# VeilTune

Existing secure fine-tuning protocols hand the data owner hidden representations or logits from every step, and from those the data owner can copy the provider's LoRA adapter: from the hidden representations of a single mini-batch of 32 samples, the copy agrees with the original on 99.2% of test inputs, and logits alone give about 94%. VeilTune instead runs the forward pass, backpropagation and the Adam update entirely in secure multi-party computation and reveals only the trained adapter to the model owner, averaging 5.3 points above SecP-Tuning on four few-shot tasks with RoBERTa-large and staying within 0.3 to 1.7 points of plaintext training.

Existing secure fine-tuning protocols hand the data owner hidden representations or logits from every step, and from those the data owner can copy the provider's LoRA adapter: from the hidden representations of a single mini-batch of 32 samples, the copy agrees with the original on 99.2% of test inputs, and logits alone give about 94%. VeilTune instead runs the forward pass, backpropagation and the Adam update entirely in secure multi-party computation and reveals only the trained adapter to the model owner, averaging 5.3 points above SecP-Tuning on four few-shot tasks with RoBERTa-large and staying within 0.3 to 1.7 points of plaintext training.