Summary of Differentially Private Learning Beyond the Classical Dimensionality Regime, by Cynthia Dwork et al.
Differentially Private Learning Beyond the Classical Dimensionality Regime
by Cynthia Dwork, Pranay Tankala, Linjun Zhang
First submitted to arxiv on: 20 Nov 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 research explores the challenges of differentially private learning in the proportional dimensionality regime, where both data samples and problem dimension grow at rates proportional to each other. This setting is more complex than previous work, which assumed either zero or negligible ratios between the number of samples and problem dimension. The study provides sharp theoretical estimates for several well-studied differentially private algorithms, including output perturbation, objective perturbation, and noisy stochastic gradient descent, for robust linear regression and logistic regression tasks. These estimates enable a more nuanced understanding of the price of privacy for these algorithms, revealing previously unobserved phenomena like “double descent”-like behavior in training error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Differentially private learning is a way to keep data private while still using it for important tasks like predictions and decision-making. The researchers looked at how well different algorithms work when both the amount of data and the complexity of the problem grow at the same rate. They found that some algorithms do better than others, depending on the situation. |
Keywords
» Artificial intelligence » Linear regression » Logistic regression » Stochastic gradient descent
