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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)

Paper ID
hd94b8052f0814113
Venue
VLDB
Year
2009
Pagerank
4.9793485e-05
Overall Rank
12,835 | 13.71%
DOI
10.14778/1687627.1687759

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Authors

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}
}

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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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