Summary of Initial Exploration Of Zero-shot Privacy Utility Tradeoffs in Tabular Data Using Gpt-4, by Bishwas Mandal et al.
Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
by Bishwas Mandal, George Amariucai, Shuangqing Wei
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel approach is proposed to manage the privacy-utility tradeoff in tabular data using large language models (LLMs) like GPT-4. The method involves converting tabular data into text format and providing sanitization instructions to LLMs, aiming to obscure private features while preserving utility-related attributes. Various sanitization strategies are explored, revealing that this approach yields comparable performance to more complex adversarial optimization methods. Although the prompts successfully hide private features from existing machine learning models, further work is needed to meet fairness metrics. The study demonstrates the potential effectiveness of LLMs in adhering to these metrics, with some results mirroring those achieved by established techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are used to manage privacy and utility in tabular data. The approach involves converting data into text and giving instructions to the model. This helps hide private information while keeping useful information. The study shows that this method works as well as more complicated ways of doing it. While it’s a step forward, more work is needed to make sure it’s fair. |
Keywords
» Artificial intelligence » Gpt » Machine learning » Optimization
