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)
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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 |
|---|---|---|---|---|
| 1,463 | No Free Lunch in Data Privacy | 2011 | SIGMOD | 0.00011856229 |
| 2,228 | Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies | 2014 | SIGMOD | 9.2488181e-05 |
| 3,178 | Bayesian Differential Privacy on Correlated Data | 2015 | SIGMOD | 7.4350161e-05 |
| 4,461 | Pufferfish Privacy Mechanisms for Correlated Data | 2017 | SIGMOD | 6.1571961e-05 |
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