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Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness
https://uec.repo.nii.ac.jp/records/8922
https://uec.repo.nii.ac.jp/records/89225a20c07f-f607-42eb-829a-02c7b7ad1a51
| 名前 / ファイル | ライセンス | アクション |
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1-TDSC (4.1 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2019-01-21 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題 | privacy | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題 | data mining | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題 | l-diversity | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題 | t-closeness | |||||||||||||
| 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
× Okumura, Hiroshi
× Takenouchi, Takao
× Ohsuga, Akihiko
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets. | |||||||||||||
| 書誌情報 |
en : IEEE Transactions on Dependable and Secure Computing 巻 16, 号 4, p. 580-593, 発行日 2019-07 |
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| 出版者 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 1545-5971 | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | 10.1109/TDSC.2017.2698472 | |||||||||||||
| 権利 | ||||||||||||||
| 権利情報 | © 2019 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. This is not the published version. Please cite only the published version. | |||||||||||||
| 関連サイト | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/TDSC.2017.2698472 | |||||||||||||
| 著者版フラグ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
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Sei, Yuichi, Okumura, Hiroshi, Takenouchi, Takao, Ohsuga, Akihiko, 2019, Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness: IEEE, 580–593 p.
