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| 開示リスク評価× | 差分プライバシー× | |
|---|---|---|
| 分野 | プライバシー | プライバシー |
| 系統≠ | Regression model | Machine learning |
| 提唱年≠ | 1989 | 2006 |
| 提唱者≠ | George Duncan & Diane Lambert | Cynthia Dwork |
| 種類≠ | Probabilistic risk model | Privacy-preserving randomized mechanism |
| 原典≠ | Duncan, G. T., & Lambert, D. (1989). The risk of disclosure for microdata. Journal of Business & Economic Statistics, 7(2), 207–217. DOI ↗ | Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12. DOI ↗ |
| 別名 | Microdata Disclosure Risk, Statistical Disclosure Control Risk Estimation, Istatistiksel Açıklama Riski Değerlendirmesi, Re-identification Risk Assessment | DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik |
| 関連 | 3 | 3 |
| 概要≠ | Disclosure Risk Assessment is a probabilistic framework introduced by Duncan and Lambert (1989) for quantifying how likely it is that releasing microdata — individual-level records from surveys or administrative files — will allow an outside party to identify a specific respondent or infer sensitive attributes. It is used by statistical agencies, data custodians, and researchers charged with protecting confidentiality before any public release of person-level datasets. | Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff. |
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