Summary of Privacy For Free in the Over-parameterized Regime, by Simone Bombari and Marco Mondelli
Privacy for Free in the Over-Parameterized Regime
by Simone Bombari, Marco Mondelli
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 Differentially private gradient descent (DP-GD) is a well-known algorithm for training deep learning models with provable guarantees on data privacy. The research community has extensively studied the performance cost of DP-GD compared to standard gradient descent, deriving upper bounds on the excess population risk in various learning settings. However, existing bounds typically deteriorate as the number of parameters grows beyond the number of training samples, a common scenario in modern deep-learning practice. This lack of theoretical insights leaves practitioners without clear guidance, leading some to reduce model complexity or use larger models to achieve better results. In this work, we show that in the random features model with quadratic loss, privacy can be achieved at no cost when the number of parameters is sufficiently large, even in strongly private settings. This challenges the conventional wisdom that over-parameterization inherently hinders performance in private learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DP-GD helps train deep learning models while keeping data private. Researchers have tried to understand how this works compared to regular training methods. They found that existing rules for understanding this process break down when there are more model parameters than training examples. This makes it hard for experts to give clear guidance on what to do. Some might reduce the complexity of their models or use bigger ones to get better results. A new study shows that in a special type of model, privacy can be achieved without any cost if the model has enough parameters. This goes against common wisdom that adding more model parts makes private learning harder. |
Keywords
» Artificial intelligence » Deep learning » Gradient descent
