Summary of Towards Quantifying the Privacy Of Redacted Text, by Vaibhav Gusain et al.
Towards Quantifying The Privacy Of Redacted Text
by Vaibhav Gusain, Douglas Leith
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel approach to evaluating the privacy of redacted text using transformer-based deep learning networks. The method reconstructs original texts from redacted versions by generating multiple consistent full texts that capture sentence similarity. This allows for estimating the number, diversity, and quality of full texts consistent with the redacted text, ultimately enabling privacy evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out what a censored document really says. You might try to fill in the gaps, but it’s hard to know if your version is correct or not. This paper helps solve this problem by using powerful AI models to create many different versions of the original text that make sense and are similar to each other. By looking at these versions, we can estimate how good our guesses are and get a better idea of what the original text says. |
Keywords
» Artificial intelligence » Deep learning » Transformer
