论文标题
差异化学习需要更好的功能（或更多数据）
Differentially Private Learning Needs Better Features (or Much More Data)
论文作者
论文摘要
我们证明，私人机器学习尚未在许多规范视觉任务上达到其“ Alexnet时刻”：在手工特征上训练的线性模型明显优于端到端的端到端深神经网络，以实现适度的隐私预算。为了超越手工制作的功能的性能，我们表明私人学习需要更多的私人数据，或者访问从类似域中学到的公共数据中学到的功能。我们的工作介绍了简单而强大的基线，用于差异化学习，可以告知对该领域未来进步的评估。
We demonstrate that differentially private machine learning has not yet reached its "AlexNet moment" on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we show that private learning requires either much more private data, or access to features learned on public data from a similar domain. Our work introduces simple yet strong baselines for differentially private learning that can inform the evaluation of future progress in this area.
