Summary of Safe Training with Sensitive In-domain Data: Leveraging Data Fragmentation to Mitigate Linkage Attacks, by Mariia Ignashina et al.
Safe Training with Sensitive In-domain Data: Leveraging Data Fragmentation To Mitigate Linkage Attacks
by Mariia Ignashina, Julia Ive
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a safer approach to training text generation models by sharing domain-specific short phrases randomly grouped together instead of full texts. This method, called “fragmented data,” prevents sensitive information from being reproduced in one sequence, mitigating the risk of linkage attacks. The authors fine-tune several state-of-the-art language models using this fragmented data and demonstrate their utility for classification tasks, such as predicting cardiovascular diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem with text generation models that can leak sensitive information. They do this by breaking down big texts into small pieces and mixing them up. This makes it hard for the model to put the pieces back together again, so even if someone gets some of the text fragments, they won’t be able to figure out what the original text was about. |
Keywords
» Artificial intelligence » Classification » Text generation
