Summary of Efficient Sparse Least Absolute Deviation Regression with Differential Privacy, by Weidong Liu et al.
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
by Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 proposes a fast and privacy-preserving learning solution for sparse robust regression problems. The authors develop an algorithm called Fast Robust And Privacy-Preserving Estimation (FRAPPE) that can handle non-smooth loss functions, unlike most existing privacy-preserving algorithms. FRAPPE achieves a good trade-off between privacy and statistical accuracy by reformulating the sparse least absolute deviation problem as a penalized least square estimation problem and injecting noise to guarantee differential privacy. The algorithm is shown to outperform state-of-the-art methods in both privacy and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us keep our data private while still doing important science work. It’s like having a secret recipe that keeps your information safe, but still lets you make discoveries! The authors came up with a new way to solve a problem where we want to learn something about the world, but we don’t want people to know too much about what we’re learning. They called it FRAPPE, and it’s really good at balancing keeping things private with making sure our answers are accurate. |
Keywords
* Artificial intelligence * Regression
