Summary of Private Linear Regression with Differential Privacy and Pac Privacy, by Hillary Yang
Private Linear Regression with Differential Privacy and PAC Privacy
by Hillary Yang
First submitted to arxiv on: 3 Dec 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 Linear regression is a widely used statistical tool, but existing methods don’t always provide sufficient privacy guarantees. To address this, we compare two types of privacy-preserving linear regression: differential privacy and PAC Privacy. We train models on three real-world datasets and find that the choice of privacy method significantly impacts model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different ways to make sure a machine learning model doesn’t reveal too much about individual data points it was trained on. The two methods we tested are called differential privacy and PAC Privacy. Our results show that which method you use makes a big difference in how well the model works. |
Keywords
» Artificial intelligence » Linear regression » Machine learning
