{"updated":"2025-01-19T14:13:19.641537+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00221288","sets":["6504:11035:11042"]},"path":["11042"],"owner":"44499","recid":"221288","title":["プライバシ保護が必要な個人データに対応した分散機械学習モデルの検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-17"},"_buckets":{"deposit":"9eca351b-8fb8-44c2-8dfb-8d9512bf654b"},"_deposit":{"id":"221288","pid":{"type":"depid","value":"221288","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"プライバシ保護が必要な個人データに対応した分散機械学習モデルの検討","author_link":["579273","579274","579275","579272"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"プライバシ保護が必要な個人データに対応した分散機械学習モデルの検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2022-02-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大"},{"subitem_text_value":"東大"},{"subitem_text_value":"工学院大"},{"subitem_text_value":"お茶の水女子大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/221288/files/IPSJ-Z84-1ZA-02.pdf","label":"IPSJ-Z84-1ZA-02.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-22"}],"format":"application/pdf","filename":"IPSJ-Z84-1ZA-02.pdf","filesize":[{"value":"494.1 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"d3f6be8e-2193-400a-a444-dfeda2663b69","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高野, 紗輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中尾, 彰宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 実靖"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，federated learningなどデバイス上にある個人情報を保護しながらそれらのデータをサーバ上での機械学習に用いることが盛んに研究されている．しかし，プライバシ保護が十分であるとはいえず，機密性が高くデバイスの外へ情報を持ち出したくない個人データを学習に用いることができない．本研究ではエッジサーバと連携しつつデバイス上でも機械学習を動かすリッチクライアントに適した分散機械学習モデルの検討を行う．本稿では，エッジサーバ上で一般的なデータを用いて学習した結果をデバイスで引き継ぐ学習モデルを提案する．Jetson Nanoを用いた顔画像認識を行った結果，提案モデルを用いることで機密性の高いデータも含めた学習が可能となることを確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"232","bibliographic_titles":[{"bibliographic_title":"第84回全国大会講演論文集"}],"bibliographicPageStart":"231","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:21:17.736395+00:00","id":221288,"links":{}}