Summary of On Convex Optimization with Semi-sensitive Features, by Badih Ghazi et al.
On Convex Optimization with Semi-Sensitive Features
by Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS)
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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 novel approach to differentially private empirical risk minimization (DP-ERM) is proposed, which generalizes the Label DP setting by considering semi-sensitive features. The authors derive improved upper and lower bounds on the excess risk for DP-ERM, demonstrating that the error scales polylogarithmically in terms of the sensitive domain size, outperforming previous results with polynomial scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways to protect people’s private information when using machine learning models. Imagine a situation where some features or characteristics are more sensitive than others, and you want to keep that information private. The authors developed new methods to achieve this goal while still getting good results from the model. They showed that their approach is better than previous ones in certain situations. |
Keywords
* Artificial intelligence * Machine learning
