Summary of On the Privacy Risk Of In-context Learning, by Haonan Duan et al.
On the Privacy Risk of In-context Learning
by Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, Franziska Boenisch
First submitted to arxiv on: 15 Nov 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 The proposed study demonstrates that deploying large language models (LLMs) with natural language prompts poses significant privacy risks to the data used within those prompts. Specifically, it shows that LLMs can be vulnerable to highly effective membership inference attacks, which could compromise private datasets. The researchers observe that this risk exceeds that of fine-tuned models at similar utility levels. They attribute this increased risk to the model’s sensitivity to its prompts, which leads to higher prediction confidence on prompted data. To mitigate this risk, they propose ensembling different versions of a prompted model, which can decrease membership inference risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at learning new things quickly. They can do many tasks just by being given simple instructions written in everyday language. These instructions often contain secret information about a specific project or goal that someone wants to achieve. The researchers found that using these language models in this way creates a big problem for keeping private data safe. They showed that an attacker could easily figure out if certain data was used to train the model, which is bad news for people who want to keep their data private. To make things safer, they suggest combining many different versions of the model together, which can help hide sensitive information. |
Keywords
» Artificial intelligence » Inference
