On the Design and Quantification of Privacy Preserving Data Mining Algorithms
Summary: EM-based distribution reconstruction that provably converges to the MLE from perturbed data, improving estimation accuracy and robustness with large samples. Defines quantitative privacy metrics to measure reconstruction loss and compare perturbation mechanisms, providing a foundation for evaluating privacy-preserving data mining. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074232718 |
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