SENSITIVITY OF LEARNERS’ PRIVACY DATA (LPD) IN MOBILE LEARNING SYSTEM: A FUZZY ANALYTIC HIERARCHY SCHEME (FAHS) SOLUTION

Abstract

Mobile technologies give room for possibilities of regular monitoring of learner’s behaviour in order to establish proper user privacy protection. In educational system, safeguarding and free flow of administering of learners’ privacy protection is key factor in learners’ location and personal data. Learner's preferences, goals are important to achieve assessment by teachers’ and smooth relationship among learners and create compromised preserving learners’ privacy. To this end, learners’ sensitive data in the cloud big data are exposed to sub-consciousness, stalking and theft. Therefore, the article addresses the issues of sensitivity among the learners’ sensitive attributes such as personal and mobile devices data that enrolled in Mobile Learning System. However, attributes sensitivity solution using Fuzzy Analytical Hierarchy Schemes are being explored for the use of learners’ profile due to the real danger from the Internet usage. Hence, concerns about sensitivity of learners’ privacy data motivated this paper to adopt attributes partitioning strategy into sensitive and non-sensitive attributes ranging from 1 to 5 enforce privacy during learner profile information. Comparison between learners’ data and mobile devices, shows that medical records as learners’ data has FAHS weight of 0.9940 and APH weight of 0.0811 with highest sensitivity of 5 as most sensitive learners’ private data. While browsing history as mobile devices has FAHS weight of 0.7861 and APH weight of 0.1471 with highest sensitivity of 5 as most sensitive mobile device. This implies that, these most/highest sensitive data/devices are vulnerable and must be protected to avoid privacy breaches, stalking, abuses, theft, sub-consciousness, harassments, and undue advantages of learners. In future works, preserving the privacy of sensitive MLS learners’ privacy data sensitivity can be performed in a permissioned blockchain environment of Ethereum platform.