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Bayesian Differential Privacy on Correlated Data

Summary: Proposes Bayesian differential privacy (Pufferfish) for perturbation privacy on correlated data. Gaussian correlation model analyzes privacy across adversaries with varying priors, showing worst privacy under minimal knowledge and uncertain priors. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5039
Venue
SIGMOD
Year
2015
Pagerank
7.4350161e-05
Overall Rank
3,178 | 77.92%
DOI
10.1145/2733272.2747643

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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
137 Revealing Information while Preserving Privacy 2003 PODS 0.00042381562
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00037858416
506 Relationship Privacy: Output Perturbation for Queries with Joins 2009 PODS 0.00021481335
567 Practical Privacy: The SuLQ Framework 2005 PODS 0.00019940193
1,082 A Formal Analysis of Information Disclosure in Data Exchange 2004 SIGMOD 0.00014196516
1,446 PrivBayes: Private Data Release via Bayesian Networks 2014 SIGMOD 0.00011931212
1,463 No Free Lunch in Data Privacy 2011 SIGMOD 0.00011856229
1,760 The Boundary Between Privacy and Utility in Data Publishing 2007 VLDB 0.00010641674
2,228 Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies 2014 SIGMOD 9.2488181e-05
2,404 Attacks on Privacy and deFinetti's Theorem 2009 SIGMOD 8.8736984e-05
2,579 Simulatable Auditing 2005 PODS 8.5010694e-05
2,629 Epistemic Privacy 2008 PODS 8.4237864e-05
4,185 Towards an Axiomatization of Statistical Privacy and Utility 2010 PODS 6.3713506e-05
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