An Adaptive Mechanism for Accurate Query Answering under Differential Privacy
Summary: Adaptive mechanism for counting queries under (ε,δ)-DP; automatically selects a private strategy set from the workload. Approximates the optimal strategy for any linear workload; yields near-optimal error with no extra privacy cost, beating prior methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chao Li
- 2. Gerome Miklau
Incoming Citations (Sorted by Pagerank)
Showing 15 of 15 citing papers.
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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 |
|---|---|---|---|---|
| 83 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00053933811 |
| 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 |
| 742 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00017360873 |
| 878 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00015702437 |
| 2,776 | iReduct: Differential Privacy with Reduced Relative Errors | 2011 | SIGMOD | 8.1326122e-05 |
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