mlsanityData to 14 Oct 2026

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

arXiv 2403.07797 ↗First posted 12 Mar 2024cs.LG cs.AIcited by 8

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Abstract (arXiv)

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that jam-pgm is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

20 of 56 references.

References · 20

  1. 2021Differentially Private Fine-tuning of Language Modelscited by 400
  2. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  3. 2016Concentrated Differential Privacy: Simplifications, Extensions, and Lower Boundscited by 346
  4. 2018Differentially Private Generative Adversarial Networkcited by 235
  5. 2020The Discrete Gaussian for Differential Privacycited by 135
  6. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 102
  7. 2020Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identificationcited by 90
  8. 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 90
  9. 2019Graphical-model based estimation and inference for differential privacycited by 74
  10. 2021Differentially Private Query Release Through Adaptive Projectioncited by 52
  11. 2020New Oracle-Efficient Algorithms for Private Synthetic Data Releasecited by 43
  12. 2019Limits of Private Learning with Access to Public Datacited by 39
8 more
  1. 2021Leveraging Public Data for Practical Private Query Releasecited by 39
  2. 2020Private Query Release Assisted by Public Datacited by 27
  3. 2014Dual Query: Practical Private Query Release for High Dimensional Datacited by 26
  4. 2020Kamino: Constraint-Aware Differentially Private Data Synthesiscited by 25
  5. 2022Fully Adaptive Composition in Differential Privacycited by 22
  6. 2016Comparative Study of Differentially Private Data Synthesis Methodscited by 13
  7. 2015Principled Evaluation of Differentially Private Algorithms using DPBenchcited by 6
  8. 2021Relaxed Marginal Consistency for Differentially Private Query Answeringcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 1.

8 papers.

  1. 2025InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacycited by 10
  2. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 9
  3. 2025Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?cited by 5
  4. 2025RAPID: Retrieval Augmented Training of Differentially Private Diffusion Modelscited by 3
  5. 2025Leveraging Vertical Public-Private Split for Improved Synthetic Data Generationcited by 2
  6. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  7. 2025Lower Bounds for Public-Private Learning under Distribution Shiftcited by 0
  8. 2025Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesiscited by 0