{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00086648","sets":["6164:6165:6462:6909"]},"path":["6909"],"owner":"11","recid":"86648","title":["プライバシー保護決定木学習におけるエントロピーを近似する順序同型関数"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-10-23"},"_buckets":{"deposit":"969f0082-794e-4a29-bf14-e3ce8e88720f"},"_deposit":{"id":"86648","pid":{"type":"depid","value":"86648","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"プライバシー保護決定木学習におけるエントロピーを近似する順序同型関数","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"プライバシー保護決定木学習におけるエントロピーを近似する順序同型関数"},{"subitem_title":"Order-Isomorphism Function for approximate entropy in Privacy-Preserving Decision Tree Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"privacy，Data Mining，Decision Tree，Similarity","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2012-10-23","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東海大学"},{"subitem_text_value":"富士通研究所"},{"subitem_text_value":"富士通研究所"},{"subitem_text_value":"富士通研究所"},{"subitem_text_value":"富士通研究所"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokai University","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Laboratories Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Laboratories Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Laboratories Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu Laboratories Ltd.","subitem_text_language":"en"}]},"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/86648/files/IPSJCSS2012013.pdf"},"date":[{"dateType":"Available","dateValue":"2014-10-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2012013.pdf","filesize":[{"value":"107.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4325705f-69a6-4d1e-a35b-719acd8ad02d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"菊池, 浩明"},{"creatorName":"伊藤, 孝一"},{"creatorName":"牛田, 芽生恵"},{"creatorName":"津田, 宏"},{"creatorName":"山岡, 裕司"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroaki, Kikuchi","creatorNameLang":"en"},{"creatorName":"Kouichi, Ito","creatorNameLang":"en"},{"creatorName":"Mebae, Ushida","creatorNameLang":"en"},{"creatorName":"Hiroshi, Tsuda","creatorNameLang":"en"},{"creatorName":"Yuji, Yamaoka","creatorNameLang":"en"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"プライバシー保護決定木学習では，機密性のあるデータセットを持つ複数の組織が互いsの値を秘匿した方法で，最適な識別子を選択するエントロピー利得を求める．しかし，この計算には大きな計算量を要する．そこで，この研究では，新たなエントロピー関数と順序同型な関数を最大値と最小値で定義し，それによる計算量削減を試みる．公開データセットにおける評価を報告する．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Privacy-preserving decision tree learning protocol allow multiple parties with confidential datasets to jointly perform entropy gain to choose the best classifier in privacy-preserving way. The entropy function requires huge computational overhead to preform. Hence, in this study, a new order-isomorphism function is defined using simple max and min that are less intensive in computation. The evaluation with public datasets will be reported.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"97","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2012論文集"}],"bibliographicPageStart":"92","bibliographicIssueDates":{"bibliographicIssueDate":"2012-10-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2012"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":86648,"updated":"2025-01-21T17:34:59.166672+00:00","links":{},"created":"2025-01-18T23:37:39.795193+00:00"}