Knowledge Hiding in Decision Trees for Learning Analytics Applications

Abstract

Nowadays there is a wide range of digital information available to educational institutions regarding learners, including performance records, educational resources, student attendance, feedback on the course material, evaluations of courses and social network data. Although collecting, using, and sharing educational data do offer substantial potential, the privacy-sensitivity of the data raises legitimate privacy concerns. The sharing of data among education organizations has become an increasingly common procedure. However, any organization will most likely try to keep some patterns hidden if it must share its datasets with others. This chapter focuses on preserving the privacy of sensitive patterns when inducing decision trees and demonstrates the application of a heuristic to an educational data set. The employed heuristic hiding method allows the sanitized raw data to be readily available for public use and, thus, is preferable over other heuristic solutions, like output perturbation or cryptographic techniques, which limit the usability of the data.