{"id":187194,"updated":"2025-01-20T02:22:06.290685+00:00","links":{},"created":"2025-01-19T00:53:53.123351+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00187194","sets":["6164:6165:6462:9463"]},"path":["9463"],"owner":"11","recid":"187194","title":["PWSCUP2017: 長期間の履歴データの再識別リスクを競う"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-16"},"_buckets":{"deposit":"3eada0d7-9a4e-433f-830c-e84a09054ae0"},"_deposit":{"id":"187194","pid":{"type":"depid","value":"187194","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"PWSCUP2017: 長期間の履歴データの再識別リスクを競う","author_link":["422496","422511","422494","422503","422499","422508","422493","422501","422509","422513","422512","422500","422505","422504","422514","422507","422498","422497","422502","422495","422506","422510"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"PWSCUP2017: 長期間の履歴データの再識別リスクを競う"},{"subitem_title":"PWSCUP2017: Evaluate Re-Identification Risk on Long Time History Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"PWS，匿名加工，再識別リスク，購買履歴","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2017-10-16","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"},{"subitem_text_value":"富士通クラウドテクノロジーズ"},{"subitem_text_value":"東京大学／国立研究開発法人理化学研究所"},{"subitem_text_value":"国立研究開発法人 情報通信研究機構"},{"subitem_text_value":"新日鉄住金ソリューションズ株式会社"},{"subitem_text_value":"NTTセキュアプラットフォーム究所"},{"subitem_text_value":"国立研究開発法人産業技術総合研究所"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"株式会社富士通研究所"},{"subitem_text_value":"株式会社KDDI総合研究所"},{"subitem_text_value":"筑波大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Fujitsu 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Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"菊池, 浩明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小栗, 秀暢"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中川, 裕志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"野島, 良"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"波多野, 卓磨"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱田, 浩気"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"村上, 隆夫"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"門田, 将徳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山岡, 裕司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山田, 明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡辺, 知恵美"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroaki, 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Watanabe","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":"2017年5月から本施行された改正個人情報保護法により，本人同意不要な第三者提供を可能とする匿名加工情報の利用が始まった．しかしながら，匿名加工情報の再識別リスクについては，それほど自明ではなく，標準的な評価手法は定まっていない．そこで，我々は共通のデータセットを用いて匿名加工を行い，加工されたデータの再識別の性能を競うコンテストを企画し，有用性が高く安全な匿名加工情報の技術開発を試みる．本稿では，このコンテストの基本定義，有用性の評価方法，安全性を定量的に定めるためのサンプルとなる再識別アルゴリズムなどについて述べる．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"On May 30, 2017, the amended Act on the Protection of Personal Information has been enforced fully in Japan.Hence, business parties are required the long-term history to be divided into small datasets so that identification to individual is impossible. To access the risk to be compromised accurately, the data needs to balance the utility and the security. We propose a new competition for best anonymization and re-identification algorithm. Our dataset consists of a customer dataset and a transaction dataset and these datasets are linked with pseudonyms, assigned for each customer identities. The paper addresses the aim of the competition, the target dataset,  sample algorithms, utility and security metrics.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2017論文集"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2017-10-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"11"}}