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Status: Unchecked ×Keyword: differential privacy ×Clear all

2 claims from 1 paper

1. Computer Science › Privacy-Preserving Technologies in Data

   Differentially Private Fine-tuning of Language Models

   Da Yu, Naik, Bačkurs et al. · Journal of Privacy and Confidentiality · 2024

   The paper presents simpler, sparser and faster methods for differentially private fine-tuning of large pre-trained language models, reporting better privacy-utility trade-offs on many standard NLP tasks.

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   1. Unchecked“Our experiments show that differentially private adaptations of these approaches outperform previous private algorithms in three important dimensions: utility, privacy, and the computational and memory cost of private training.”
   2. UncheckedOn the MNLI task, private fine-tuning reaches 87.8% accuracy with RoBERTa-Large and 83.5% with RoBERTa-Base at a privacy budget of ε = 6.7.“For example, on the MNLI dataset we achieve an accuracy of $87.8\%$ using RoBERTa-Large and $83.5\%$ using RoBERTa-Base with a privacy budget of $ε= 6.7$.”