Summary of Efficient Privacy-preserving Kan Inference Using Homomorphic Encryption, by Zhizheng Lai et al.
Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption
by Zhizheng Lai, Yufei Zhou, Peijia Zheng, Lin Chen
First submitted to arxiv on: 12 Sep 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 recently proposed Kolmogorov-Arnold Networks (KANs) offer enhanced interpretability and greater model expressiveness. However, KANs also present challenges related to privacy leakage during inference. To address this issue, we propose an accurate and efficient privacy-preserving inference scheme tailored for KANs. Our approach introduces a task-specific polynomial approximation for the SiLU activation function, dynamically adjusting the approximation range to ensure high accuracy on real-world datasets. Additionally, we develop an efficient method for computing B-spline functions within the HE domain, leveraging techniques such as repeat packing, lazy combination, and comparison functions. We evaluate the effectiveness of our privacy-preserving KAN inference scheme on both symbolic formula evaluation and image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kolmogorov-Arnold Networks are new types of artificial intelligence models that can be very good at understanding data. However, these models have a problem: they can leak private information when used to make predictions. This is a big issue because people should be able to use AI without sharing their personal secrets. To fix this problem, researchers came up with a new way to use AI models that keeps the information safe. They did this by making the model think about the data in a special way and using secret math tricks. The new method works well on many different types of data and is much faster than other methods. |
Keywords
» Artificial intelligence » Image classification » Inference
