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
- 2. Gabriel Bender
- 3. Michael Hay
- 4. Johannes Gehrke
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 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 |
|---|---|---|---|---|
| 111 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00047073785 |
| 178 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00037697111 |
| 273 | Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets | 1999 | SIGMOD | 0.00029390945 |
| 715 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00017725693 |
| 742 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00017360873 |
| 4,794 | Optimal Random Perturbation at Multiple Privacy Levels | 2009 | VLDB | 5.9161511e-05 |
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