Summary of Scaling Up the Banded Matrix Factorization Mechanism For Differentially Private Ml, by Ryan Mckenna
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML
by Ryan McKenna
First submitted to arxiv on: 24 May 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 The proposed paper presents techniques to scale up the state-of-the-art correlated noise mechanism, DP-BandMF, for large-scale private machine learning. This method optimizes the balance between privacy amplification and noise correlation, but is currently limited by scalability issues in handling large-scale training scenarios with millions of model parameters and thousands of training iterations. The paper aims to overcome these limitations and extend the applicability of DP-BandMF to virtually any scale, while maintaining negligible utility degradation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to make private machine learning work better for big datasets. Researchers have found that adding special kinds of noise, like DP-BandMF, can help keep data safe while still allowing computers to learn from it. But this method has one problem: it gets too slow when dealing with really big datasets. The goal of this project is to make it possible to use DP-BandMF on any size dataset, without sacrificing its ability to protect privacy. |
Keywords
» Artificial intelligence » Machine learning
