# Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model

arXiv: https://arxiv.org/abs/2502.05505 · HTML: https://mlsanity.com/paper/2502.05505/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Privacy-preserving tabular data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 37 of its 66 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Privacy-preserving tabular data synthesis](https://mlsanity.com/synth/privacy-preserving-tabular-data-synthesis/)

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Large Language Models Can Be Strong Differentially Private Learners](https://mlsanity.com/paper/2110.05679/) (2021)
- [Differentially Private Fine-tuning of Language Models](https://mlsanity.com/paper/2110.06500/) (2021)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Meta-Sim: Learning to Generate Synthetic Datasets](https://mlsanity.com/paper/1904.11621/) (2019)
- [Differentially Private Diffusion Models](https://mlsanity.com/paper/2210.09929/) (2022)
- [GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators](https://mlsanity.com/paper/2006.08265/) (2020)
- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [Differentially Private Diffusion Models Generate Useful Synthetic Images](https://mlsanity.com/paper/2302.13861/) (2023)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation](https://mlsanity.com/paper/2002.11603/) (2020)
- [Differentially Private Synthetic Data via Foundation Model APIs 2: Text](https://mlsanity.com/paper/2403.01749/) (2024)
- [Differentially Private Synthetic Data via Foundation Model APIs 1: Images](https://mlsanity.com/paper/2305.15560/) (2023)
- [Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation](https://mlsanity.com/paper/2008.09092/) (2020)
- [Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence](https://mlsanity.com/paper/2111.01177/) (2021)
- [Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping](https://mlsanity.com/paper/2212.01539/) (2022)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [DP-LDMs: Differentially Private Latent Diffusion Models](https://mlsanity.com/paper/2305.15759/) (2023)
- [PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs](https://mlsanity.com/paper/2406.02958/) (2024)
- [Pre-trained Perceptual Features Improve Differentially Private Image Generation](https://mlsanity.com/paper/2205.12900/) (2022)

## Built on by (most cited first, 13 of 13)

- [POPri: Private Federated Learning using Preference-Optimized Synthetic Data](https://mlsanity.com/paper/2504.16438/) (2025)
- [DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis](https://mlsanity.com/paper/2503.14681/) (2025)
- [Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries](https://mlsanity.com/paper/2506.07555/) (2025)
- [Struct-Bench: A Benchmark for Differentially Private Structured Text Generation](https://mlsanity.com/paper/2509.10696/) (2025)
- [Clustering and Median Aggregation Improve Differentially Private Inference](https://mlsanity.com/paper/2506.04566/) (2025)
- [Private Evolution Converges](https://mlsanity.com/paper/2506.08312/) (2025)
- [Differentially Private Synthetic Data via APIs 4: Tabular Data](https://mlsanity.com/paper/2606.08259/) (2026)
- [From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum](https://mlsanity.com/paper/2601.06368/) (2026)
- [DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning](https://mlsanity.com/paper/2602.18633/) (2026)
- [PE-means: Improved Differentially Private k-means Clustering through Private Evolution](https://mlsanity.com/paper/2606.00342/) (2026)
- [Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data](https://mlsanity.com/paper/2606.16952/) (2026)
- [QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing](https://mlsanity.com/paper/2609.17995/) (2026)
- [FinancialAuditBench: Benchmark Construction under Differential Privacy Using Real-World Priors](https://mlsanity.com/paper/2609.32835/) (2026)

## Abstract (arXiv)

Differentially private (DP) synthetic data enables the use of sensitive datasets while providing strong privacy guarantees. Recently, Private Evolution (PE) has emerged as a promising framework for generating DP synthetic data using only inference APIs of foundation models. However, suitable foundation models are not always available for every private data domain. In this paper, we show that PE is more general than previously understood and can incorporate APIs beyond foundation models. In particular, we show that state-of-the-art non-neural-network data synthesizers, including computer graphics-based image generators and physics simulation tools (which we refer to as simulators), integrate naturally into PE. We call the resulting method Sim-PE. Sim-PE broadens the applicability of PE while improving performance. For image synthesis, Sim-PE improves downstream classification accuracy by up to 3x, reduces FID by up to 80%, and is significantly more efficient. We further show that simulators and foundation models can be seamlessly combined within Sim-PE for additional gains. Beyond performance improvements, Sim-PE enables new DP applications that are difficult or impossible for existing methods, including generating DP control signals whose execution reproduces the distribution of private observations. The code is open-sourced in the Private Evolution Python library: https://github.com/microsoft/DPSDA