I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption
<|reference_start|>I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption: In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited. However, challenges surface when data sharing encounters obstacles due to stringent privacy regulations or user apprehension regarding personal information disclosure. Earlier works based on secure multiparty computation (SMC) and fully homomorphic encryption (FHE) for privacy-preserving machine learning (PPML) focused more on privacy-preserving inference than privacy-preserving training. In response, we introduce BlindTuner, a privacy-preserving fine-tuning system that enables transformer training exclusively on homomorphically encrypted data for image classification. Our extensive experimentation validates BlindTuner's effectiveness by demonstrating comparable accuracy to non-encrypted models. Notably, our findings highlight a substantial speed enhancement of 1.5x to 600x over previous work in this domain.<|reference_end|>
@article{panzade2024i,
    title={I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of
  Transformers using Fully Homomorphic Encryption},
    author={Prajwal Panzade, Daniel Takabi, Zhipeng Cai},
    journal={arXiv preprint arXiv:2402.09059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09059},
    primaryClass={cs.LG cs.AI cs.CR}
}