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Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy

Summary: Derives practical utility guarantees for Bayesian differential privacy (BDP) under arbitrary and structured correlations (Gaussian multivariate, Markov chains) and formalizes theoretical links to standard DP. Proposes a method to adapt DP mechanisms to meet BDP with competitive utility, validated on real datasets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14028
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,721 | 25.42%
DOI
10.14778/3749646.3749679

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

Rank Cited Paper Year Venue Pagerank
1,465 No Free Lunch in Data Privacy 2011 SIGMOD 0.00011860847
2,227 Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies 2014 SIGMOD 9.2421238e-05
3,172 Bayesian Differential Privacy on Correlated Data 2015 SIGMOD 7.4411955e-05
4,461 Pufferfish Privacy Mechanisms for Correlated Data 2017 SIGMOD 6.1616828e-05
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