Summary of Public-data Assisted Private Stochastic Optimization: Power and Limitations, by Enayat Ullah et al.
Public-data Assisted Private Stochastic Optimization: Power and Limitations
by Enayat Ullah, Michael Menart, Raef Bassily, Cristóbal Guzmán, Raman Arora
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Optimization and Control (math.OC); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 paper investigates the limits and capabilities of public-data assisted differentially private (PA-DP) algorithms in stochastic convex optimization (SCO) and supervised learning tasks. Specifically, it focuses on the problem of SCO with either labeled or unlabeled public data. The authors establish lower bounds for PA-DP mean estimation and show that simple strategies, such as treating all data as private or discarding private data, are optimal up to constant factors. They also propose novel methods for leveraging public data in private supervised learning, including an efficient algorithm for generalized linear models (GLM) with unlabeled public data. The results have implications for applications in neural networks and non-Euclidean geometries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can use public information to help make private decisions better. It’s like trying to find the best way to make a decision when you don’t know everything, but some people do. They found that if you just use all the information as private or throw it away, that’s actually the best you can do. But they also showed that there are ways to use public information to make better decisions in certain situations. This could be important for things like artificial intelligence and learning from big data. |
Keywords
* Artificial intelligence * Optimization * Supervised
