论文标题

TEDL:基于深度学习的文本加密方法

TEDL: A Text Encryption Method Based on Deep Learning

论文作者

Li, Xiang, Wang, Peng

论文摘要

近年来,人们越来越重视信息安全性,并提出了各种加密方法。但是,对于对称加密方法,众所周知的加密技术仍然依靠关键空间来保证安全性并遭受频繁的密钥更新。旨在解决这些问题,本文提出了一种基于深度学习的新文本加密方法,称为TEDL,其中秘密密钥包括深度学习模型中的超参数,加密的核心步骤是将输入数据转换为在超参数下训练的权重。首先,双方通过根据指定的超参数训练深度学习模型来建立一个单词矢量表。然后,在带有SHA-256函数和其他技巧的Word Vector表上构建了一个自更换代码簿。当通信开始时,加密和解密等效于索引和倒置索引,从而实现了明文和密文之间的转换。实验和相关分析的结果表明,TEDL在安全性,效率,一般性方面表现良好,并且对关键重新分布的频率的需求较低。特别是,作为当前加密方法的补充,构造代码手册的耗时过程增加了蛮力攻击的困难,而不会降低沟通效率。

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel text encryption method based on deep learning called TEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks while not degrade the communication efficiency.

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