Optimizing Fitness-For-Use of Differentially Private Linear Queries
Summary: Treats DP for linear queries with per-query accuracy, noting matrix mechanisms optimize total error rather than per-query usefulness. Proposes Gaussian-noise strategy with optimized covariance to meet per-query accuracy while minimizing privacy cost. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yingtai Xiao
- 2. Zeyu Ding
- 3. Yuxin Wang
- 4. Danfeng Zhang
- 5. Daniel Kifer
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,417 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 4.7355114e-05 |
| 8,837 | Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration | 2023 | VLDB | 4.4393184e-05 |
| 9,512 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB | 4.3335882e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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