Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms
Summary: Free-gap from Noisy Max: release the noisy gap to the runner-up at no extra privacy cost, boosting downstream counting accuracy by up to 50%. Sparse Vector: adaptively budget privacy, spending less on queries well above threshold to process more queries, via a careful privacy analysis. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zeyu Ding (Pennsylvania State University)
- 2. Yuxin Wang (Pennsylvania State University)
- 3. Danfeng Zhang (Pennsylvania State University)
- 4. Daniel Kifer (Pennsylvania State University)
BibTeX Citation
@article{ding_vldb20,
title = {{Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms}},
author = {Ding, Zeyu and Wang, Yuxin and Zhang, Danfeng and Kifer, Daniel},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {293--306},
doi = {10.14778/3368289.3368295},
url = {https://doi.org/10.14778/3368289.3368295},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,783 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB | 5.1285234e-05 |
| 11,316 | Calibrating Noise for Group Privacy in Subsampled Mechanisms | 2025 | VLDB | 4.9769913e-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 |
|---|---|---|---|---|
| 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038504258 |
| 276 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022332903 |
| 2,058 | Understanding the Sparse Vector Technique for Differential Privacy | 2017 | VLDB | 9.1069458e-05 |
| 4,414 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.6080858e-05 |
| 5,003 | Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics | 2017 | SIGMOD | 6.3175519e-05 |
| 7,506 | Pythia: Data Dependent Differentially Private Algorithm Selection | 2017 | SIGMOD | 5.5051028e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 800 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD |
| 2 | 11,611 | Confidence Intervals for Private Query Processing | 2024 | VLDB |
| 3 | 8,139 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB |
| 4 | 9,783 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB |
| 5 | 10,861 | Fast and Private Max-Sum Diversification | 2026 | VLDB |
| 6 | 2,129 | An Adaptive Mechanism for Accurate Query Answering under Differential Privacy | 2012 | VLDB |
| 7 | 626 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS |
| 8 | 1,530 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |
| 9 | 2,058 | Understanding the Sparse Vector Technique for Differential Privacy | 2017 | VLDB |
| 10 | 9,240 | Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise | 2025 | VLDB |