Summary of Membership Inference Attacks Cannot Prove That a Model Was Trained on Your Data, by Jie Zhang et al.
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data
by Jie Zhang, Debeshee Das, Gautam Kamath, Florian Tramèr
First submitted to arxiv on: 29 Sep 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 paper addresses the issue of verifying whether a machine learning model was trained on a specific dataset. This is crucial in recent lawsuits involving foundation models trained on vast amounts of web data. Prior approaches have suggested using membership inference attacks to prove training data, but this method is flawed as it requires demonstrating a low false positive rate without knowing the exact contents of the training set or being able to efficiently retrain a large foundation model. The paper highlights two potential paths forward for creating sound training data proofs: leveraging data extraction attacks and membership inference on special “canary” data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure someone knows that a machine learning model was trained using certain data. This is important in court cases involving big models trained from the internet. People have suggested using techniques to figure out if a model was trained on some data, but this approach has a major problem: it’s hard to know how often the technique makes mistakes without knowing what’s in the training data or being able to train a new big model. The paper suggests two ways to fix this issue. |
Keywords
» Artificial intelligence » Inference » Machine learning
