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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Takaki, Osamu Hamamoto, Nobukuni Takefusa, Atsuko Yokoyama, Shigetoshi Aida, Kento Gunma University 4-2 Aramaki-machi Maebashi371-8510 Japan National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo101-8430 Japan

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. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： anonymization differential privacy educational data analysis elasticsearch learning management system (LMS) local multilevel assignment of parameter values α-criterion

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

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Procedia Computer Science 2023年 225卷 3774-3784页

作者： Osamu Takaki Nobukuni Hamamoto Atsuko Takefusa Shigetoshi Yokoyama Kento Aida

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.

关键词： anonymization learning management system (LMS) educational data analysis differential privacy α-criterion local multilevel assignment of parameter values elasticsearch

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

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Procedia Computer Science 2022年 207卷 2556-2566页

作者： Osamu Takaki Nobukuni Hamamoto Atsuko Takefusa Shigetoshi Yokoyama Kento Aida Gunma University 4-2 Aramaki-machi Maebashi 371-8510 Japan National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo 101-8430 Japan

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.

关键词： anonymization learning management system (LMS) educational data analysis differential privacy Laplace mechanism α-criteria local multilevel assignment of parameter values

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