WEKO3
アイテム
{"_buckets": {"deposit": "615ae26d-f61d-47fc-9bfa-0191abacd804"}, "_deposit": {"created_by": 13, "id": "9665", "owners": [13], "pid": {"revision_id": 0, "type": "depid", "value": "9665"}, "status": "published"}, "_oai": {"id": "oai:uec.repo.nii.ac.jp:00009665", "sets": ["6"]}, "author_link": ["26046", "26047"], "control_number": "9665", "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2020", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "10", "bibliographicPageEnd": "2785", "bibliographicPageStart": "2785", "bibliographicVolumeNumber": "20", "bibliographic_titles": [{"bibliographic_title": "Sensors", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered.", "subitem_description_type": "Abstract"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "MDPI"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isIdenticalTo", "subitem_relation_type_id": {"subitem_relation_type_id_text": "10.3390/s20102785", "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/10.3390/s20102785", "subitem_relation_type_select": "DOI"}}]}, "item_10001_rights_15": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)."}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1424-8220", "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": "Sei, Yuichi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "26046", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ohsuga, Akihiko", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "26047", "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": "2020-10-30"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "sensors-20-02785-v2.pdf", "filesize": [{"value": "1.7 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensefree": "CC BY", "licensetype": "license_free", "mimetype": "application/pdf", "size": 1700000.0, "url": {"label": "sensors-20-02785-v2", "url": "https://uec.repo.nii.ac.jp/record/9665/files/sensors-20-02785-v2.pdf"}, "version_id": "aadc8bfe-e4ec-4b3c-a515-4e39f4094864"}]}, "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": "Differentially Private Mobile Crowd Sensing Considering Sensing Errors", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Differentially Private Mobile Crowd Sensing Considering Sensing Errors", "subitem_title_language": "en"}]}, "item_type_id": "10001", "owner": "13", "path": ["6"], "permalink_uri": "https://uec.repo.nii.ac.jp/records/9665", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2020-10-30"}, "publish_date": "2020-10-30", "publish_status": "0", "recid": "9665", "relation": {}, "relation_version_is_last": true, "title": ["Differentially Private Mobile Crowd Sensing Considering Sensing Errors"], "weko_shared_id": -1}
Differentially Private Mobile Crowd Sensing Considering Sensing Errors
https://uec.repo.nii.ac.jp/records/9665
https://uec.repo.nii.ac.jp/records/9665a47ace89-069f-4b77-ac6e-8fbf8de2c197
名前 / ファイル | ライセンス | アクション |
---|---|---|
sensors-20-02785-v2 (1.7 MB)
|
CC BY
|
Item type | 学術雑誌論文 / Journal Article(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2020-10-30 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Differentially Private Mobile Crowd Sensing Considering Sensing Errors | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Sei, Yuichi
× Sei, Yuichi× Ohsuga, Akihiko |
|||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered. | |||||
書誌情報 |
en : Sensors 巻 20, 号 10, p. 2785-2785, 発行日 2020 |
|||||
出版者 | ||||||
出版者 | MDPI | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1424-8220 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.3390/s20102785 | |||||
権利 | ||||||
権利情報 | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.3390/s20102785 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |