title: Harnessing large-language models to generate private synthetic text
summary: Differentially private (DP) training methods like DP-SGD can protect
  sensitive training data by ensuring that ML models will not reveal private
  information. An alternative approach, which this paper studies, is to use a
  sensitive dataset to generate a new synthetic dataset which is differentially
  private with respect to the original data. Doing so has several advantages:
  synthetic data can be reused for other tasks (including for hyper parameter
  tuning), retained indefinitely, or shared with third parties without
  sacrificing privacy.
    However, obtaining DP data is much harder than introducing DP during
  training. To make it feasible for text, recent work has utilized public data by
  starting with a pre-trained generative language model and privately finetuning
  it on sensitive data. This model can be used to sample a DP synthetic dataset.
  While this strategy seems straightforward, executing it has proven problematic.
  Previous approaches either show significant performance loss, or have, as we
  show, critical design flaws.
    In this paper we demonstrate that a proper training objective along with
  tuning fewer parameters results in excellent DP synthetic data quality. Our
  approach is competitive with direct DP-training of downstream classifiers in
  terms of performance on downstream tasks. We also demonstrate that our DP
  synthetic data is not only useful for downstream classifier training, but also
  to tune those same models.
paper_authors._id: 647d5421396de7684dec4480
paper_authors.name: Alexey Kurakin
paper_authors._id: 647d5421396de7684dec4481
paper_authors.name: Natalia Ponomareva
paper_authors._id: 647d5421396de7684dec4482
paper_authors.name: Umar Syed
paper_authors._id: 647d5421396de7684dec4483
paper_authors.name: Liam MacDermed
paper_authors._id: 647d5421396de7684dec4484
paper_authors.name: Andreas Terzis
paper_title: Harnessing large-language models to generate private synthetic text
paper_summary: Differentially private (DP) training methods like DP-SGD can protect
  sensitive training data by ensuring that ML models will not reveal private
  information. An alternative approach, which this paper studies, is to use a
  sensitive dataset to generate a new synthetic dataset which is differentially
  private with respect to the original data. Doing so has several advantages:
  synthetic data can be reused for other tasks (including for hyper parameter
  tuning), retained indefinitely, or shared with third parties without
  sacrificing privacy.
    However, obtaining DP data is much harder than introducing DP during
  training. To make it feasible for text, recent work has utilized public data by
  starting with a pre-trained generative language model and privately finetuning
  it on sensitive data. This model can be used to sample a DP synthetic dataset.
  While this strategy seems straightforward, executing it has proven problematic.
  Previous approaches either show significant performance loss, or have, as we
  show, critical design flaws.
    In this paper we demonstrate that a proper training objective along with
  tuning fewer parameters results in excellent DP synthetic data quality. Our
  approach is competitive with direct DP-training of downstream classifiers in
  terms of performance on downstream tasks. We also demonstrate that our DP
  synthetic data is not only useful for downstream classifier training, but also
  to tune those same models.
paper_ai_keywords: differentially private (DP) DP-SGD synthetic dataset pre-trained generative language model privately fine-tuning DP synthetic dataset training objective downstream classifiers downstream tasks performance loss critical design flaws