Framework for Differentially Private Data Analysis with Multiple Accuracy Requirements
Summary: Introduces a differential privacy framework for multi-analysis with per-analysis accuracy guarantees under a fixed privacy budget. When the budget cannot satisfy all analyses, it optimizes allocation to maximize the number of analyses (or sub-analyses) that meet their accuracy targets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Karl Knopf
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,417 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 4.7355114e-05 |
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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 |
|---|---|---|---|---|
| 178 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00037697111 |
| 2,434 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 8.8278955e-05 |
| 2,465 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7518123e-05 |
| 4,502 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.1366984e-05 |
| 6,065 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 5.2291685e-05 |
| 7,619 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB | 4.6941145e-05 |
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