In recent years, the public has become more aware of security concerns, and the demand for convenient and efficient encryption technology has increased. Biological data is used in identity authentication and key generation as the innate characteristic information of people. Biological key have the advantage of convenience and fast application without carrying any document; however, they also have the disadvantage of biological characteristic leaks and the inability to change. Based on the advantages and disadvantages of biological key, we propose the concept of semantic key. Language, a medium that fills the lives of people, has similar characteristics of convenience and fast application as biological key; however, semantic key will not reveal biological information. As the amount of semantic information is large, it can be changed at any time. Compared with biological key, it provides better security and flexibility. Therefore, we propose a semantic key generation framework of semantic extraction + feature stabilization + fuzzy extraction that improves the existing semantic extraction model and feature stabilization model and design the semantic key extraction model. In terms of artificial sentence formation, semantic key can be extracted with an accuracy of more than 99%, and an error rate of less than 0.5%.

中文翻译：

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基于自然语言的语义密钥生成

近年来，公众对安全问题的关注度越来越高，对便捷高效的加密技术的需求也越来越大。生物数据作为人的固有特征信息用于身份认证和密钥生成。生物钥匙无需携带任何文件，使用方便快捷；但是，它们也有生物特征泄漏和无法改变的缺点。基于生物密钥的优缺点，我们提出了语义密钥的概念。语言作为一种充斥着人们生活的媒介，具有与生物钥匙相似的便捷、快速应用的特点；但是，语义密钥不会泄露生物信息。由于语义信息量很大，可以随时更改。与生物密钥相比，它提供了更好的安全性和灵活性。因此，我们提出了语义提取+特征稳定+模糊提取的语义关键生成框架，改进了现有的语义提取模型和特征稳定模型，并设计了语义关键提取模型。在人工造句方面，语义键提取准确率达99%以上，错误率小于0.5%。