Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this work, we explore the benefits of VP in constructing compelling neural network classifiers with differential privacy (DP). We explore and integrate VP into canonical DP training methods and demonstrate its simplicity and efficiency. In particular, we discover that VP in tandem with PATE, a state-of-the-art DP training method that leverages the knowledge transfer from an ensemble of teachers, achieves the state-of-the-art privacy-utility trade-off with minimum expenditure of privacy budget. Moreover, we conduct additional experiments on cross-domain image classification with a sufficient domain gap to further unveil the advantage of VP in DP. Lastly, we also conduct extensive ablation studies to validate the effectiveness and contribution of VP under DP consideration.

翻译：视觉提示（Visual Prompting，VP）是一种新兴且强大的技术，能够通过工程化一个训练良好的冻结源模型，高效地实现下游任务的样本自适应。在本研究中，我们探讨了VP在构建具有差分隐私（DP）的强效神经网络分类器中的优势。我们将VP融入经典的DP训练方法中，并展示了其简洁性与高效性。特别地，我们发现VP与PATE（一种利用多教师模型知识转移的先进DP训练方法）相结合，能在最小化隐私预算投入的同时，实现当前最优的隐私-效用权衡。此外，我们通过跨域图像分类实验（设置充分的域差距），进一步揭示了VP在DP中的优势。最后，我们进行了全面的消融研究，以验证DP框架下VP的有效性与贡献。