Summary of Adaptively Private Next-token Prediction Of Large Language Models, by James Flemings et al.
Adaptively Private Next-Token Prediction of Large Language Models
by James Flemings, Meisam Razaviyayn, Murali Annavaram
First submitted to arxiv on: 2 Oct 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 recent development in Large Language Models (LLMs) has led to an increased focus on ensuring their privacy. One approach involves training LLMs differentially privately, but this method is computationally expensive and can compromise model utility. To address this issue, a Machine Learning as a Service (MLaaS) provider can privatize predictions during the decoding process. However, previous solutions have limitations, including Private Mixing of Ensemble Distributions (PMixED), which must satisfy a fixed privacy level for a given number of queries. Our proposed solution, Adaptive PMixED (AdaPMixED), is a private decoding framework that adapts to the private and public output distributions evaluated on a given input query. We introduce a noisy screening mechanism and data-dependent analysis to reduce privacy loss while preserving model utility. Experimental evaluations show that AdaPMixED can reduce privacy loss by 16x while maintaining strong utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent breakthroughs in Large Language Models (LLMs) have led to concerns about protecting their privacy. Imagine if someone could access your private conversations just because they asked nicely! To keep this from happening, we need new ways to make LLMs more secure. One approach is to train them differently so that they don’t reveal too much information. However, this method can be very slow and make the models less useful. A better solution might be to privatize the predictions when someone asks a question. But there are still some problems with this approach. Our team has come up with a new way to do this called AdaPMixED. It’s like a special filter that reduces how much information is shared while still keeping the model useful. We tested it and found that it can make LLMs 16 times more private while still working well. |
Keywords
* Artificial intelligence * Machine learning
