Feb. 15, 2024, 5:42 a.m. | Prajwal Panzade, Daniel Takabi, Zhipeng Cai

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09059v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenges cs.ai cs.cr cs.lg data data sharing encryption fine-tuning homomorphic encryption landscape machine machine learning obstacles surface training training data transformer transformer models transformers type

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