De-identification is Insufficient to Protect Student Privacy, or – What Can a Field Trip Reveal?
Elad Yacobson, Orly Fuhrman, Sara Hershkovitz, Giora Alexandron
Abstract
Learning analytics have the potential to improve teaching and learning in K–12 education, but as student data is increasingly being collected and transferred for the purpose of analysis, it is important to take measures that will protect student privacy. A common approach to achieve this goal is the de-identification of the data, meaning the removal of personal details that can reveal student identity. However, as we demonstrate, de-identification alone is not a complete solution. We show how we can discover sensitive information about students by linking de-identified datasets with publicly available school data, using unsupervised machine learning techniques. This underlines that de-identification alone is insufficient if we wish to further learning analytics in K–12 without compromising student privacy.