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)
Incoming Non-self Citations Over Time
Authors
- 1. Bin Yang
- 2. Issei Sato
- 3. Hiroshi Nakagawa
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,461 | Pufferfish Privacy Mechanisms for Correlated Data | 2017 | SIGMOD | 6.1616828e-05 |
| 4,754 | Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism | 2021 | VLDB | 5.9468785e-05 |
| 5,246 | Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics | 2017 | SIGMOD | 5.6063332e-05 |
| 10,721 | Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy | 2025 | VLDB | 4.1945683e-05 |
| 11,744 | ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release | 2018 | VLDB | 4.1945683e-05 |
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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.
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