracts, based on the existing agreements between schools and technology vendors.…”

“Block Chain in Privacypreservation of Records in Education -a Current Research Trend”

“…• In [3], locality-sensitive hashing (LSH) technique is adapted for attaining the goal of privacy preservation. However, the classical LSH-based recommendation method suffered from less accuracy wherein the quality of service varies in big range.…”