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 (University of Waterloo)
BibTeX Citation
@inproceedings{knopf_sigmod21,
title = {{Framework for Differentially Private Data Analysis with Multiple Accuracy Requirements}},
author = {Knopf, Karl},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450587},
url = {https://dl.acm.org/doi/10.1145/3448016.3450587},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,376 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 5.893174e-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 |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031639377 |
| 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 9.047803e-05 |
| 2,300 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7833594e-05 |
| 4,361 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.7443476e-05 |
| 5,055 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 6.3824509e-05 |
| 7,639 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB | 5.5767645e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,008 | Exploring Privacy-Accuracy Tradeoffs using DPComp | 2016 | SIGMOD |
| 2 | 8,843 | Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration | 2023 | VLDB |
| 3 | 7,968 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB |
| 4 | 2,108 | An Adaptive Mechanism for Accurate Query Answering under Differential Privacy | 2012 | VLDB |
| 5 | 6,534 | Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy | 2023 | SIGMOD |
| 6 | 6,376 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD |
| 7 | 9,602 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB |
| 8 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 9 | 7,639 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB |
| 10 | 9,603 | Multi-Analyst Differential Privacy for Online Query Answering | 2023 | VLDB |