{"updated":"2025-01-19T11:12:17.276700+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230306","sets":["6504:11436:11441"]},"path":["11441"],"owner":"44499","recid":"230306","title":["機密性の高いデータの使用を可能にするリッチデバイスを用いた分散機械学習の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"c101bd18-326b-490e-bc50-3e13c976c98f"},"_deposit":{"id":"230306","pid":{"type":"depid","value":"230306","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機密性の高いデータの使用を可能にするリッチデバイスを用いた分散機械学習の検討","author_link":["620353","620354","620355","620356"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機密性の高いデータの使用を可能にするリッチデバイスを用いた分散機械学習の検討"}]},"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":"2023-02-16","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/230306/files/IPSJ-Z85-1Y-02.pdf","label":"IPSJ-Z85-1Y-02.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1Y-02.pdf","filesize":[{"value":"606.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"a23517d4-1440-4a88-b9ac-28883d3074fe","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 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":"従来の分散機械学習分野の研究では，高性能なサーバが全てのデータを管理するものが多く，個人情報を外部のサーバへ送信することによる情報漏洩の危険性がある．この課題に対し，我々はデバイス上の個人情報を外部へと一切送信しないプライバシ保護に優れた分散機械学習モデルを検討する．本論文では，エッジサーバでの学習をエッジデバイスに継承させ，ユーザの許可を得た結果のみをエッジサーバにフィードバックする提案モデルを，エッジデバイスとしてJetson Nanoを用いて実装した．その結果，エッジデバイス上では短時間で個人情報に関する知見を得られ，エッジサーバ上ではより精度の高い結果を安全に得ることが可能であった．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"74","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"73","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:29:59.128855+00:00","id":230306,"links":{}}