作者: 刺耳的聲音 時(shí)間: 2025-3-21 20:42 作者: 檢查 時(shí)間: 2025-3-22 00:38
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作者: 現(xiàn)代 時(shí)間: 2025-3-22 04:48 作者: Flatter 時(shí)間: 2025-3-22 09:03
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.作者: Acetaldehyde 時(shí)間: 2025-3-22 13:39 作者: Acetaldehyde 時(shí)間: 2025-3-22 17:38 作者: 多產(chǎn)魚 時(shí)間: 2025-3-23 00:33
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.作者: Lipohypertrophy 時(shí)間: 2025-3-23 04:23
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.作者: Malleable 時(shí)間: 2025-3-23 08:16 作者: Fecal-Impaction 時(shí)間: 2025-3-23 13:34 作者: 整潔 時(shí)間: 2025-3-23 15:31 作者: 摻和 時(shí)間: 2025-3-23 20:20
https://doi.org/10.1007/978-1-349-19305-9the public sector is pushed to release as much information as possible for the sake of transparency. Organizations releasing data include national statistical institutes (whose core mission is to publish statistical information), healthcare authorities (which occasionally release epidemiologic infor作者: harmony 時(shí)間: 2025-3-23 22:39 作者: 揭穿真相 時(shí)間: 2025-3-24 05:59
Moosa A. Elayah,Laurent A. Lambertn be used by the data controller to empirically evaluate the disclosure risk associated with an anonymized data set. The data protector or controller uses a record linkage algorithm (or several such algorithms) to link each record in the anonymized data with a record in the original data set. Since 作者: 口訣 時(shí)間: 2025-3-24 07:59
Paula Satne,Krisanna M. Scheiterntifier) attributes, thereby preserving the anonymity of the respondents in the data set. Although the privacy guarantees offered by .-anonymity are limited (i.e., it does not protect against attribute disclosure), its simplicity has made it quite popular. It is sometimes seen as offering a minimal 作者: 恫嚇 時(shí)間: 2025-3-24 11:43 作者: Accede 時(shí)間: 2025-3-24 16:17
Kant on Punishment, Pardon, and Forgivenessd on those principles can be adapted to yield .-closeness by adding the .-closeness constraint in the search for a feasible minimal generalization: in [50] the Incognito algorithm and in [51] the Mondrian algorithm are respectively adapted to .-closeness.作者: 試驗(yàn) 時(shí)間: 2025-3-24 20:10
https://doi.org/10.1057/9780230371576nitially stated as a privacy guarantee in an interactive setting, where queries are submitted to a database containing the original individual records. However, it is general enough to deal with microdata releases. The principle underlying differential privacy is that the presence or absence of any 作者: strain 時(shí)間: 2025-3-25 02:30 作者: 詞匯表 時(shí)間: 2025-3-25 04:09
The Superpowers and the October Ward/or attribute disclosure. These are tightly related to the two views of privacy that have been presented in Chapter 2: anonymity (it should not be possible to re-identify any individual in the published data) and confidentiality or secrecy (access to the released data should not reveal confidential作者: 全國性 時(shí)間: 2025-3-25 08:05 作者: CARE 時(shí)間: 2025-3-25 13:18
Conclusions and Research Directions,d/or attribute disclosure. These are tightly related to the two views of privacy that have been presented in Chapter 2: anonymity (it should not be possible to re-identify any individual in the published data) and confidentiality or secrecy (access to the released data should not reveal confidential information related to any specific individual).作者: Institution 時(shí)間: 2025-3-25 18:47
https://doi.org/10.1057/9780230371576Although differential privacy was designed as a privacy model for queryable databases, as introduced in Section 8.6, several methods to generate differentially private data sets have been proposed. This chapter reviews perturbative masking approaches to generate a differentially private data set aimed at being as general as .-anonymity [96, 97].作者: absorbed 時(shí)間: 2025-3-26 00:01 作者: 厚顏無恥 時(shí)間: 2025-3-26 03:20
978-3-031-01219-8Springer Nature Switzerland AG 2016作者: 打擊 時(shí)間: 2025-3-26 05:13
Database Anonymization978-3-031-02347-7Series ISSN 1945-9742 Series E-ISSN 1945-9750 作者: glomeruli 時(shí)間: 2025-3-26 08:29 作者: Overdose 時(shí)間: 2025-3-26 14:28
The Superpowers and the October Ward/or attribute disclosure. These are tightly related to the two views of privacy that have been presented in Chapter 2: anonymity (it should not be possible to re-identify any individual in the published data) and confidentiality or secrecy (access to the released data should not reveal confidential information related to any specific individual).作者: Aphorism 時(shí)間: 2025-3-26 18:44 作者: 微粒 時(shí)間: 2025-3-27 00:53 作者: blight 時(shí)間: 2025-3-27 03:27 作者: 邪惡的你 時(shí)間: 2025-3-27 08:43 作者: 有幫助 時(shí)間: 2025-3-27 12:19 作者: exacerbate 時(shí)間: 2025-3-27 17:18
Beyond ,-Anonymity: ,-Diversity and ,-Closeness,hat an individual’s privacy must be protected if the corresponding record is hidden within a group of . records. However, this principle fails to provide sufficient protection when the records in the .-anonymous group have a similar value for the confidential attribute. In other words, .-anonymity p作者: Urgency 時(shí)間: 2025-3-27 20:11 作者: 縮短 時(shí)間: 2025-3-27 23:55 作者: 浸軟 時(shí)間: 2025-3-28 03:21
Differential Privacy by Individual Ranking Microaggregation,variate microaggregation to reduce the sensitivity (and, thus, the required amount of noise) in the masked records. Using multivariate microaggregation to reduce the sensitivity was complex because the change of a single record in a data set could lead to multiple changes in the microaggregation clu作者: DIS 時(shí)間: 2025-3-28 06:21
Conclusions and Research Directions,d/or attribute disclosure. These are tightly related to the two views of privacy that have been presented in Chapter 2: anonymity (it should not be possible to re-identify any individual in the published data) and confidentiality or secrecy (access to the released data should not reveal confidential作者: 真實(shí)的你 時(shí)間: 2025-3-28 11:24
Book 2016in 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作者: narcissism 時(shí)間: 2025-3-28 16:15
https://doi.org/10.1007/978-1-349-19305-9espondents. Hence, rather than publishing accurate information for each individual, the aim should be to provide useful statistical information, that is, to preserve as much as possible in the released data the statistical properties of the original data.作者: 變白 時(shí)間: 2025-3-28 19:49 作者: gait-cycle 時(shí)間: 2025-3-28 22:57
