Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release
Summary: Efficient method to release contingency tables/OLAP cubes that simultaneously ensures differential privacy, accuracy, and consistency by projecting noisy private marginals to the nearest consistent set. Proves this post-processing preserves privacy and never increases error beyond the original mechanism. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Boaz Barak (Princeton University)
- 2. Kamalika Chaudhuri (University of California Berkeley)
- 3. Cynthia Dwork (Microsoft)
- 4. Satyen Kale (Princeton University)
- 5. Frank McSherry (Microsoft)
- 6. Kunal Talwar (Microsoft)
BibTeX Citation
@inproceedings{barak_pods07,
address = {New York, NY, USA},
series = {{PODS} '07},
title = {{Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release}},
url = {https://dl.acm.org/doi/10.1145/1265530.1265569},
doi = {10.1145/1265530.1265569},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Barak, Boaz and Chaudhuri, Kamalika and Dwork, Cynthia and Kale, Satyen and McSherry, Frank and Talwar, Kunal},
year = {2007}
}Incoming Citations (Sorted by Pagerank)
Showing 44 of 44 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 68 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00037958605 |
| 122 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030770793 |
| 226 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00023984903 |
| 254 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023020039 |
| 520 | Practical Privacy: The SuLQ Framework | 2005 | PODS | 0.00016939312 |
| 1,097 | Privacy Preserving OLAP | 2005 | SIGMOD | 0.00012041002 |
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