Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy
Summary: First practical differential privacy technique for answering a batch of correlated queries via a low-rank workload. LRM nears the DP lower bound for batch queries and significantly outperforms prior approaches (Matrix Mechanism, naive) on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ganzhao Yuan
- 2. Zhenjie Zhang
- 3. Marianne Winslett
- 4. Xiaokui Xiao
- 5. Yin Yang
- 6. Zhifeng Hao
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 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 |
|---|---|---|---|---|
| 136 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.0004241101 |
| 178 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00037697111 |
| 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 |
| 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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