Multi-Analyst Differential Privacy for Online Query Answering
Summary: Extends multi-analyst DP budget sharing from offline to online query answering, where future queries are unknown. Proves query-order uncertainty fundamentally limits equitable guarantees, then proposes a universally safe mechanism and randomized-order approach enabling unbounded-query mechanisms. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. David Pujol (Duke University)
- 2. Albert Sun (Duke University)
- 3. Brandon Fain (Duke University)
- 4. Ashwin Machanavajjhala (Duke University)
BibTeX Citation
@article{pujol_vldb23,
title = {{Multi-Analyst Differential Privacy for Online Query Answering}},
author = {Pujol, David and Sun, Albert and Fain, Brandon and Machanavajjhala, Ashwin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {816--828},
doi = {10.14778/3574245.3574265},
url = {https://doi.org/10.14778/3574245.3574265},
year = {2023}
}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 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 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 |
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00031082693 |
| 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016420715 |
| 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015747402 |
| 775 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00014110531 |
| 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 9.047803e-05 |
| 4,881 | Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics | 2017 | SIGMOD | 6.465615e-05 |
| 7,639 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB | 5.5767645e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,898 | A workload-adaptive mechanism for linear queries under local differential privacy | 2020 | VLDB |
| 2 | 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS |
| 3 | 7,850 | Differentially Private Data Release over Multiple Tables | 2023 | PODS |
| 4 | 7,292 | A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries | 2022 | PODS |
| 5 | 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD |
| 6 | 9,604 | Framework for Differentially Private Data Analysis with Multiple Accuracy Requirements | 2021 | SIGMOD |
| 7 | 6,534 | Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy | 2023 | SIGMOD |
| 8 | 9,602 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB |
| 9 | 2,108 | An Adaptive Mechanism for Accurate Query Answering under Differential Privacy | 2012 | VLDB |
| 10 | 7,639 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB |