Summary of Sok: a Review Of Differentially Private Linear Models For High-dimensional Data, by Amol Khanna and Edward Raff and Nathan Inkawhich
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
by Amol Khanna, Edward Raff, Nathan Inkawhich
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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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 This paper presents a systematic comparison of optimization techniques for high-dimensional differentially private linear models, aiming to guarantee the privacy of training data. The authors review existing methods, including robust and coordinate-optimized algorithms, which are found to perform best in empirical tests. The study contributes to future research in this area by providing a comprehensive overview of the methods and their relative performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different ways to train linear models when you want to keep your data private. It shows that some methods work better than others at balancing accuracy with privacy protection. The researchers tested many methods and found that certain approaches, like using multiple coordinates, perform best. This information can help other researchers in the field. |
Keywords
* Artificial intelligence * Optimization
