AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Summary: AIM is a workload-adaptive DP synthetic-data generator that iteratively selects measurements, privately measures them, and yields data from noisy results. It links measurement choice to workload relevance and data-approximation, provides high-probability per-query error bounds, and outperforms existing DP mechanisms. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan McKenna (University of Massachusetts Amherst)
- 2. Brett Mullins (University of Massachusetts Amherst)
- 3. Daniel Sheldon (University of Massachusetts Amherst)
- 4. Gerome Miklau (University of Massachusetts Amherst)
BibTeX Citation
@article{mckenna_vldb22,
title = {{AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data}},
author = {McKenna, Ryan and Mullins, Brett and Sheldon, Daniel and Miklau, Gerome},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2599--2612},
doi = {10.14778/3551793.3551817},
url = {https://doi.org/10.14778/3551793.3551817},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00031082693 |
| 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 |
| 2,300 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7833594e-05 |
| 2,476 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 8.5258582e-05 |
| 3,016 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB | 7.8484858e-05 |
| 3,412 | Kamino: Constraint-Aware Differentially Private Data Synthesis | 2021 | VLDB | 7.4344343e-05 |
| 7,924 | PSynDB: Accurate and Accessible Private Data Generation | 2019 | VLDB | 5.5181056e-05 |
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