Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

• URL: http://arxiv.org/abs/2606.26772v1
• Date: Thu, 25 Jun 2026 08:57:16 GMT
• Title: Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
• Abstract summary: We propose a new framework for Differentially Private (DP) training of neural networks.<n>We employ a hypernetwork trained on public datasets to map a private dataset to the parameters of the target model.<n>Because privacy noise is injected only once into a low-dimensional dataset representation, our approach can significantly reduce the adverse effect of noise.
• Score: 7.085954928597583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training. In this paper, we propose a new framework for DP learning that avoids iterative optimization in parameter space. Instead of updating the target model using privatized gradients, we employ a hypernetwork trained on public datasets to map a private dataset to the parameters of the target model. Specifically, each example is embedded into a low-dimensional representation, the embeddings are aggregated and perturbed to obtain a DP dataset embedding, and the hypernetwork generates the target model parameters from this noisy embedding. Because privacy noise is injected only once into a low-dimensional dataset representation, our approach can significantly reduce the adverse effect of noise. We theoretically show in a synthetic setting that, under a fixed privacy budget, models produced by our approach achieve higher utility than those trained with DP-SGD. Moreover, we apply our approach to LoRA fine-tuning of diffusion models and show that it achieves lower FID than LoRA models trained with DP-SGD and other public-data-guided methods.

Related papers

• Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy [9.339558179603214]
  Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection.<n>Traditional LDP mechanisms inject noise uniformly across all dimensions regardless of their relative importance to the downstream objective.<n>We propose a novel approach that mitigates noise in task-relevant subspaces of the data representation.
  arXiv  Detail & Related papers  (2026-05-16T05:01:41Z)
• Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training [10.229653770070202]
  Training generative models with differential privacy (DP) typically involves injecting noise into gradient updates or adapting the discriminator's training procedure. We consider the slicing privacy mechanism that injects noise into random low-dimensional projections of the private data. We present a kernel-based estimator for this divergence, circumventing the need for adversarial training.
  arXiv  Detail & Related papers  (2024-10-25T19:32:58Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization [49.1574468325115]
  Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. Differentially private SGD [DP-SGD] requires the modification of the standard gradient descent [SGD] algorithm for training new models. A novel regularization strategy is proposed to achieve the same goal in a more efficient manner.
  arXiv  Detail & Related papers  (2024-09-25T17:59:32Z)
• Differentially Private Fine-Tuning of Diffusion Models [22.454127503937883]
  The integration of Differential Privacy with diffusion models (DMs) presents a promising yet challenging frontier. Recent developments in this field have highlighted the potential for generating high-quality synthetic data by pre-training on public data. We propose a strategy optimized for private diffusion models, which minimizes the number of trainable parameters to enhance the privacy-utility trade-off.
  arXiv  Detail & Related papers  (2024-06-03T14:18:04Z)
• Sparsity-Preserving Differentially Private Training of Large Embedding Models [67.29926605156788]
  DP-SGD is a training algorithm that combines differential privacy with gradient descent. Applying DP-SGD naively to embedding models can destroy gradient sparsity, leading to reduced training efficiency. We present two new algorithms, DP-FEST and DP-AdaFEST, that preserve gradient sparsity during private training of large embedding models.
  arXiv  Detail & Related papers  (2023-11-14T17:59:51Z)
• Differential Privacy Meets Neural Network Pruning [10.77469946354744]
  We study the interplay between neural network pruning and differential privacy, through the two modes of parameter updates. Our experimental results demonstrate how decreasing the parameter space improves differentially private training. By studying two popular forms of pruning which do not rely on gradients and do not incur an additional privacy loss, we show that random selection performs on par with magnitude-based selection.
  arXiv  Detail & Related papers  (2023-03-08T14:27:35Z)
• Differentially Private Diffusion Models [46.46256537222917]
  We build on the recent success of diffusion models (DMs) and introduce Differentially Private Diffusion Models (DPDMs) We propose noise multiplicity, a powerful modification of DP-SGD tailored to the training of DMs. We validate our novel DPDMs on image generation benchmarks and achieve state-of-the-art performance in all experiments.
  arXiv  Detail & Related papers  (2022-10-18T15:20:47Z)
• Large Scale Transfer Learning for Differentially Private Image Classification [51.10365553035979]
  Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. Private training using DP-SGD protects against leakage by injecting noise into individual example gradients. While this result is quite appealing, the computational cost of training large-scale models with DP-SGD is substantially higher than non-private training.
  arXiv  Detail & Related papers  (2022-05-06T01:22:20Z)
• Scaling Structured Inference with Randomization [64.18063627155128]
  We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference. It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
  arXiv  Detail & Related papers  (2021-12-07T11:26:41Z)
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence [73.14373832423156]
  We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. Unlike existing approaches for training differentially private generative models, we do not rely on adversarial objectives.
  arXiv  Detail & Related papers  (2021-11-01T18:10:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.