Marginal Release Under Local Differential Privacy
Summary: Local differential privacy for materializing multidimensional marginals; first tight theoretical bounds on accuracy under LDP. Empirical and theoretical evaluation shows Fourier-based releases beat direct local-marginal methods for modeling and correlation tests. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Graham Cormode (University of Warwick)
- 2. Tejas Kulkarni (University of Warwick)
- 3. Divesh Srivastava (AT&T)
BibTeX Citation
@inproceedings{cormode_sigmod18,
title = {{Marginal Release Under Local Differential Privacy}},
author = {Cormode, Graham and Kulkarni, Tejas and Srivastava, Divesh},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3196906},
url = {https://dl.acm.org/doi/10.1145/3183713.3196906},
year = {2018}
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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 |
|---|---|---|---|---|
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030654879 |
| 134 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00029947443 |
| 801 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00013824207 |
| 6,516 | Differential Privacy in the Wild: A tutorial on current practices & open challenges | 2016 | VLDB | 5.7432938e-05 |
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