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| k-Anonymity: Bảo vệ Quyền riêng tư Cá nhân trong Dữ liệu Được Công bố× | Tạo dữ liệu tổng hợp để kiểm soát tiết lộ× | |
|---|---|---|
| Lĩnh vực | Quyền riêng tư | Quyền riêng tư |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2002 | 1993 |
| Người khởi xướng≠ | Latanya Sweeney | Donald Rubin |
| Loại≠ | Privacy-preserving data transformation | Privacy-preserving data synthesis |
| Công trình gốc≠ | Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570. DOI ↗ | Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468. link ↗ |
| Tên gọi khác | k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik | Fully Synthetic Data, Partial Synthetic Data, Statistical Data Synthesis, Sentetik Veri Üretimi |
| Liên quan≠ | 2 | 3 |
| Tóm tắt≠ | k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources. | Synthetic data generation is a statistical disclosure limitation technique introduced by Donald Rubin in 1993, in which values in a confidential dataset are replaced by draws from a fitted posterior predictive distribution rather than released directly. The resulting artificial records preserve the joint statistical structure of the original data while preventing the identification of real individuals, enabling analysts to work with a publicly releasable dataset that behaves like the original for most inferential purposes. |
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