Summary of I Can’t See It but I Can Fine-tune It: on Encrypted Fine-tuning Of Transformers Using Fully Homomorphic Encryption, by Prajwal Panzade et al.
I can’t see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption
by Prajwal Panzade, Daniel Takabi, Zhipeng Cai
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents BlindTuner, a novel system for fine-tuning transformer models while preserving privacy. In today’s machine learning landscape, where data sharing is crucial, challenges arise when data access is limited due to strict privacy regulations or user concerns. The proposed approach focuses on training transformer models exclusively on homomorphically encrypted data for image classification tasks. The authors demonstrate the effectiveness of BlindTuner by achieving comparable accuracy to non-encrypted models while showcasing a significant speed enhancement of 1.5x to 600x over previous work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BlindTuner is a new way to train machine learning models without sharing personal information. It’s like using code to keep your data safe and private, even when it needs to be shared with others. The researchers tested this method on image classification tasks and found that it works just as well as other methods, but much faster. |
Keywords
* Artificial intelligence * Fine tuning * Image classification * Machine learning * Transformer
