Differentially Private Hierarchical Count-of-Counts Histograms
Summary: Introduces hierarchical count-of-counts histograms, counting groups by size across attribute hierarchies. Develops a differentially private release mechanism with suitable error metrics that enforces consistency across granularities. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yu-Hsuan Kuo (Pennsylvania State University)
- 2. Cho-Chun Chiu (Pennsylvania State University)
- 3. Daniel Kifer (Pennsylvania State University; United States Census Bureau)
- 4. Michael Hay (Colgate University)
- 5. Ashwin Machanavajjhala (Duke University)
BibTeX Citation
@article{kuo_vldb18,
title = {{Differentially Private Hierarchical Count-of-Counts Histograms}},
author = {Kuo, Yu-Hsuan and Chiu, Cho-Chun and Kifer, Daniel and Hay, Michael and Machanavajjhala, Ashwin},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {1509--1521},
doi = {10.14778/3236187.3236202},
url = {https://doi.org/10.14778/3236187.3236202},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,170 | QuickSel: Quick Selectivity Learning with Mixture Models | 2020 | SIGMOD | 0.00011827259 |
| 11,712 | ATLANTIC: Making Database Differentially Private and Faster with Accuracy Guarantee | 2021 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031639377 |
| 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016420715 |
| 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015747402 |
| 775 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00014110531 |
| 1,308 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011216361 |
| 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010548904 |
| 6,701 | Information Preservation in Statistical Privacy and Bayesian Estimation of Unattributed Histograms | 2013 | SIGMOD | 5.7980998e-05 |
| 7,359 | Pythia: Data Dependent Differentially Private Algorithm Selection | 2017 | SIGMOD | 5.6341162e-05 |
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|---|---|---|---|---|
| 1 | 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD |
| 2 | 6,083 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD |
| 3 | 3,121 | Answering Range Queries Under Local Differential Privacy | 2019 | VLDB |
| 4 | 2,856 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB |
| 5 | 11,632 | Data-Independent Space Partitionings for Summaries | 2021 | PODS |
| 6 | 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB |
| 7 | 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB |
| 8 | 1,308 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD |
| 9 | 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB |
| 10 | 7,831 | Differentially Private Hierarchical Heavy Hitters | 2024 | PODS |