Summary of Assessing the Impact Of Image Dataset Features on Privacy-preserving Machine Learning, by Lucas Lange and Maurice-maximilian Heykeroth and Erhard Rahm
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
by Lucas Lange, Maurice-Maximilian Heykeroth, Erhard Rahm
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 Machine Learning models trained on sensitive data face security challenges as they can be attacked and leak information. This study identifies image dataset characteristics that affect the utility and vulnerability of private and non-private Convolutional Neural Network (CNN) models. Through analyzing multiple datasets and privacy budgets, we find that imbalanced datasets increase vulnerability in minority classes, but Differential Privacy (DP) mitigates this issue. Datasets with fewer classes improve both model utility and privacy, while high entropy or low Fisher Discriminant Ratio (FDR) datasets deteriorate the utility-privacy trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are important for many tasks, including computer vision. However, these models can be attacked if they’re trained on sensitive data. The study looked at how different types of image datasets affect the performance and security of private and non-private Convolutional Neural Network (CNN) models. They found that certain types of datasets make it harder to balance the model’s performance and privacy. By understanding these characteristics, we can better protect our data and ensure our models are secure. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Neural network
