{"created":"2025-01-19T01:04:46.051153+00:00","updated":"2025-01-19T21:00:09.408278+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00201505","sets":["6164:6165:6462:10022"]},"path":["10022"],"owner":"44499","recid":"201505","title":["r-VAE: VAEへの再構成誤差の取り込みと時系列データ曖昧化への応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-10-14"},"_buckets":{"deposit":"ca5aa3d0-5ac6-4aaa-9bb9-79890c8737b6"},"_deposit":{"id":"201505","pid":{"type":"depid","value":"201505","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"r-VAE: VAEへの再構成誤差の取り込みと時系列データ曖昧化への応用","author_link":["493121","493119","493122","493120"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"r-VAE: VAEへの再構成誤差の取り込みと時系列データ曖昧化への応用"},{"subitem_title":"r-VAE: Reconstruction Loss Aware VAE, Its Capability of Obfuscation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"プライバシー保護，データ曖昧化，自己情報コントロール，表現学習，VAE: 変分オートエンコーダ","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2019-10-14","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":"セコム株式会社 IS研究所"},{"subitem_text_value":"セコム株式会社 IS研究所"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Intelligent Systems Laboratory, SECOM CO., LTD.","subitem_text_language":"en"},{"subitem_text_value":"Intelligent Systems Laboratory, SECOM CO., 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/201505/files/IPSJCSS2019212.pdf","label":"IPSJCSS2019212.pdf"},"date":[{"dateType":"Available","dateValue":"2021-10-14"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2019212.pdf","filesize":[{"value":"2.6 MB"}],"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":"2acd247c-e218-4111-943d-ef2314a0b303","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松永, 昌浩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"チャン, クワン カイ"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masahiro, Matsunaga","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Quang, Khai Tran","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_18_relation_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_select":"NCID","subitem_relation_type_id_text":"ISSN　1882-0840"}}]},"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":"IoT 機器やウェアラブルデバイスから得られたデータを活用するサービスへの期待が高い．しかし，このようなサービスを利用したいとしても，ライフログのようなデータをそのまま提供することには抵抗感が生じる場合がある．そこで，我々は，データの加工に利用可能な機械学習モデルであるVAEを応用し，データの提供先やデータの利用目的に応じてデータを曖昧化する度合を柔軟に変更できる手法の開発を試みた．その実現にあたり，本稿では，VAEにおける再構成誤差を潜在変数として表現可能な機械学習モデル r-VAE: Reconstruction loss aware VAE を提案する．さらに，r-VAEを応用することで，スマートホームから得られた時系列データの曖昧化を行った．その結果，r-VAEの潜在変数の値を段階的に変化させることで，入力データが段階的に曖昧化されていくことが確認できた．そして，r-VAEによって曖昧化されたデータのプライバシーと有用性について考察した．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper introduces a new generative model r-VAE: Reconstruction loss aware VAE which learns reconstruction loss as latent variables.Furthermore, we apply r-VAE to obfuscating data which were collected from smart homes.As a result, our model can obfuscate input data by sliding a specific latent variable in latent space.We discuss the tradeoffs between privacy and utility of the obfuscated data converted by r-VAE.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1511","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2019論文集"}],"bibliographicPageStart":"1504","bibliographicIssueDates":{"bibliographicIssueDate":"2019-10-14","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":201505,"links":{}}