Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss

Summary: Publish NBC views with privacy preserved, no accuracy loss. Linear-time perturbation of NBC stats yields sanitized, rational-number representations and synthetic data; preserves NBC behavior under non-uniform priors with real-data validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9973
Venue: VLDB
Year: 2009
Pagerank: 4.1945683e-05
Overall Rank: 12,352 | 14.07%
DOI: -

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Authors

1. Barzan Mozafari
2. Carlo Zaniolo

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074232718
177	Limiting Privacy Breaches in Privacy Preserving Data Mining	2003	PODS	0.0003788711
304	On the Complexity of Optimal K-Anonymity	2004	PODS	0.00028290121
455	Incognito: Efficient Full-Domain K-Anonymity	2005	SIGMOD	0.00022717354
1,083	A Formal Analysis of Information Disclosure in Data Exchange	2004	SIGMOD	0.00014210752
1,506	Auditing Boolean Attributes	2000	PODS	0.00011618118
1,761	The Boundary Between Privacy and Utility in Data Publishing	2007	VLDB	0.00010651764
2,577	Simulatable Auditing	2005	PODS	8.5099821e-05
3,785	Checking for k-Anonymity Violation by Views	2005	VLDB	6.7690512e-05
4,899	Deriving Private Information from Randomized Data	2005	SIGMOD	5.8439867e-05

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