{"links":{},"id":219744,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219744","sets":["6164:6165:6640:11008"]},"path":["11008"],"owner":"44499","recid":"219744","title":["key-valueデータにおける局所差分プライバシーアルゴリズムPrivKVの改良"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-06"},"_buckets":{"deposit":"be186b2c-2412-4edf-82c2-10a07ceeabf6"},"_deposit":{"id":"219744","pid":{"type":"depid","value":"219744","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"key-valueデータにおける局所差分プライバシーアルゴリズムPrivKVの改良","author_link":["573461","573462","573460"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"key-valueデータにおける局所差分プライバシーアルゴリズムPrivKVの改良"}]},"item_type_id":"18","publish_date":"2022-07-06","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":"明治大学先端数理科学研究科"},{"subitem_text_value":"明治大学総合数理学部"},{"subitem_text_value":"Department of Information Management and Finance, National Yang Ming Chiao Tung University"}]},"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/219744/files/IPSJ-DICOMO2022170.pdf","label":"IPSJ-DICOMO2022170.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2022170.pdf","filesize":[{"value":"1.0 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":"44"}],"accessrole":"open_date","version_id":"574ef38d-f5b3-437d-a082-3addc318b20b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"堀込, 光"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菊池, 浩明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chia-Mu, Yu"}],"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":"局所差分プライバシは，単一次元の個人の持つプライバシー情報に局所的にノイズを付与することで，プライバシー情報が特定されることを防ぐ技術である．Randmized Response (RR) や Harmony のような従来の局所差分プライバシアルゴリズムでは，単一次元の情報しか扱うことができなかった．Ye らによって提案された局所差分プライバシアルゴリズム PrivKV では，離散値と連続値の 2 次元のデータである key-value データについて，離散値と連続値の相関を維持したプライバシー情報の収集を可能にした．しかし，PrivKV では，最尤推定法で集計がされており，精度が十分ではない．そこで，本稿では，PrivKV に対してExpectation Maximization (EM) アルゴリズムを適用する手法を提案し，数値実験により従来手法との精度を比較する．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1216","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2022論文集"}],"bibliographicPageStart":"1209","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:19:48.415382+00:00","updated":"2025-01-19T14:47:34.956466+00:00"}