Boosting the Accuracy of Differentially Private Histograms Through Consistency
Summary: Improves differentially private histogram accuracy by selecting informative queries and projecting noisy answers onto consistency constraints via maximum-likelihood post-processing. Demonstrates precise graph degree-sequence estimation and accurate arbitrary range queries while preserving privacy. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Hay (University of Massachusetts Amherst)
- 2. Vibhor Rastogi (University of Washington)
- 3. Gerome Miklau (University of Massachusetts Amherst)
- 4. Dan Suciu (University of Washington)
BibTeX Citation
@article{hay_vldb10,
title = {{Boosting the Accuracy of Differentially Private Histograms Through Consistency}},
author = {Hay, Michael and Rastogi, Vibhor and Miklau, Gerome and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '10},
volume = {3},
number = {1},
pages = {1021--1032},
doi = {10.14778/1920841.1920974},
url = {https://doi.org/10.14778/1920841.1920974},
year = {2010}
}Incoming Citations (Sorted by Pagerank)
Showing 26 of 76 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038378358 |
| 134 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00029947443 |
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