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AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data

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arxiv 2201.12677 v2 pith:XPHJXGGH submitted 2022-01-29 cs.DB

Ryan McKenna , Brett Mullins , Daniel Sheldon , Gerome Miklau This is my paper · ORCID
classification cs.DB
keywords datasyntheticalgorithmdifferentiallymeasurementsprivatequeriesvariety
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?

    cs.LG 2025-02 conditional novelty 6.0 of 10

    API access to Gemini 1.0 Pro does not improve differentially private synthetic tabular data beyond established non-LLM baselines on the tested datasets and workloads.

  2. How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

    cs.CR 2025-12 conditional novelty 2.0 of 10

    A practical, extremely thorough survey of differentially private synthetic data generation: methods, privacy units, evaluation metrics, and end-to-end system components across four data modalities.

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