Summary of Improving the Privacy and Practicality Of Objective Perturbation For Differentially Private Linear Learners, by Rachel Redberg et al.
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by Rachel Redberg, Antti Koskela, Yu-Xiang Wang
First submitted to arxiv on: 31 Dec 2023
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 This research paper explores a novel approach to differential privacy in machine learning, specifically focusing on the objective perturbation mechanism. By revamping this method with tightened privacy analyses and advanced computational tools, the authors demonstrate its competitive performance with differentially private stochastic gradient descent (DP-SGD) on complex convex generalized linear problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a special kind of math that helps keep personal data safe in machine learning models. The old way was good but not as good as another method called DP-SGD. This new approach makes the old way better by adding more math and computer tools. Now, it’s almost as good as DP-SGD for solving certain kinds of problems. |
Keywords
* Artificial intelligence * Machine learning * Stochastic gradient descent
