{"updated":"2025-01-19T12:47:06.825874+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225514","sets":["1164:2822:11181:11182"]},"path":["11182"],"owner":"44499","recid":"225514","title":["Intel Xeon及び富士通A64FX上での ビット削減版準同型暗号ライブラリの評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-16"},"_buckets":{"deposit":"51054dab-3994-4611-8661-c8520628ef5b"},"_deposit":{"id":"225514","pid":{"type":"depid","value":"225514","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Intel Xeon及び富士通A64FX上での ビット削減版準同型暗号ライブラリの評価","author_link":["596872","596869","596874","596873","596866","596867","596868","596865","596871","596870"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Intel Xeon及び富士通A64FX上での ビット削減版準同型暗号ライブラリの評価"},{"subitem_title":"Evaluation of Bit-Reduced Homomorphic Encryption Library on Intel Xeon and Fujitsu A64FX","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"性能評価","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-03-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"早稲田大学"},{"subitem_text_value":"理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Waseda University","subitem_text_language":"en"},{"subitem_text_value":"RIKEN","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/225514/files/IPSJ-EMB23062007.pdf","label":"IPSJ-EMB23062007.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-EMB23062007.pdf","filesize":[{"value":"1.6 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健太郎"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Msaki, Nishi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Teppei, Shishido","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Xinyi, Li","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keiji, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kentaro, Sano","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12149313","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":"2188-868X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層学習が様々な場面で利用されるようになり，それと共に学習データや入力データ，推論結果，さらには学習モデルの保護が問題となりつつある．そのため，データを暗号化したまま演算処理できる準同型暗号による深層学習が注目されている．しかしながら，準同型暗号による演算コストは高く，これまでに, 並列化やハードウェアアクセラレータの利用といった様々な高速化手法が提案されている．一方筆者等は，深層学習の推論処理において多くのビット数が必要としないことに注目し，ビット削減版準同型暗号を利用した深層学習推論を提案してきた．本稿ではまず，ビット削減版の深層学習処理を準同型暗号ライブラリ SEAL とSEAL を用いた深層学習フレームワーク HE-Transformer に実装し，Intel Xeon プロセッサ上で評価した．さらに，富士通 A64FX 上でビット削減版準同型暗号を実装し評価を行なった．評価の結果，CryptoNets を用いた MNIST データセットの分類においてオリジナルの nGraph-HE2 に対し Intel Xeon 上で最大で 9.37 倍の速度向上が得られた．また，富士通 A64FX 上で行列積を評価した結果，Intel Xeon W-2145 と比較して，最大 1.08 倍の速度向上が得られた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告組込みシステム（EMB）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2023-EMB-62"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:25:01.339003+00:00","id":225514,"links":{}}