iReduct: Differential Privacy with Reduced Relative Errors
Summary: iReduct provides differential privacy with reduced relative errors by allocating noise adaptively across query results. A novel resampling-based correlated-noise technique improves utility for small vs large answers, demonstrated on marginals of multi-dimensional histograms with real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaokui Xiao (Nanyang Technological University)
- 2. Gabriel Bender (Cornell University)
- 3. Michael Hay (Cornell University)
- 4. Johannes Gehrke (Cornell University)
BibTeX Citation
@inproceedings{xiao_sigmod11,
title = {{iReduct: Differential Privacy with Reduced Relative Errors}},
author = {Xiao, Xiaokui and Bender, Gabriel and Hay, Michael and Gehrke, Johannes},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989348},
url = {https://dl.acm.org/doi/10.1145/1989323.1989348},
year = {2011}
}
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031639377 |
| 130 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00030604781 |
| 213 | Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets | 1999 | SIGMOD | 0.00024723025 |
| 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015747402 |
| 757 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00014306168 |
| 4,732 | Optimal Random Perturbation at Multiple Privacy Levels | 2009 | VLDB | 6.5343833e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,198 | Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity | 2021 | VLDB |
| 2 | 6,666 | Residual Sensitivity for Differentially Private Multi-Way Joins | 2021 | SIGMOD |
| 3 | 11,280 | Confidence Intervals for Private Query Processing | 2024 | VLDB |
| 4 | 4,132 | Answering Multi-Dimensional Range Queries under Local Differential Privacy | 2021 | VLDB |
| 5 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 6 | 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB |
| 7 | 775 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD |
| 8 | 11,203 | Personalized Truncation for Personalized Privacy | 2024 | SIGMOD |
| 9 | 3,935 | Output Perturbation with Query Relaxation | 2008 | VLDB |
| 10 | 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |