Reasonable Setting Values for Anonymization Algorithms for Online Educational Data Analysis Support System
Osamu Takaki, Nobukuni Hamamoto, Atsuko Takefusa, Shigetoshi Yokoyama, Kento Aida · Procedia Computer Science · 2022
We propose a criterion of reasonable parameters for an algorithm that aggregates target data with anonymized data for safe data analysis in online educational systems, called learning management systems (LMSs). We also statistically investigate parameters that can satisfy the proposed criteria using an anonymization algorithm and real large-scale data. We use an open dataset containing one year's worth of product review data due to the difficulty of collecting LMS data large enough for the evaluation. Furthermore, we discuss an approach to address cases where no parameter satisfies the proposed criteria.