{"_buckets": {"deposit": "b7818dca-3631-4500-a084-b316120e776f"}, "_deposit": {"created_by": 13, "id": "9201", "owners": [13], "pid": {"revision_id": 0, "type": "depid", "value": "9201"}, "status": "published"}, "_oai": {"id": "oai:uec.repo.nii.ac.jp:00009201", "sets": ["6"]}, "author_link": ["24288", "25085", "24348", "24350"], "control_number": "9201", "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2014-09-15", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "9", "bibliographicPageEnd": "2133", "bibliographicPageStart": "2120", "bibliographicVolumeNumber": "55", "bibliographic_titles": [{"bibliographic_title": "情報処理学会論文誌", "bibliographic_titleLang": "ja"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "多くのユーザからセンシングしたデータを収集し，その分布を把握することによって，マーケティング等に役立てることができる．しかし，これらのデータには個人を特定できる情報が含まれることがあり，ユーザのプライバシ情報が漏洩するリスクがある．このような問題に対応し，ユーザが一定の確率で真実でない情報をサーバに送信するよう制約を設けることで，プライバシを保護しつつ，サーバ側で真のデータ分布を再構築するRandomized Response（RR）という手法が提案されている．再構築された結果と真のデータ分布との間には誤差があるが，収集対象となるユーザ属性が複数ある場合，従来手法ではどの程度の誤差が発生するか知ることができず，再構築した結果から有効な分析ができないという実用上の課題があった．また，要求されるプライバシ保護レベルを満たしたうえで，この誤差を最小化できるようなRRのパラメータの設定方法も提案されていない．さらに，ユーザ属性の数が増加するほど，再構築に要する計算時間が膨大になるという課題もある．本論文ではこれら実用上の課題を解決する手法を提案する．数学的解析および実データを利用したシミュレーション結果により，提案手法の有効性を示す．", "subitem_description_type": "Abstract"}, {"subitem_description": "Ubiquitous computing environment can collect sensing data of users. These data can be used for several purposes such as decision-making of companies. However, collecting user data may include personally identifiable information and violate their privacy. Randomized response scheme which collect disguised data of each user and can assume true data distributions of users have been proposed. However, existing studies do not provide a calculation method of estimated errors between the true data distributions and the reconstructed data distributions when multiple attributes are needed to be anonymized and collected. Also, they do not provide a method of setting an RR\u0027s parameter that will minimize erros and ensures a required privacy level. Moreover, existing studies need a lot of calculation time for reconstructing data distributions if the number of user attributes is large. We prove out proposed method is effective by mathematical analysis and simulations.", "subitem_description_type": "Abstract"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "情報処理学会"}]}, "item_10001_relation_17": {"attribute_name": "関連サイト", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://id.nii.ac.jp/1001/00103084/", "subitem_relation_type_select": "URI"}}]}, "item_10001_rights_15": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "© 2014 Information Processing Society of Japan. 本著作物の著作権は情報処理学会に帰属します。本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」に従うことをお願いいたします"}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1882-7764", "subitem_source_identifier_type": "ISSN"}]}, "item_10001_version_type_20": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "清, 雄一", "creatorNameLang": "ja"}, {"creatorName": "セイ, ユウイチ", "creatorNameLang": "ja-Kana"}], "nameIdentifiers": [{"nameIdentifier": "24288", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "大須賀, 昭彦", "creatorNameLang": "ja"}, {"creatorName": "オオスガ, アキヒコ", "creatorNameLang": "ja-Kana"}], "nameIdentifiers": [{"nameIdentifier": "25085", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Sei, Yuichi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "24348", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ohsuga, Akihiko", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "24350", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2019-05-15"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "IPSJ-JNL5509018.pdf", "filesize": [{"value": "4.7 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 4700000.0, "url": {"label": "IPSJ-JNL5509018", "url": "https://uec.repo.nii.ac.jp/record/9201/files/IPSJ-JNL5509018.pdf"}, "version_id": "b252db75-869d-4939-b5b6-b7a86a896993"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "プライバシ", "subitem_subject_scheme": "Other"}, {"subitem_subject": "データマイニング", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "多次元属性のための匿名データ収集アルゴリズムの提案", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "多次元属性のための匿名データ収集アルゴリズムの提案", "subitem_title_language": "ja"}, {"subitem_title": "Anonymized Data Collection for Multi-dimensional Attributes", "subitem_title_language": "en"}]}, "item_type_id": "10001", "owner": "13", "path": ["6"], "permalink_uri": "https://uec.repo.nii.ac.jp/records/9201", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2019-05-15"}, "publish_date": "2019-05-15", "publish_status": "0", "recid": "9201", "relation": {}, "relation_version_is_last": true, "title": ["多次元属性のための匿名データ収集アルゴリズムの提案"], "weko_shared_id": -1}