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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)

Paper ID
12937
Venue
VLDB
Year
2022
Pagerank
8.0637668e-05
Overall Rank
2,841 | 80.51%
DOI
10.14778/3551793.3551817

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Authors

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}
}

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