Summary of Differentially Private Prototypes For Imbalanced Transfer Learning, by Dariush Wahdany et al.
Differentially Private Prototypes for Imbalanced Transfer Learning
by Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Differentially Private Prototype Learning (DPPL) paradigm addresses the limitations of existing differential privacy solutions for private transfer learning. By leveraging pre-trained encoders to extract features from private data and generating differentially private prototypes, DPPL offers high-utility predictions while ensuring strong privacy guarantees under pure differential privacy. The approach can be further improved by privately sampling prototypes from publicly available data points used to train the encoder. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Differentially Private Prototype Learning is a new way to keep machine learning models private. It’s like using a special filter to make sure your model doesn’t leak information about your training data. This is important because some models can reveal private details just by looking at how they were trained. The new approach uses pre-trained models to extract features from private data and creates special prototypes that represent each class in the data. These prototypes are designed to be publicly releasable for making predictions, but still keep the original training data private. This method is useful when you have limited private data or when your data is imbalanced. |
Keywords
» Artificial intelligence » Encoder » Machine learning » Transfer learning
