Summary of Differentially Private Bilevel Optimization, by Guy Kornowski
Differentially Private Bilevel Optimization
by Guy Kornowski
First submitted to arxiv on: 29 Sep 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 A novel set of differentially private (DP) algorithms is proposed for bilevel optimization, a problem class gaining popularity in machine learning applications. These algorithms provide any desired level of privacy while avoiding computationally expensive Hessian calculations, making them suitable for large-scale settings. The authors’ gradient-based (,)-DP algorithm achieves a hypergradient norm bound of ((/n){1/2}+(/n){1/3}), where n is the dataset size and d_/d_ are the upper/lower level dimensions. The analysis covers constrained and unconstrained problems, accounts for mini-batch gradients, and applies to both empirical and population losses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to keep personal data private while still using it for important machine learning tasks has been discovered. This method is called differentially private (DP) and helps protect people’s information by making sure that even if a small part of the data is revealed, it won’t give away any sensitive details. The researchers developed special algorithms that can do this job well without needing to calculate something called the Hessian, which would be very time-consuming for big datasets. Their method works with both small and large sets of data and handles different types of problems. |
Keywords
» Artificial intelligence » Machine learning » Optimization
