方法对比
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| 安全多方计算× | k-匿名化：保护发布数据中的个体隐私× | |
|---|---|---|
| 领域 | 隐私 | 隐私 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1982 | 2002 |
| 提出者≠ | Andrew Yao | Latanya Sweeney |
| 类型≠ | Cryptographic protocol family | Privacy-preserving data transformation |
| 开创性文献≠ | Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164. DOI ↗ | Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570. DOI ↗ |
| 别名 | MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama | k-Anonymization, k-Anonymous Microdata, Quasi-Identifier Suppression Model, k-Anonimlik |
| 相关≠ | 3 | 2 |
| 摘要≠ | Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning. | 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. |
| ScholarGate数据集 ↗ |
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