Summary of Faster Algorithms For User-level Private Stochastic Convex Optimization, by Andrew Lowy et al.
Faster Algorithms for User-Level Private Stochastic Convex Optimization
by Andrew Lowy, Daogao Liu, Hilal Asi
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Optimization and Control (math.OC)
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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 studies private stochastic convex optimization under user-level differential privacy constraints. The authors aim to develop novel algorithms that can efficiently optimize large-scale machine learning models while protecting the privacy of individual users’ data collections. Existing methods are limited by restrictive assumptions and computational complexity, making them impractical for many applications. To address these limitations, the researchers provide three new algorithms with state-of-the-art excess risk and runtime guarantees, without stringent assumptions. The first algorithm achieves optimal excess risk in linear time under mild smoothness assumptions, while the second and third algorithms achieve optimal excess risk in approximately (mn)^(9/8) and n^(11/8) m^(5/4) gradient computations, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on a new way to protect people’s privacy when using machine learning. They want to make sure that even if a lot of data is shared, individual users’ information stays safe. Right now, there are some methods for doing this, but they’re not very good because they take too long or make assumptions that aren’t true. The authors have created three new ways to do private machine learning that are faster and more practical than what’s currently available. They tested these methods on different types of data and found that they work really well. |
Keywords
* Artificial intelligence * Machine learning * Optimization
