  We study the problem of differentially private (DP) fine-tuning of large
pre-trained models -- a recent privacy-preserving approach suitable for solving
downstream tasks with sensitive data. Existing work has demonstrated that high
accuracy is possible under strong privacy constraint, yet requires significant
computational overhead or modifications to the network architecture. We propose
differentially private bias-term fine-tuning (DP-BiTFiT), which matches the
state-of-the-art accuracy for DP algorithms and the efficiency of the standard
BiTFiT. DP-BiTFiT is model agnostic (not modifying the network architecture),
parameter efficient (only training about 0.1% of the parameters), and
computation efficient (almost removing the overhead caused by DP, in both the
time and space complexity). On a wide range of tasks, DP-BiTFiT is 2~30X faster
and uses 2~8X less memory than DP full fine-tuning, even faster than the
standard full fine-tuning. This amazing efficiency enables us to conduct DP
fine-tuning on language and vision tasks with long-sequence texts and
high-resolution images, which were computationally difficult using existing
methods. We open-source our code at FastDP
(https://github.com/awslabs/fast-differential-privacy).
