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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
he01cc2d53aeff486
Venue
VLDB
Year
2025
Pagerank
4.9793485e-05
Overall Rank
11,351 | 23.69%
DOI
10.14778/3749646.3749679

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Authors

BibTeX Citation

@article{lange_vldb25,
        title = {{Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy}},
        author = {Lange, Martin and Guerra-Balboa, Patricia and Parra-Arnau, Javier and Strufe, Thorsten},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4090--4103},
        doi = {10.14778/3749646.3749679},
        url = {https://doi.org/10.14778/3749646.3749679},
        year = {2025}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,246 No Free Lunch in Data Privacy 2011 SIGMOD 0.00011346748
2,370 Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies 2014 SIGMOD 8.5616358e-05
3,123 Bayesian Differential Privacy on Correlated Data 2015 SIGMOD 7.6232896e-05
3,909 Pufferfish Privacy Mechanisms for Correlated Data 2017 SIGMOD 6.9313197e-05
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