Principled Evaluation of Differentially Private Algorithms using DPBench
Summary: DPBench: a principled, standardized framework for evaluating differential privacy algorithms. 15 algorithms on 27 datasets (1D/2D ranges) reveal data-dependent noise and scale/shape effects; resolves inconsistencies and benchmarks against baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Hay (Colgate University)
- 2. Ashwin Machanavajjhala (Duke University)
- 3. Gerome Miklau (University of Massachusetts Amherst)
- 4. Yan Chen (Duke University)
- 5. Dan Zhang (University of Massachusetts Amherst)
BibTeX Citation
@inproceedings{hay_sigmod16,
title = {{Principled Evaluation of Differentially Private Algorithms using DPBench}},
author = {Hay, Michael and Machanavajjhala, Ashwin and Miklau, Gerome and Chen, Yan and Zhang, Dan},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2882931},
url = {https://dl.acm.org/doi/10.1145/2882903.2882931},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,850 | Differentially Private Data Release over Multiple Tables | 2023 | PODS |
| 2 | 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB |
| 3 | 3,260 | Answering Range Queries Under Local Differential Privacy | 2019 | SIGMOD |
| 4 | 10,786 | Computing Inconsistency Measures Under Differential Privacy | 2025 | SIGMOD |
| 5 | 7,338 | DPGraph: A Benchmark Platform for Differentially Private Graph Analysis | 2021 | SIGMOD |
| 6 | 12,079 | Design of Policy-Aware Differentially Private Algorithms | 2016 | VLDB |
| 7 | 7,292 | A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries | 2022 | PODS |
| 8 | 5,008 | Exploring Privacy-Accuracy Tradeoffs using DPComp | 2016 | SIGMOD |
| 9 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 10 | 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |