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Re-Identification in Differentially Private Incomplete Datasets
https://uec.repo.nii.ac.jp/records/10328
https://uec.repo.nii.ac.jp/records/103280a509219-535d-40b1-84d9-8f438b75b78d
名前 / ファイル | ライセンス | アクション |
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OJCS.2022.3175999.pdf (2.8 MB)
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CC BY 4.0
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2023-03-13 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Re-Identification in Differentially Private Incomplete Datasets | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Differential privacy | |||||
キーワード | ||||||
言語 | en | |||||
主題 | ethical and privacy framework | |||||
キーワード | ||||||
言語 | en | |||||
主題 | re-identification | |||||
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× Ohsuga, Akihiko |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de facto standard privacy metric. Despite the general belief that the risk of re-identification is insignificant when these techniques are applied, this study shows that even after applying these techniques, the risk of being re-identified is highly significant for some data. This study proposes in detail an algorithm for estimating the number of people in a population who have certain attribute values based on incomplete, differentially private databases. If the estimated number is one, the probability that only one person with that attribute value is present in the population is high, which means that there is a high probability of re-identification. Therefore, this study concludes that the re-identification risk must be evaluated even after applying state-of-the-art techniques to protect privacy. | |||||
書誌情報 |
en : IEEE Open Journal of the Computer Society 巻 3, p. 62-72, 発行日 2022-05-20 |
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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 | 26441268 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1109/OJCS.2022.3175999 | |||||
権利 | ||||||
権利情報 | (c) 2022 Author(s).This article is distributed under a Creative Commons Attribution (CC BY 4.0) License. | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/OJCS.2022.3175999 | |||||
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 |