Reasonable Setting Values for Anonymization Algorithms for Online Educational Data Analysis Support System

• DOI: 10.1016/j.procs.2022.09.314
• Publication Date: 2022-10-19T04:10:45Z
• Authors: Osamu Takaki; Nobukuni Hamamoto; Atsuko Takefusa; Shigetoshi Yokoyama; Kento Aida
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 0101 mathematics; 01 natural sciences

We propose a criterion of reasonable parameters for an algorithm that aggregates target data with anonymized safe analysis in online educational systems, called learning management systems (LMSs). also statistically investigate can satisfy the proposed criteria using anonymization and real large-scale data. use open dataset containing one year's worth product review due to difficulty collecting LMS large enough evaluation. Furthermore, we discuss approach address cases where no parameter satisfies criteria.