Differentially Private Image Classification from Features

Understand

Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP).

• Moreover, somewhat surprisingly, recent works have found that privately training just the last layer of a pre-trained model provides the best utility with DP.
• While past studies largely rely on algorithms like DP-SGD for training large models, in the specific case of privately learning from features, we observe that computational burden is low enough to allow for more sophisticated optimization schemes, including second-order methods.
• To that end, we systematically explore the effect of design parameters such as loss function and optimization algorithm.