Differentially Private Representation Learning via Image Captioning

Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barrier to achieving this ideal is its sub-optimal privacy-accuracy trade-off, which is particularly visible in DP representation learning. Specifically, it has been shown that under modest privacy budgets, most models learn representations that are not significantly better than hand-crafted features. In this work, we show that effective DP representation learning can be done via image captioning and scaling up to internet-scale multimodal datasets. Through a series of engineering tricks, we successfully train a DP image captioner (DP-Cap) on a 233M subset of LAION-2B from scratch using a reasonable amount of computation, and obtaining unprecedented high-quality image features that can be used in a variety of downstream vision and vision-language tasks. For example, under a privacy budget of $\varepsilon=8$ for the LAION dataset, a linear classifier trained on top of learned DP-Cap features attains $65. 8%$ accuracy on ImageNet-1K, considerably improving the previous SOTA of $56. 5%$. Our work challenges the prevailing sentiment that high-utility DP representation learning cannot be achieved by training from scratch.

Authors

• Tom Sander
• Yaodong Yu
• Maziar Sanjabi
• Alain Durmus
• Yi Ma 0001
• Kamalika Chaudhuri
• Chuan Guo 0001

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