Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss
Summary: Publishes privacy-safe Naive Bayes statistics/views without changing classifier decisions or accuracy, avoiding randomization/anonymization loss. A linear-time ratio-perturbation algorithm yields publishable rational/synthetic datasets, with extensions beyond uniform priors. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Barzan Mozafari (University of California Los Angeles)
- 2. Carlo Zaniolo (University of California Los Angeles)
BibTeX Citation
@article{mozafari_vldb09,
title = {{Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss}},
author = {Mozafari, Barzan and Zaniolo, Carlo},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687759},
url = {https://doi.org/10.14778/1687627.1687759},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 68 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00037958605 |
| 226 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00023984903 |
| 404 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00019081867 |
| 469 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD | 0.0001775622 |
| 1,109 | A Formal Analysis of Information Disclosure in Data Exchange | 2004 | SIGMOD | 0.00011987291 |
| 1,782 | The Boundary Between Privacy and Utility in Data Publishing | 2007 | VLDB | 9.652327e-05 |
| 2,050 | Auditing Boolean Attributes | 2000 | PODS | 9.1201864e-05 |
| 2,449 | Simulatable Auditing | 2005 | PODS | 8.4490697e-05 |
| 3,926 | Checking for k-Anonymity Violation by Views | 2005 | VLDB | 6.9196266e-05 |
| 4,651 | Deriving Private Information from Randomized Data | 2005 | SIGMOD | 6.4857639e-05 |
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