E-commerce financial data encryption method based on AES algorithm

Abstract

The invention belongs to the technical field of data encryption processing, and particularly relates to an E-commerce financial data encryption method based on an AES algorithm. The method comprises the following steps: S1, carrying out financial data encryption key expansion; s2, expansion round key addition processing; s3, in combination with an AES algorithm, the actual efficiency of target encryption is improved, and an AES measurement e-commerce financial data multi-round encryption model is constructed; and S4, completing data encryption processing by adopting a ciphertext output and dynamic decryption mode. According to the method, the financial data is subjected to AES encryption, sensitive information is converted into the ciphertext which cannot be directly interpreted, and the data is better prevented from being stolen or leaked in the transmission and storage process. Even if an attacker obtains the ciphertext, it is difficult to recover the original data through brute force cracking and the like, a stronger security mechanism is formed, the security of the system is further improved, and a safer and more reliable data protection scheme is expected to be provided for the e-commerce industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of data encryption processing, and specifically relates to an e-commerce financial data encryption method based on the AES algorithm. Background Art

[0002] With the development of e-commerce, the protection of knowledge and financial data has become an indispensable part of platform management. Generally speaking, financial data contains important content such as user transaction records and capital flow information. Once leaked or tampered with, it will not only cause huge losses to users, but also seriously affect the reputation and operation of the e-commerce platform. In order to avoid the above situation, financial data encryption is currently used for processing. Reference 1: Zhu Jintan. A big data encryption method based on a new true random number generator [J]. Microcomputer Applications, 2024, 40(02): 184-187. The proposed traditional new true random number financial data encryption method uses a true random number generator to generate a key with the same length as the financial data to ensure the randomness and unpredictability of the key. Subsequently, the true random number bytes are combined with the financial data through one-to-one sequential addition or XOR operation to achieve encryption; Reference 2: Li Jingbin, Zheng Zhenzhen. Communication data encryption transmission method based on weighted Fourier transform mathematical model [J]. Yangtze River Information Communication, 2024, 37(02): 99-101. The proposed traditional weighted Fourier transform mathematical model financial data encryption method combines Fourier transform to convert financial data from time domain to frequency domain, so that the frequency components of the data can be highlighted. In the frequency domain, the different frequency components of the data are weighted by weighting coefficients to achieve data encryption. Although this type of data encryption can achieve the expected encryption goals, due to changes in the environment and the continuous increase of data, coupled with the influence of external environment and specific factors, the final encryption result is difficult to meet the expected standards.

[0003] Currently, e-commerce financial data encryption calculations are mostly performed in the form of independent unit encryption, and the target encryption efficiency is low, resulting in a decrease in the final encryption rate. Summary of the invention

[0004] The purpose of the present invention is to address the above technical problems and propose an e-commerce financial data encryption method based on the AES algorithm.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0006] An e-commerce financial data encryption method based on the AES algorithm comprises the following steps: S1: expanding the financial data encryption key; S2: expanding the round key plus processing; S3: combining the AES algorithm to improve the actual efficiency of target encryption and constructing an AES multi-round encryption model for e-commerce financial data; S4: completing data encryption processing by outputting ciphertext and dynamic decryption.

[0007] Further, as a preferred technical solution of the present invention, the S1 specifically comprises the following steps:

[0008] S1.1: The original key provided by the user is used as the input guide target of key expansion, and the initial key and a fixed key are used for encryption. At this time, the preset ciphertext is imported into the encrypted structure to form an independent encryption program, thus generating the first round key;

[0009] S1.2: To increase the encryption level at this time, it is necessary to merge the first round key and the initial key, and use the Rcon array to convert the current merged key; according to the converted financial data encryption key, a new round of merged encryption key is formed; at this time, the current extended encryption strength needs to be calculated, such as formula (1):

[0010]

[0011] Among them, K represents the extended encryption strength, θ represents the encryption coverage area, ρ represents the encryption processing frequency, Q represents the size of the key space, Z represents the key length, and W represents the encryption protection range.

[0012] Further as a preferred technical solution of the present invention, S2 specifically includes the following steps:

[0013] S2.1: Divide the original financial data into multiple 128-bit or 16-byte groups. When the total length of the data is not a multiple of 16 bytes, it is first padded to the nearest multiple of 16 bytes. At the same time, the encrypted state is shifted row by row. First, the rows of the state matrix are circularly shifted to the left, and the bitwise difference is calculated for each data group, as shown in formula (2):

[0014]

[0015] Among them, L represents the encryption bit difference, υ represents the left shift length, φ represents the autocorrelation function, n represents the encryption byte multiple, and f represents the number of row differences; combined with the current measurement, according to the changes in the encryption bit difference of e-commerce financial data, two equal-length round keys and data groups are subjected to XOR operations for each bit, realizing basic processing of the round keys;

[0016] S2.2: Set the encryption sequence of the current round of secret keys and arrange and distribute them in the order of encryption. Based on the distribution of the current encryption sequence and the specific type of e-commerce financial data, encode them with multiple character sequences and calculate the homogeneous distribution encryption eigenvalues, as shown in formula (3):

[0017]

[0018] Among them, m(c) represents the homogeneous distribution encryption eigenvalue, j represents the sequence fluctuation amplitude, s(t) and g(e) represent the key agreement and characteristic agreement respectively, and s represents the encryption identification bit.

[0019] Further, as a preferred technical solution of the present invention, S3 specifically includes the following steps:

[0020] S3.1. Combined with the AES algorithm, multi-round encryption modeling is performed for the processing of e-commerce financial data; based on the set encryption program, basic security analysis is performed on the current operating status of the e-commerce financial platform according to the preset encryption requirements;

[0021] S3.2, transmitting the set block encryption data packet to the preset position, and designing the basic encryption model structure based on the encryption sequence arrangement distribution;

[0022] S3.3. Set the encryption and decryption protocols according to the encrypted content; combine the homogeneous distribution encryption features to perform encoding fusion and encryption reconstruction, as shown in formula (4):

[0023] R(u)=ι d +ψ o +λ(4)

[0024] Where R(u) represents encrypted reconstruction, ι d represents the encryption dimension, ψ o represents the random linear encryption difference, and λ represents the boundary coefficient;

[0025] S3.4. According to the current measurement, the initial encryption protocol is further adjusted through encryption reconstruction, and then the encryption sequence is reorganized. Combined with AES calculation, the expression of the data encryption model is constructed, such as formula (5):

[0026] C=E(K,P)(5)

[0027] Among them, C represents the output ciphertext of the encryption model, E represents the encryption target, K represents the set key, and P represents the plaintext.

[0028] Further, as a preferred technical solution of the present invention, the S4 specifically includes the following steps:

[0029] After the e-commerce financial data has been encrypted by multiple rounds of AES, the model will eventually generate a ciphertext. After the ciphertext is output, it will be securely stored or transmitted through the channel. After the transmission is completed, dynamic decryption is performed, and the ciphertext is restored to the original data using the inverse process of AES encryption. The decryption process is the opposite of the encryption process. In each round of decryption, the corresponding round key will be used to perform XOR operations and other operations to gradually restore the original data. After the decryption process is completed, if an incorrect key error or data corruption occurs, error handling needs to be carried out. At this time, the error encryption times limit is calculated based on the processing conditions and data in the encryption process, as shown in formula (6):

[0030]

[0031] Where N represents the limit of the number of incorrect decryption times, represents decrypted financial data, Y represents the initial erroneous decrypted data, and the calculated erroneous decryption limit is set as the encryption verification limit. After exceeding the erroneous decryption limit, the ciphertext needs to be imported for encryption and re-encrypted according to the designed encryption process.

[0032] The e-commerce financial data encryption method based on the AES algorithm described in the present invention has the following technical effects compared with the prior art by using the above technical solution:

[0033] (1) The present invention uses AES encryption to convert sensitive information into ciphertext that cannot be directly interpreted, thereby better preventing data from being stolen or leaked during transmission and storage. Even if an attacker obtains the ciphertext, it is difficult to recover the original data through brute force cracking, etc., forming a stronger security mechanism and further improving the security of the system, in order to provide a safer and more reliable data protection solution for the e-commerce industry.

[0034] (2) The encryption rate finally obtained by the AES encryption method for e-commerce financial data proposed in the present invention is relatively high, which shows that with the assistance and support of the AES algorithm, the designed encryption method is more specific, the encryption coverage is expanded, and the encryption speed and efficiency are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of a method flow of an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of the first round of key encryption processing according to an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of Rcon array encryption key conversion according to an embodiment of the present invention;

[0038] Figure 41. It is a schematic diagram of the structure of the AES multi-round encryption model for e-commerce financial data according to an embodiment of the present invention;

[0039] Figure 5 is a schematic diagram of classification processing of financial data of enterprise G according to an embodiment of the present invention;

[0040] Figure 6 It is a schematic diagram of encryption processing of the financial data area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention is further explained below in detail with reference to the accompanying drawings so that those skilled in the art can more deeply understand the present invention and be able to implement it. However, the following reference examples are only used to explain the present invention and are not intended to limit the present invention.

[0042] like Figure 1 As shown, an e-commerce financial data encryption method based on the AES algorithm includes the following steps:

[0043] S1: Perform financial data encryption key expansion;

[0044] The key expansion process requires round keys generated from the original key to increase the complexity and randomness of the actual encryption and improve the overall encryption level. The AES algorithm supports three key lengths of 128 bits, 192 bits, and 256 bits. According to the daily encryption needs of e-commerce financial data, select the appropriate key length. For general basic financial data, a 128-bit key length is sufficient. The original key provided by the user will be used as the input guide target for key expansion. The initial key and a fixed one will be used for encryption. At this time, the preset ciphertext will be imported into the encrypted structure to form an independent encryption program, thus generating the first round key. The specific execution process is as follows: Figure 2 As shown, Figure 2 The encryption process is mainly for the first round of keys. At this time, according to the encryption processing structure designed above, in order to increase the encryption level at this time, it is necessary to merge the first round key and the initial key, and use the Rcon array to convert the current merged key. Figure 3 As shown, Figure 3 The main task is to convert the encryption key of the Rcon array. Based on the converted financial data encryption key, a new round of integrated encryption key is formed. At this time, the current extended encryption strength needs to be calculated, see formula (1):

[0045]

[0046] In formula (1), K represents the extended encryption strength, θ represents the encryption coverage area, ρ represents the encryption processing frequency, Q represents the size of the key space, Z represents the key length, and W represents the encryption protection range.

[0047] After completing the current encryption extension, analyze whether the encryption strength at this time meets the requirements of the subsequent encryption processing, and lay the foundation for the subsequent execution and processing of e-commerce financial data. The currently set encryption extension key is not fixed, and can be adjusted regularly according to actual needs to increase the flexibility and stability of the key. However, during the encryption process, the encrypted Rcon array conversion must be maintained to further ensure the stability and controllability of the encryption.

[0048] S2: Expand the round key and add processing;

[0049] The purpose of the initial round key addition is to combine the original financial data (i.e. plain text) with the initial round key in multiple stages to further improve the current encryption environment. The original financial data is divided into multiple 128-bit (i.e. 16-byte) groups. If the total length of the data is not a multiple of 16 bytes, it is necessary to first pad (Padding) to the nearest multiple of 16 bytes, and at the same time, perform row shift processing on the encrypted state. The rows of the state matrix can be first cyclically shifted to the left, and the bitwise difference operation can be performed. For each data group, the encrypted bitwise difference is measured and calculated, as shown in formula (2):

[0050]

[0051] In formula (2), L represents the encryption bit difference, υ represents the left shift length, φ represents the autocorrelation function, n represents the encryption byte multiple, and f represents the number of row differences.

[0052] Combined with the current measurement, according to the change of the encryption bit difference of the e-commerce financial data, two equal-length round keys and data groups are subjected to XOR operations per bit to achieve basic processing of the round keys. Next, the encryption sequence of the current round key is set and arranged and distributed in the order of encryption. Based on the distribution of the current encryption sequence and the specific type of e-commerce financial data, it is encoded with multiple character sequences and the homogeneous distribution encryption eigenvalue is calculated, as shown in formula (3):

[0053]

[0054] In formula (3), m(c) represents the homogeneous distribution encryption feature value, j represents the sequence fluctuation amplitude, s(t) and g(e) represent the key agreement and feature agreement respectively, and s represents the encryption identification bit.

[0055] According to current measurements, the encryption level of the secret key is increased by homogeneously distributing the encryption eigenvalues, and the processing structure of the encryption sequence is adjusted. Due to the complexity and randomness of the round keys, the master key also needs to be calibrated to better ensure the security of the data.

[0056] S3: Combined with the AES algorithm, the actual efficiency of target encryption is improved, and an AES multi-round encryption model for e-commerce financial data is constructed;

[0057] Combined with the AES algorithm, multi-round encryption modeling is performed for the processing of e-commerce financial data. Based on the encryption program set above, basic security analysis is performed on the current operating status of the e-commerce financial platform according to the preset encryption requirements. In general, the number of AES rounds determines the strength and security of the encryption. The more rounds, the higher the encryption security, but the encryption speed will decrease accordingly. Conversely, the fewer rounds, the lower the encryption security, but the encryption speed will increase accordingly. The block encryption data packet set above is transmitted to the preset location, and the basic encryption model structure is designed based on the encryption sequence arrangement and distribution, see Figure 4 As shown, Figure 4 The main purpose is to design and verify the structure of the multi-round encryption model for AES e-commerce financial data. Subsequently, it is necessary to set the encryption and decryption protocols in the model according to the encrypted content. Combining the homogeneous distribution encryption features, the encoding fusion is performed to perform encryption reconstruction, see formula (4):

[0058] R(u)=ι d +ψ o +λ (4)

[0059] In formula (4), R(u) represents encrypted reconstruction, ι d represents the encryption dimension, ψ o represents the random linear encryption difference, and λ represents the boundary coefficient.

[0060] According to the current measurement, through encryption reconstruction, the initial encryption protocol is further adjusted, and then the encryption sequence is reorganized. Combined with AES calculation, the expression of the data encryption model is constructed, see formula (5):

[0061] C=E(K,P)(5)

[0062] In formula (5), C represents the output ciphertext of the encryption model, E represents the encryption target, K represents the set key, and P represents the plaintext. Combined with the encryption results output by the model, comparative verification and practical analysis are carried out. Through parameter adjustment and algorithm optimization, the comprehensive application ability of the model is improved to provide strong protection for e-commerce financial data.

[0063] S4: Data encryption is completed by outputting ciphertext and dynamic decryption;

[0064] After the e-commerce financial data has been encrypted by multiple rounds of AES, the model will eventually generate a ciphertext. This ciphertext is obtained after the original financial data has been transformed by a complex encryption algorithm. Its content is completely different from the original data and is highly random and complex. After the ciphertext is output, it is securely stored or transmitted through the channel. Since the ciphertext has been encrypted, even if it is intercepted, the attacker cannot directly read the content. Based on this, in order to further improve the encryption program, dynamic decryption processing is performed after the transmission is completed. The ciphertext is restored to the original data using the inverse process of AES encryption. Usually, the decryption process is opposite to the encryption process. In each round of decryption, the corresponding round key is used to perform operations such as XOR operations to gradually restore the original data. After the decryption process is completed, if an incorrect key error, data corruption, etc. occur, error handling needs to be carried out. At this time, combined with the processing conditions and data in the encryption process, the limit of the number of incorrect encryption times is calculated, as shown in formula (6):

[0065]

[0066] In formula (6), N represents the limit of the number of incorrect decryption times, represents decrypted financial data, Y represents the initial erroneous decrypted data, and the calculated erroneous decryption times limit is set as the encryption verification limit standard. After exceeding the erroneous decryption upper limit, the ciphertext needs to be imported for encryption processing, and re-encrypted according to the encryption process designed above, to further ensure the actual effect of e-commerce data encryption processing and increase the security and integrity of financial data.

[0067] Test the method of the present invention: Combined with the AES algorithm, the actual application effect of the e-commerce financial data encryption method is analyzed and verified. Considering the authenticity and reliability of the final test results, the analysis is carried out in the form of comparison, and the e-commerce financial platform of G company is used as the target object of the test. Set the traditional new true random number financial data encryption method, the traditional weighted Fourier transform mathematical model financial data encryption method, and the AES calculation e-commerce financial data encryption method designed this time. Collect and summarize the data and information used in the platform, and store them in categories for subsequent use. Next, with the assistance of the AES algorithm, set up and build the test environment.

[0068] In a complex context, a test environment for the e-commerce financial data encryption method based on the AES algorithm is built, and the actual test program is stabilized. First, the data encryption platform is connected to the G enterprise e-commerce financial platform, and a shared transmission program for data information is set up. Data acquisition is performed in 6 cycles. At this time, the data size is 45Gbit, the non-privacy financial data size is 30Gbit, and the privacy financial data size is 15Gbit. In the actual transmission process, it is necessary to encrypt privacy data and non-privacy data at the same time, and to perform double encryption on non-privacy data. At present, in a complex context, the collected data is classified and processed according to the application type of financial data. Figure 5 As shown; Figure 5 The main task is to classify the financial data of G Company. Next, the classified data will be stored and converted into data packets with consistent format and quantity. Based on this, it is also necessary to set up an encryption protection mechanism on the channel to expand the actual encryption scope. Then, according to the encryption requirements, set auxiliary test indicators and parameters, as shown in Table 1:

[0069] Table 1 Financial data auxiliary test indicators and parameter settings

[0070]

[0071] Table 1 mainly sets the auxiliary test indicators and parameters for financial data. On this basis, the stability of the current enterprise's financial management platform operation is adjusted, the main frequency is 3.55GHz, the memory is 2.5GB, and the bandwidth / delay is set to 1.2GB / s and 120ms. So far, the setting and construction of the test environment have been completed. Next, combined with the AES algorithm, the e-commerce financial data encryption method is measured and compared with practice.

[0072] In the test environment built above, combined with the AES algorithm, a practical analysis of the encryption method of e-commerce financial data is conducted. Currently, the classified data is encrypted in combination. According to the encryption method designed above, this time, a combination of privacy data and non-privacy data is used for encryption. Import the ciphertext into the program, convert the real-time encryption form, and perform encryption processing in three stages. Set the encrypted public key and private key respectively. On this basis, it is necessary to perform secondary deep encryption on the privacy data. At this time, the Lyapunov exponent of the encrypted data is calculated, see formula (7):

[0073]

[0074] In formula (7): D q represents the data encryption Lyapunov exponent, β represents the encryption mean, Represents the total amount of data, v represents the encryption correlation coefficient, a represents the encryption critical value, and χ represents the directional encryption distribution area. Combined with the currently calculated data encryption Lyapunov index, the collected data is processed for regional distribution, and local encryption processing is carried out according to the change trend of the Lyapunov index, see Figure 6 As shown; Figure 6 The main process is to encrypt the financial data area, combine the encryption characteristics of the Lyapunov exponent conversion at this time, use the AES algorithm to convert the encrypted data at this time into a block encryption form, and combine the size of the encrypted data to be fixed and unified. After completing the data encryption, use the preset channel to transmit the data packet to the specified location, and after class processing according to the preset program, calculate the final encryption rate, see formula (8):

[0075]

[0076] In formula (8), H represents the encryption rate of financial data, π 1 and π 2 Respectively represent the basic encryption convergence speed and the actual encryption convergence speed, represents repeated encryption data, γ represents the encryption distribution feature quantity, and ο represents the adaptive function. Combined with the current measurement, the verification of the test results is completed, as shown in Table 2:

[0077] Table 2 AES calculation of financial data encryption test results

[0078]

[0079] Result Table 2, analysis of the test results: Compared with the traditional new true random number financial data encryption method and the traditional weighted Fourier transform mathematical model financial data encryption method, the AES e-commerce financial data encryption method proposed in the present invention finally obtains a relatively high encryption rate, which shows that with the assistance and support of the AES algorithm, the designed encryption method is more specific, the encryption coverage is expanded, and the encryption speed and efficiency are greatly improved.

[0080] With the assistance and support of the AES algorithm, the financial data encryption form proposed by the present invention is more flexible and changeable, and its own advantages are gradually highlighted. The financial data protection effect formed for e-commerce has been further improved, while ensuring data security, improving data processing efficiency, and jointly promoting the innovation and development of e-commerce financial data encryption technology.

[0081] The specific implementation scheme described above further describes in detail the purpose, technical scheme and beneficial effects of the present invention. It should be understood that the above is only a specific implementation scheme of the present invention and is not intended to limit the scope of the present invention. Any equivalent changes and modifications made by any technician in the field without departing from the concept and principle of the present invention should fall within the scope of protection of the present invention.

Claims

1. An e-commerce financial data encryption method based on the AES algorithm, characterized in that: The following steps are involved: S1: Perform financial data encryption key expansion; S2: Expand the round key and add processing; S3: Combined with the AES algorithm, the actual efficiency of target encryption is improved, and an AES multi-round encryption model for e-commerce financial data is constructed; S4: Data encryption is completed by outputting ciphertext and dynamic decryption.

2. The e-commerce financial data encryption method based on the AES algorithm according to claim 1 is characterized in that: The S1 specifically includes the following steps: S1.1: The original key provided by the user is used as the input guide target of key expansion, and the initial key and a fixed key are used for encryption. At this time, the preset ciphertext is imported into the encrypted structure to form an independent encryption program, thus generating the first round key; S1.2: To increase the encryption level at this time, it is necessary to merge the first round key and the initial key, and use the Rcon array to convert the current merged key; according to the converted financial data encryption key, a new round of merged encryption key is formed; at this time, the current extended encryption strength needs to be calculated, such as formula (1): Among them, K represents the extended encryption strength, θ represents the encryption coverage area, ρ represents the encryption processing frequency, Q represents the size of the key space, Z represents the key length, and W represents the encryption protection range.

3. The e-commerce financial data encryption method based on the AES algorithm according to claim 2 is characterized in that: The S2 specifically includes the following steps: S2.1: Divide the original financial data into multiple 128-bit or 16-byte groups. When the total length of the data is not a multiple of 16 bytes, it is first padded to the nearest multiple of 16 bytes. At the same time, the encrypted state is shifted row by row. First, the rows of the state matrix are circularly shifted to the left, and the bitwise difference is calculated for each data group, as shown in formula (2): Among them, L represents the encryption bit difference, υ represents the left shift length, φ represents the autocorrelation function, n represents the encryption byte multiple, and f represents the number of row differences; combined with the current measurement, according to the changes in the encryption bit difference of e-commerce financial data, two equal-length round keys and data groups are subjected to XOR operations for each bit, realizing basic processing of the round keys; S2.2: Set the encryption sequence of the current round of secret keys and arrange and distribute them in the order of encryption. Based on the distribution of the current encryption sequence and the specific type of e-commerce financial data, encode them with multiple character sequences and calculate the homogeneous distribution encryption eigenvalues, as shown in formula (3): Among them, m(c) represents the homogeneous distribution encryption eigenvalue, j represents the sequence fluctuation amplitude, s(t) and g(e) represent the key agreement and characteristic agreement respectively, and s represents the encryption identification bit.

4. The e-commerce financial data encryption method based on the AES algorithm according to claim 3 is characterized in that: The S3 specifically includes the following steps: S3.

1. Combined with the AES algorithm, multi-round encryption modeling is performed for the processing of e-commerce financial data; based on the set encryption program, basic security analysis is performed on the current operating status of the e-commerce financial platform according to the preset encryption requirements; S3.2, transmitting the set block encryption data packet to the preset position, and designing the basic encryption model structure based on the encryption sequence arrangement distribution; S3.

3. Set the encryption and decryption protocols according to the encrypted content; combine the homogeneous distribution encryption features to perform encoding fusion and encryption reconstruction, as shown in formula (4): R(u)=i d +ψ o +λ (4) Where R(u) represents encrypted reconstruction, ι d represents the encryption dimension, ψ o represents the random linear encryption difference, and λ represents the boundary coefficient; S3.

4. According to the current measurement, the initial encryption protocol is further adjusted through encryption reconstruction, and then the encryption sequence is reorganized. Combined with AES calculation, the expression of the data encryption model is constructed, such as formula (5): C=E(K,P) (5) Among them, C represents the output ciphertext of the encryption model, E represents the encryption target, K represents the set key, and P represents the plaintext.

5. The e-commerce financial data encryption method based on the AES algorithm according to claim 4 is characterized in that: The S4 specifically comprises the following steps: After the e-commerce financial data has been encrypted by multiple rounds of AES, the model will eventually generate a ciphertext. After the ciphertext is output, it will be securely stored or transmitted through the channel. After the transmission is completed, dynamic decryption is performed, and the ciphertext is restored to the original data using the inverse process of AES encryption. The decryption process is the opposite of the encryption process. In each round of decryption, the corresponding round key will be used to perform XOR operations and other operations to gradually restore the original data. After the decryption process is completed, if an incorrect key error or data corruption occurs, error handling needs to be carried out. At this time, the error encryption times limit is calculated based on the processing conditions and data in the encryption process, as shown in formula (6): Among them, N represents the limit of the number of incorrect decryption times, θ represents the decrypted financial data, and Y represents the initial incorrect decryption data. The calculated limit of the number of incorrect decryption times is set as the restriction standard for encryption verification. After exceeding the upper limit of the incorrect decryption, it is necessary to import the ciphertext for encryption processing and re-encrypt it according to the designed encryption process.

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