Summary of Amalgam: a Framework For Obfuscated Neural Network Training on the Cloud, by Sifat Ut Taki and Spyridon Mastorakis
Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud
by Sifat Ut Taki, Spyridon Mastorakis
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Systems and Control (eess.SY)
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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 This paper introduces Amalgam, a framework for training neural networks (NNs) in cloud-based environments while preserving privacy. The issue addressed is that proprietary NN models and datasets are vulnerable to exposure when trained on the cloud. To address this, Amalgam adds calibrated noise to both the model architectures and training datasets, effectively hiding them from the cloud provider. After training, Amalgam extracts the original models, ensuring accuracy and correctness without significant overheads. The framework is evaluated with computer vision and natural language processing tasks, demonstrating its effectiveness in maintaining privacy while training NNs on the cloud. The prototype implementation is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep secret how artificial intelligence (AI) models are trained when using special computers called clouds. Currently, this information can be seen by the people running those clouds. To solve this problem, the authors created a tool called Amalgam that adds “noise” to the AI model and the data it uses to learn. This noise makes it hard for others to understand what the original AI model is doing. The tool works well and doesn’t slow down the training process too much. |
Keywords
» Artificial intelligence » Natural language processing
