{"created":"2025-01-19T01:45:10.248353+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240784","sets":["6164:6165:6462:11854"]},"path":["11854"],"owner":"11","recid":"240784","title":["差分プライバシーを保証したモデル説明DPGD-Explainに対するレコード再構築リスクの実験評価"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-10-15"},"_buckets":{"deposit":"3663365c-d086-40e7-9f8d-328bb8ae5363"},"_deposit":{"id":"240784","pid":{"type":"depid","value":"240784","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"差分プライバシーを保証したモデル説明DPGD-Explainに対するレコード再構築リスクの実験評価","author_link":["661264","661265","661266","661267"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"差分プライバシーを保証したモデル説明DPGD-Explainに対するレコード再構築リスクの実験評価","subitem_title_language":"ja"},{"subitem_title":"Empirical Evaluation of Record Reconstruction Risk from DPGD-Explain Model Explanations with Differential Privacy","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"説明可能性，差分プライバシー，レコード再構築攻撃，DPGD-Explain","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2024-10-15","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":"明治大学大学院先端数理科学研究科"},{"subitem_text_value":"明治大学大学院先端数理科学研究科"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Advanced Mathematical Sciences, Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Advanced Mathematical Sciences, Meiji University","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/240784/files/IPSJ-CSS2024038.pdf","label":"IPSJ-CSS2024038.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2024038.pdf","filesize":[{"value":"695.0 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":"db668c1b-93c7-481a-aad6-99ce7f6c4cdb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"當麻, 僚太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菊池, 浩明"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ryotaro, Toma","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroaki, Kikuchi","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":"機械学習モデルの公平性や学習の透明性を保証し，ユーザに納得感を与えるために機械学習モデルの出力を説明する説明可能性技術が注目されている．機械学習モデルを用いたサービスの多くはMachine Learning as a Service（MLaaS）と呼ばれるプラットフォーム上で提供されており，これらのMLaaSプラットフォームでは，モデルの出力に加えて，モデルを説明するいくつかの指標を提供している． 2022年にPatelらは差分プライバシーを保証した説明可能性指標DPGD-Explainを提案している．しかし，モデル説明可能性値を学習することにより，学習データの値を推測するレコードを再構築されるリスクが疑われる．そこで，本研究では，DPGD-Explainに対するレコード再構築リスクを調べ，プライバシー予算や説明の質と安全性との関係を明らかにする．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with a sense of understanding.  Most services for machine learning models are offered in a style of Machine Learning as a Service (MLaaS) platforms, which provide several methods to explain model outputs. Patel et al. (2022) proposed DPGD-Explain, model explanations with differential privacy.  Nevertheless, it remains unclear how model explanations with guarantee of differential privacy is vulnerable against the record reconstruction attack that trains the behavior between input data and the model explanations.  In this study, we investigate the record reconstruction risk of DPGD-Explain in terms of the privacy budget and the quality. ","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"280","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2024論文集"}],"bibliographicPageStart":"274","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":240784,"updated":"2025-03-06T05:10:43.132303+00:00","links":{}}