Implementation of Anonymization Algorithms for Log Data Analysis on a Cloud-Based Learning Management System

Abstract

In this study, we aimed to construct a system to perform statistical analyses for educational improvement while preserving privacy. Various probabilistic statistical algorithms have been developed for data agitation. However, one of the major challenges in using these algorithms is determining appropriate parameters. To address this issue, our previous research proposed the “α-criterion” as a criterion that must be satisfied by appropriate parameters. This paper outlines a system that calculates the parameters that satisfy the α-criterion while performing real-time data agitation on the given aggregate data. To verify that our implementation actually works, we show a use case with sample data on kibana-Elastic log analysis provided by Elastic N. V.