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基于伽罗华域理论的新型密码数据加密。

A Novel Cipher-Based Data Encryption with Galois Field Theory.

机构信息

Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia.

Software Developer Applications, 671 E Royal Ln, APT 1074, Irving, TX 75039, USA.

出版信息

Sensors (Basel). 2023 Mar 20;23(6):3287. doi: 10.3390/s23063287.

DOI:10.3390/s23063287
PMID:36991997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10059044/
Abstract

Both the act of keeping information secret and the research on how to achieve it are included in the broad category of cryptography. When people refer to "information security," they are referring to the study and use of methods that make data transfers harder to intercept. When we talk about "information security," this is what we have in mind. Using private keys to encrypt and decode messages is a part of this procedure. Because of its vital role in modern information theory, computer security, and engineering, cryptography is now considered to be a branch of both mathematics and computer science. Because of its mathematical properties, the Galois field may be used to encrypt and decode information, making it relevant to the subject of cryptography. The ability to encrypt and decode information is one such use. In this case, the data may be encoded as a Galois vector, and the scrambling process could include the application of mathematical operations that involve an inverse. While this method is unsafe when used on its own, it forms the foundation for secure symmetric algorithms like AES and DES when combined with other bit shuffling methods. A two-by-two encryption matrix is used to protect the two data streams, each of which contains 25 bits of binary information which is included in the proposed work. Each cell in the matrix represents an irreducible polynomial of degree 6. Fine-tuning the values of the bits that make up each of the two 25-bit binary data streams using the Discrete Cosine Transform (DCT) with the Advanced Encryption Standard (AES) Method yields two polynomials of degree 6. Optimization is carried out using the Black Widow Optimization technique is used to tune the key generation in the cryptographic processing. By doing so, we can produce two polynomials of the same degree, which was our original aim. Users may also use cryptography to look for signs of tampering, such as whether a hacker obtained unauthorized access to a patient's medical records and made any changes to them. Cryptography also allows people to look for signs of tampering with data. Indeed, this is another use of cryptography. It also has the added value of allowing users to check for indications of data manipulation. Users may also positively identify faraway people and objects, which is especially useful for verifying a document's authenticity since it lessens the possibility that it was fabricated. The proposed work achieves higher accuracy of 97.24%, higher throughput of 93.47%, and a minimum decryption time of 0.0047 s.

摘要

密码学包括保密信息的行为和研究如何实现保密信息的行为。当人们提到“信息安全”时,他们指的是研究和使用使数据传输更难拦截的方法。当我们谈论“信息安全”时,就是指我们所想到的。使用私钥加密和解密消息是该过程的一部分。由于密码学在现代信息理论、计算机安全和工程中具有至关重要的作用,因此现在它被认为是数学和计算机科学的一个分支。由于其数学性质,伽罗瓦域可用于加密和解密信息,因此与密码学主题相关。加密和解密信息是这样的一种用途。在这种情况下,数据可以编码为伽罗瓦向量,并且加扰过程可以包括应用涉及逆的数学运算。虽然单独使用这种方法不安全,但当与其他位混洗方法结合使用时,它构成了 AES 和 DES 等安全对称算法的基础。提出的工作使用一个 2x2 的加密矩阵来保护两个数据流,每个数据流包含 25 位二进制信息。矩阵中的每个单元格表示一个 6 次不可约多项式。使用离散余弦变换(DCT)和高级加密标准(AES)方法对构成两个 25 位二进制数据流的每个位的数值进行微调,生成两个 6 次多项式。使用黑寡妇优化技术对密码处理中的密钥生成进行优化。通过这样做,我们可以生成两个相同度数的多项式,这是我们的最初目标。用户还可以使用密码学来寻找篡改的迹象,例如黑客是否未经授权访问患者的病历并对其进行任何更改。密码学还允许人们寻找数据被篡改的迹象。实际上,这是密码学的另一种用途。它还具有允许用户检查数据操作指示的附加价值。用户还可以对远处的人和物体进行正面识别,这对于验证文档的真实性尤其有用,因为它降低了文档被伪造的可能性。提出的工作实现了 97.24%的更高准确性、93.47%的更高吞吐量和 0.0047s 的最小解密时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/5ee29e6a3b8f/sensors-23-03287-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/93ea74f7b333/sensors-23-03287-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/dd07ef824374/sensors-23-03287-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/61e7035ccc51/sensors-23-03287-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/b76b456406a2/sensors-23-03287-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/da800e0196f4/sensors-23-03287-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/6a0db462b36d/sensors-23-03287-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/0f58063e5ccb/sensors-23-03287-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/5ee29e6a3b8f/sensors-23-03287-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/93ea74f7b333/sensors-23-03287-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/dd07ef824374/sensors-23-03287-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/61e7035ccc51/sensors-23-03287-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/b76b456406a2/sensors-23-03287-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/da800e0196f4/sensors-23-03287-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/6a0db462b36d/sensors-23-03287-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/0f58063e5ccb/sensors-23-03287-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985a/10059044/5ee29e6a3b8f/sensors-23-03287-g008.jpg

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