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 enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the diffusion model%2C which is pre-trained on public data and fully fine-tuned with differential privacy on private data%2C can train a downstream classifier%2C while achieving a good privacy-utility tradeoff. However%2C fully fine-tuning such large diffusion models with DP-SGD can be very resource-demanding in terms of memory usage and computation. In this work%2C we investigate Parameter-Efficient Fine-Tuning (PEFT) of diffusion models using Low-Dimensional Adaptation (LoDA) with Differential Privacy. We evaluate the proposed method with the MNIST and CIFAR-10 datasets and demonstrate that such efficient fine-tuning can also generate useful synthetic samples for training downstream classifiers%2C with guaranteed privacy protection of fine-tuning data. Our source code will be made available on GitHub.?quality=80&w=800)
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