{"created":"2023-05-15T08:43:49.581887+00:00","id":8924,"links":{},"metadata":{"_buckets":{"deposit":"a4e8a06c-117a-4688-a226-d5f1f3224044"},"_deposit":{"created_by":13,"id":"8924","owners":[13],"pid":{"revision_id":0,"type":"depid","value":"8924"},"status":"published"},"_oai":{"id":"oai:uec.repo.nii.ac.jp:00008924","sets":["6"]},"author_link":["24358","24357"],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2017-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"939","bibliographicPageStart":"926","bibliographicVolumeNumber":"12","bibliographic_titles":[{},{"bibliographic_title":"IEEE Transactions on Information Forensics and Security","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users' sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies.","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1109/TIFS.2016.2632069","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1109/TIFS.2016.2632069","subitem_relation_type_select":"DOI"}}]},"item_10001_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1556-6013","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_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Sei, Yuichi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ohsuga, Akihiko","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-01-22"}],"displaytype":"detail","filename":"3-IFS.pdf","filesize":[{"value":"2.9 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"3-IFS","url":"https://uec.repo.nii.ac.jp/record/8924/files/3-IFS.pdf"},"version_id":"9483d70b-2eeb-4748-9fb3-08791bbdeef1"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Mobile crowdsensing","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"privacy","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"data mining","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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":"Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"13","path":["6"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-01-22"},"publish_date":"2019-01-22","publish_status":"0","recid":"8924","relation_version_is_last":true,"title":["Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing"],"weko_creator_id":"13","weko_shared_id":-1},"updated":"2023-05-15T10:08:21.839220+00:00"}