Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
arXiv:2606.26772v1 Announce Type: new 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, eac
The increasing scrutiny on data privacy in AI necessitates innovative approaches to training, and this research addresses a core limitation of existing differentially private methods.
This development could significantly advance training methods for privacy-preserving AI, making it more practical for real-world applications without sacrificing model utility or privacy guarantees.
The proposed hypernetwork framework offers a way to achieve differential privacy in neural networks without the common performance degradation associated with iterative gradient noise.
- · AI researchers
- · Privacy-focused AI companies
- · Sectors handling sensitive data (healthcare, finance)
- · Traditional DP-SGD methods (if this proves more effective)
- · Companies unable to implement similar advanced privacy techniques
Improved performance and broader adoption of differentially private AI models.
Reduced regulatory hurdles for AI deployment in sensitive domains due to enhanced privacy guarantees.
Increased public trust in AI applications that handle personal data, potentially accelerating AI integration across industries.
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Read at arXiv cs.LG