{"links":{},"id":211687,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211687","sets":["1164:3616:10522:10611"]},"path":["10611"],"owner":"44499","recid":"211687","title":["プライバシー保護を考慮したガウス過程回帰の秘匿演算"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-06-17"},"_buckets":{"deposit":"f0103c6c-c1c9-4aa4-8348-bebd0cc676d5"},"_deposit":{"id":"211687","pid":{"type":"depid","value":"211687","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"プライバシー保護を考慮したガウス過程回帰の秘匿演算","author_link":["538222","538219","538221","538220"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"プライバシー保護を考慮したガウス過程回帰の秘匿演算"},{"subitem_title":"Privacy-Preserving Secure Computation of Gaussian Process Regression","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-06-17","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"琉球大学情報基盤統括センター"},{"subitem_text_value":"日本電信電話株式会社未来ねっと研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Information Technology Center, University of the Ryukyus","subitem_text_language":"en"},{"subitem_text_value":"NTT Network Innovation Laboratories, Nippon Telegraph and Telephone Corp. ","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/211687/files/IPSJ-AVM21113008.pdf","label":"IPSJ-AVM21113008.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AVM21113008.pdf","filesize":[{"value":"2.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"27"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"d0fbe0e1-22d5-4b00-98e6-c67b5566fa1d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"仲地, 孝之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yitu, Wang"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takayuki, Nakachi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yitu, Wang","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438399","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8582","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，ランダムユニタリ変換に基づき生成された秘匿データに対するガウス過程回帰（GPR：Gaussian Process Regression）を提案し，その性能を評価する．近年，エッジやクラウドサービスを利用しプロバイダーが提供する計算資源の利用が急速に普及している．しかし，プロバイダーの信頼性欠如や事故によってデータの不正利用，流出，プライバシー侵害などの問題が危惧されている．本稿ではそのような背景から，プライバシー保護を考慮したガウス過程回帰の秘匿演算法について検討する．入力と出力の間に非線形の関係がある場合にも秘匿演算が成立することを示し，ガウス過程回帰の性能が劣化しないことを示す．最後にガウス過程回帰の秘匿演算を用いて，人工デー タならびに糖尿病の臨床データに対するシミュレーションにより，提案法の有効性を検証する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose Gaussian Process Regression (GPR) for encrypted data generated based on random unitary transformation and evaluate its performance. In recent years, computational forms that utilize computational resources provided by providers using edge and cloud services have rapidly become widespread. However, there are concerns about problems such as data fraud, leakage, and privacy invasion due to lack of reliability of providers and accidents. This paper examines the secure Gaussian process regression method that takes privacy protection into consideration. It is shown that the secure operation is established even when there is a non-linear relationship between the input data and the output data. Finally, as an application example of the secure Gaussian process regression, the disease progression is predicted using clinical data of diabetes, and the effectiveness of the proposed method is verified by simulation.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告オーディオビジュアル複合情報処理（AVM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-06-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2021-AVM-113"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:12:49.693377+00:00","updated":"2025-01-19T17:43:02.818752+00:00"}