https://github.com/radeity/lwe-based-fuzzy-extractor
Implementation of LWE-based fuzzy extractor
https://github.com/radeity/lwe-based-fuzzy-extractor
Last synced: over 1 year ago
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Implementation of LWE-based fuzzy extractor
- Host: GitHub
- URL: https://github.com/radeity/lwe-based-fuzzy-extractor
- Owner: Radeity
- Created: 2021-07-19T03:16:10.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-07-19T04:45:35.000Z (almost 5 years ago)
- Last Synced: 2025-01-15T14:16:44.517Z (over 1 year ago)
- Language: Python
- Size: 168 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# LWE-based-fuzzy-extractor
A Python implementation of LWE-based Fuzzy Extractor
代码仅包括模糊提取器的理论实现，本地运行需要自己处理模糊提取器输入，例如：人脸特征、指纹特征等。
- Apon等人提出的基于LWE假设可重用模糊提取器（对数级纠错）[[1]](#refer-anchor-1)
- Wen等人提出的基于LWE假设可重用模糊提取器（线性级纠错）[[2]](#refer-anchor-2)
输入需要服从特定分布以满足模糊提取器安全性，具体可见参考文献。
考虑实际应用中随机源输入达不到Wen等人的模糊提取器构造中熵的要求，Wen18.py中加入了自定义的**熵增模块**见entropy_increase.png
# Reference
[1] [Daniel Apon, Chongwon Cho, Karim Eldefrawy, and Jonathan Katz. Efficient, reusable fuzzy extractors from LWE. In International Conference on Cyber Security Cryptography and Machine Learning, pages 1–18. Springer, 2017.](https://eprint.iacr.org/2017/755.pdf)
[2] [Yunhua Wen and Shengli Liu. Reusable fuzzy extractor from LWE. In Australasian Conference on Information Security and Privacy, pages 13–27. Springer, 2018.](https://link.springer.com/chapter/10.1007/978-3-319-93638-3_2)