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Titlebook: Database Anonymization; Privacy Models, Data Josep Domingo-Ferrer,David Sánchez,Jordi Soria-Com Book 2016 Springer Nature Switzerland AG 20

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發(fā)表于 2025-3-21 19:24:40 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Database Anonymization
副標題Privacy Models, Data
編輯Josep Domingo-Ferrer,David Sánchez,Jordi Soria-Com
視頻videohttp://file.papertrans.cn/264/263345/263345.mp4
叢書名稱Synthesis Lectures on Information Security, Privacy, and Trust
圖書封面Titlebook: Database Anonymization; Privacy Models, Data Josep Domingo-Ferrer,David Sánchez,Jordi Soria-Com Book 2016 Springer Nature Switzerland AG 20
描述The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guaranteesthey offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization me
出版日期Book 2016
版次1
doihttps://doi.org/10.1007/978-3-031-02347-7
isbn_softcover978-3-031-01219-8
isbn_ebook978-3-031-02347-7Series ISSN 1945-9742 Series E-ISSN 1945-9750
issn_series 1945-9742
copyrightSpringer Nature Switzerland AG 2016
The information of publication is updating

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沙發(fā)
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1945-9742 lished data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer scienc
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Paula Satne,Krisanna M. Scheiteride sufficient protection when the records in the .-anonymous group have a similar value for the confidential attribute. In other words, .-anonymity provides protection against identity disclosure but that is not enough to prevent attribute disclosure when the values of the confidential attribute are similar across records.
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Quantifying Disclosure Risk: Record Linkage,e uses to estimate the number of re-identifications that might be obtained by a specialized intruder. If the number of re-identifications is too high, the data set needs more anonymization by the controller before it can be released.
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發(fā)表于 2025-3-23 04:23:19 | 只看該作者
The ,-Anonymity Privacy Model,imited (i.e., it does not protect against attribute disclosure), its simplicity has made it quite popular. It is sometimes seen as offering a minimal requirement for disclosure risk limitation that is later complemented with protection against attribute disclosure.
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