Summary of Idt: Dual-task Adversarial Attacks For Privacy Protection, by Pedro Faustini et al.
IDT: Dual-Task Adversarial Attacks for Privacy Protection
by Pedro Faustini, Shakila Mahjabin Tonni, Annabelle McIver, Qiongkai Xu, Mark Dras
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); 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 novel paper explores a novel adaptation of adversarial attack techniques to manipulate text and deceive classifiers while keeping predictions unchanged for another task. It proposes IDT, a method that identifies important tokens to change for the privacy task and keeps others for utility. The paper evaluates different NLP datasets and shows that IDT retains utility while outperforming existing methods in deceiving classifiers for the privacy task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps protect private information by rewriting text to hide sensitive attributes like author gender or location. It uses a new way of manipulating text, called IDT, which keeps important parts useful for their original intention. The method looks at predictions made by auxiliary models to figure out what tokens to change and what to keep the same. This is different from previous methods that often create very different texts or have problems like mode collapse. The paper shows that IDT works well on various NLP tasks. |
Keywords
* Artificial intelligence * Nlp
