Financial data processing method, system and storage medium for improving security and confidentiality

Abstract

The application relates to the field of storage encryption technology, in particular to a financial data processing method and system for improving security and privacy and a storage medium, the method comprising the following steps: extracting a tuition payment detail and converting the same into a hash table; determining local similarity and global similarity of different students with adjacent serial numbers, combining the key value difference of the different students with adjacent serial numbers in a same key, and calculating an encryption degree adjustment factor of the students in the same key; and obtaining a key value of the students in each key according to the encryption degree adjustment factor of the students in each key, and performing encryption according to the key value and the key value of the key, so as to improve the security and privacy of the financial data processing system. According to the application, the encryption security and the calculation amount can be dynamically balanced according to the importance of different encryption information.

Description

Technical Field

[0001] This application relates to the field of storage encryption technology, specifically to financial data processing methods, systems, and storage media that improve security and confidentiality. Background Technology

[0002] School financial data primarily includes information such as income, expenditure, assets and liabilities, and individual payment records. This data is crucial for the school's daily operations and long-term planning. Improving the security and confidentiality of school financial data can safeguard the school's budget formulation and resource allocation, and prevent economic losses. Asymmetric encryption algorithms have high encryption efficiency, and the ECC algorithm within asymmetric encryption algorithms can be used to encrypt important information in school financial data.

[0003] However, the importance of different information within school financial data varies. Strongly encrypting both important and unimportant information would significantly increase the computational load during encryption, while weakly encrypting both could compromise the security of important information, potentially leading to security vulnerabilities in the encryption result. Therefore, it is necessary to determine the encryption strength based on the importance of different information within the school's financial data and encrypt them separately, ensuring the security of the encryption result while reducing computational load. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method, system, and storage medium for processing financial data to enhance security and confidentiality. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for improving the security and confidentiality of financial data processing, the method comprising the following steps:

[0006] Extract tuition payment details and convert them into a hash table;

[0007] Based on the differences and similarities among the key values ​​of the same key in the hash table for different students with adjacent student IDs, determine the local similarity of different students with adjacent serial numbers;

[0008] Based on the similarity of different students with adjacent student IDs across all the same key selections, the global similarity of the same key is determined. Based on the local similarity of different students with adjacent serial numbers and the global similarity of students at the same key, the encryption level adjustment factor of the student at the same key is calculated.

[0009] Based on the encryption level adjustment factor of each student for each key, the key value of each student's key for each key is obtained, and encryption is performed based on the key value and the key value, thereby improving the security and confidentiality of the financial data processing system.

[0010] Furthermore, the keys of the hash table are the field names of the tuition payment details.

[0011] Furthermore, the specific process for determining the local similarity of different students with adjacent student IDs based on the differences and similarities among all key values ​​of the same key in the hash table is as follows:

[0012] Based on the differences and similarities between the key values ​​of all students with adjacent student IDs on the same key in the hash table, determine the differences and similarities between the key values ​​of students with adjacent student IDs on the same key.

[0013] Calculate the selection similarity of different students with adjacent student IDs for all the same key based on the key value difference and key value similarity of different students with adjacent student IDs for all the same key.

[0014] The mean of the selection similarity of different students with adjacent serial numbers across all keys is denoted as the local similarity of different students with adjacent serial numbers.

[0015] Furthermore, the specific process for obtaining the key-value difference and key-value similarity is as follows:

[0016] The difference between the key values ​​of different students with adjacent student IDs on the same key in the hash table is denoted as the difference between the key values ​​of different students with adjacent student IDs on the same key.

[0017] The similarity between the key values ​​of different students with adjacent student IDs on the same key in the hash table is denoted as the key value similarity of different students with adjacent student IDs on the same key.

[0018] Furthermore, the method for calculating the similarity is as follows:

[0019]

[0020] in, Indicates the first The student and the first The nth student in the hash table The key value difference of each key; Indicates the first The student and the first The student in the first Key-value similarity of each key; Indicates the first The student and the first The nth student in the hash table Selection similarity of individual keys.

[0021] Furthermore, the specific process for determining the global similarity of the same key is as follows:

[0022] Based on the selection similarity of all students with adjacent student IDs across all keys, a selection similarity sequence for the same key is established. A constant similarity sequence of the same length as the selection similarity sequence for the same key is also established. The cosine similarity between the selection similarity sequence for the same key and the constant similarity sequence is denoted as the global similarity of the same key.

[0023] Furthermore, the formula for calculating the encryption level adjustment factor for the same key by the student is:

[0024]

[0025] in, Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; Indicates the first The student and the first Local similarity of individual students; Indicates the first The nth student in the hash table Global similarity of each key; This represents the normalization function.

[0026] Furthermore, the specific formula for calculating the key value of each key for the student is as follows:

[0027]

[0028]

[0029]

[0030] in, Indicates the first The nth student in the hash table Logistic mapping control parameters for each key; Indicates the first preset parameter; Indicates the second preset parameter; Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; and These represent the Logistic mappings at the 1st, 2nd, and 3rd stages, respectively. Second and third The state of the Logistic mapping at the next iteration; Indicates the first The nth student in the hash table The key of the key value of each key; Indicates the preset precision coefficient; Indicates the number of iterations in the Logistic mapping; Indicates the modulo symbol; This indicates the floor function.

[0031] Secondly, embodiments of this application also provide a financial data processing system for improving security and confidentiality. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the methods described above.

[0032] Thirdly, embodiments of this application provide a financial data processing and storage medium that enhances security and confidentiality. The financial data processing and storage medium includes: an encrypted data extraction module, a local similarity evaluation module, an encryption strength evaluation module, and an encryption module.

[0033] The encrypted data extraction module is used to extract tuition payment details and convert them into a hash table;

[0034] The local similarity evaluation module is used to determine the local similarity of different students with adjacent student IDs based on the differences and similarities between the key values ​​of all the same key in the hash table.

[0035] The encryption strength evaluation module is used to determine the global similarity of the same key based on the selection similarity of different students with adjacent student IDs across all keys, and to calculate the encryption level adjustment factor of the student at the same key based on the local similarity of different students with adjacent serial numbers and the global similarity of the students at the same key.

[0036] The encryption module is used to adjust the encryption factor according to the student's encryption level for each key, obtain the key value of the student for each key, and encrypt the key value and the key value to improve the security and confidentiality of the financial data processing system.

[0037] As can be seen from the above embodiments, the financial data processing method, system, and storage medium for improving security and confidentiality provided in this application have at least the following beneficial effects:

[0038] Considering that two students with adjacent student IDs in a school may have a high degree of similarity, but these adjacent student IDs correspond to two completely different students, we cannot evaluate the differences in data information solely based on the differences between different data of the same type. Firstly, when the key corresponding to a key might be student IDs—data with high similarity but corresponding to different student information—we evaluate the similarity of adjacent students at the same key by combining the differences and similarities among all key values ​​of the same key in the hash table, thus determining the local similarity of adjacent students at the same key. Secondly, when the key corresponding to a key might be the same data such as class or payment amount in tuition payment details, we directly use the key value differences to evaluate the similarity of adjacent students at the same key. Local similarity can highlight the information corresponding to key values ​​under different keys in the hash table. The system prioritizes the importance of data, including student ID numbers and similar data types. Furthermore, it evaluates the security of encryption for different key values, determines the global similarity of the same key, and calculates an encryption adjustment factor for each student's key based on local and global similarity. A higher encryption adjustment factor indicates stronger encryption of the corresponding key value. This factor is determined by the importance of the key value and the encryption rate. Finally, based on the student's encryption adjustment factor for each key, the system obtains the key for each student's key value and encrypts the data using the key and its key. This improves the security and confidentiality of the financial data processing system, addressing the problem of dynamically balancing encryption security and computational load based on the importance of different information in the school's financial data. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating the steps of a financial data processing method for improving security and confidentiality, provided in one embodiment of this application;

[0041] Figure 2 This is a schematic diagram of the structure of a financial data processing and storage medium that enhances security and confidentiality, provided as an embodiment of this application. Detailed Implementation

[0042] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the financial data processing method, system, and storage medium for improving security and confidentiality proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] The following, in conjunction with the accompanying drawings, details the specific solutions provided in this application for improving the security and confidentiality of financial data processing methods, systems, and storage media.

[0044] Please see Figure 1 The diagram illustrates a flowchart of a method for improving the security and confidentiality of financial data processing according to an embodiment of this application. The method includes the following steps:

[0045] S001: Extract tuition payment details and convert them into a hash table.

[0046] This application uses the tuition payment details in the school's financial data as an example to encrypt the tuition payment details.

[0047] The tuition payment details record the personal information of each student in each class of the school, such as student ID, name, bank card number, and amount. Specifically, an Excel spreadsheet containing the tuition payment details for all classes in the school is obtained. The data in the Excel spreadsheet is extracted and converted into a hash table. Preferably, as an embodiment of this application, the field names in the first row of the tuition payment details Excel spreadsheet are used as the keys of the hash table, and the data corresponding to the field names in the tuition payment details Excel spreadsheet are used as the key-value pairs. The Excel data is read using the pandas library in Python, the keys and key-value pairs are extracted, and the hashlib library is used to perform hash encoding on the string composed of each key-value pair, such as MD5 encoding, to convert the string into a 128-bit hash value, thus forming the hash table.

[0048] For ease of understanding, the field names in the first row of the tuition payment details Excel spreadsheet are such as "Student ID", "Name", "Tuition Amount", etc. Converting the data into a hash table is a well-known technique and will not be elaborated further.

[0049] At this point, the hash table corresponding to the tuition payment details has been obtained.

[0050] S002: Determine the local similarity of different students with adjacent student IDs based on the differences and similarities between the key values ​​of all the same key in the hash table.

[0051] The tuition payment details, such as class and payment amount, can be found on the school's official website and are considered non-critical data. However, personal information such as students' names, student IDs, and bank card numbers are critical data and require strict encryption.

[0052] In a school, student ID numbers are generally sorted in ascending numerical order. Two students with adjacent ID numbers may have a high degree of similarity, but these adjacent ID numbers represent two completely different students. Therefore, one cannot evaluate the differences in data information solely based on the differences between different types of data.

[0053] Based on the differences and similarities between the key values ​​of different students with adjacent student IDs on all keys of the same key in the hash table, determine the differences and similarities between the key values ​​of different students with adjacent serial numbers on the same key.

[0054] The difference between the key values ​​of different students with adjacent student IDs on the same key in the hash table is denoted as the key value difference of different students with adjacent student IDs on the same key. The similarity between the key values ​​of different students with adjacent student IDs on the same key in the hash table is denoted as the key value similarity of different students with adjacent student IDs on the same key.

[0055] In this embodiment, the ED edit distance is used to measure the difference between key-value pairs of different students for the same key in the hash table, and the Jaccard similarity is used to measure the similarity between key-value pairs of different students for the same key in the hash table. As other implementations, while achieving the goal of measuring the difference between key-value pairs, implementers may use other methods in the prior art, such as the Levevshtein distance, to measure the difference between key-value pairs; this application does not impose any special limitations. The calculation of ED edit distance, Levevshtein distance, and Jaccard similarity are all well-known techniques and will not be described in detail here.

[0056] Calculate the selection similarity of different students with adjacent student IDs for all the same key based on the key value difference and key value similarity.

[0057]

[0058] in, Indicates the first The student and the first The nth student in the hash table The key value difference of each key; Indicates the first The student and the first The student in the first Key-value similarity of each key; Indicates the first The student and the first The nth student in the hash table Selection similarity of individual keys.

[0059] It is important to note that when At that time, the first To ensure the smooth progress of the calculation, each student will... direct assignment The upcoming The student and the first The student in the first The key-value similarity of the nth key is directly assigned to the value of the nth key. The student and the first The student in the first The key-value similarity of each key. For the number of students. It is understandable that the first... The student does not exist; the analysis process encountered an error. Each student is only required to ensure the smooth progress of the calculation.

[0060] The mean of the selection similarity of different students with adjacent serial numbers across all keys is denoted as the local similarity of different students with adjacent serial numbers.

[0061] When the key-value difference between adjacent students with the same key is 1, the Jaccard similarity between the corresponding key-values ​​of adjacent students is 1. In this case, the key might be data such as class or payment amount in tuition payment details, or it might be data like the same outstanding amount or loan amount. Therefore, the key-value difference is directly used to evaluate the similarity of adjacent students with the same key, obtaining the selection similarity. When the key-value difference between adjacent students with the same key is 1, the key might be data like student ID, which has high similarity but corresponds to different student information. Therefore, the product of the key-value difference and the key-value similarity is used to evaluate the similarity of adjacent students with the same key, obtaining the selection similarity. Finally, the mean of the selection similarities of adjacent students across all keys is used to evaluate the similarity of the information of adjacent students, obtaining local similarity. This highlights the importance of the information corresponding to key-values ​​under different keys in the hash table, and also increases the importance of data of the same type as student IDs.

[0062] At this point, the local similarity between different students with adjacent serial numbers is obtained.

[0063] S003: Determine the global similarity of the same key based on the selection similarity of different students with adjacent student IDs across all keys. Calculate the encryption level adjustment factor for each student at the same key based on the local similarity of different students with adjacent serial numbers and the global similarity of students at the same key.

[0064] Furthermore, the outstanding amounts and loan amounts of two students with adjacent student ID numbers in the tuition payment details may be the same, and there may be students with the same name. The local similarity of the outstanding amount, loan amount and name keys corresponding to the hash table of students with adjacent student ID numbers cannot reflect the degree of encryption of these key values. Therefore, it is necessary to further evaluate the confidentiality of encrypting different key values.

[0065] The global similarity of the same key is determined based on the similarity of the selections of different students with adjacent student IDs across all keys.

[0066] For all students with adjacent student IDs, the similarity scores of all selections for the same key are arranged in the order of the student IDs corresponding to the selection similarity scores. The selection similarity sequence for the same key is obtained. A constant similarity sequence of the same length as the selection similarity sequence for the same key is established, and all values ​​in the constant similarity sequence are 1. The cosine similarity between the selection similarity sequence for the same key and the constant similarity sequence is recorded as the global similarity of the same key.

[0067] Understandably, the first In the selection similarity sequence of keys, the selection similarity of each key is as follows: the first student and the second student at the 1st key. The similarity of the selection of each key, the second student and the third student in the first... The similarity of the selection of each key, the third student and the fourth student in the first... The similarity of the selection of each key... The student and the first The student in the first The similarity of selection of each key, the first The student and the first The student in the first The similarity of selection of each key, where... The number of students.

[0068] Based on the local similarity of different students with adjacent serial numbers and the global similarity of students on the same key, calculate the encryption level adjustment factor for students on the same key.

[0069]

[0070] in, Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; Indicates the first The student and the first Local similarity of individual students; Indicates the first The nth student in the hash table Global similarity of each key; This represents the normalization function.

[0071] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In practical applications, implementers may use other methods of existing technology, such as the Z-Score standard normalization method or the sigmoid function, to calculate the normalized value, and no limitation is made here.

[0072] The smaller the difference in key values ​​of adjacent students on the same key, and the smaller the global similarity of students on the same key, the lower the similarity between the key values ​​of corresponding keys. Therefore, it is more necessary to strengthen the encryption of the key values ​​of corresponding keys. In this case, the encryption level adjustment factor of corresponding keys should be larger.

[0073] At this point, the encryption level adjustment factor for each student at each key is obtained.

[0074] S004: Based on the encryption level adjustment factor of each student for each key, obtain the key value of each student for each key, and encrypt the key value and the key value to improve the security and confidentiality of the financial data processing system.

[0075] Based on the encryption level adjustment factor for each key, obtain the key value for each key set by the student.

[0076]

[0077]

[0078]

[0079] in, Indicates the first The nth student in the hash table Logistic mapping control parameters for each key; This represents the first preset parameter, which is the minimum value of the Logistic mapping control parameter. In this embodiment, the value of the first preset parameter is 3.57. This represents the second preset parameter, which is the maximum value of the Logistic mapping control parameter. In this embodiment, the value of the second preset parameter is 4. Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; Represents the Logistic mapping. The state of the Logistic mapping at the next iteration is obtained using a well-known technique, which will not be elaborated further. Represents the Logistic mapping. The state of the Logistic mapping at the next iteration; Indicates the first The nth student in the hash table The key of the key value of each key; This represents the preset precision coefficient. In this embodiment, the precision coefficient is set to 256. The precision coefficient ensures that the key has more possible values. This represents the number of iterations in the Logistic mapping. In this embodiment, the total number of iterations in the Logistic mapping is set to 256, i.e. ; Indicates the modulo symbol; This indicates the floor function.

[0080] The larger the Logistic mapping control parameter of the corresponding key in the hash table, the higher the protection of the encrypted key-value pair under the corresponding key should be, and the greater the complexity of the key should be.

[0081] Logistic mapping is a well-known technique and will not be elaborated further.

[0082] Encryption is performed based on the key value and the key key, thereby improving the security and confidentiality of the financial data processing system.

[0083] All students' keys in the hash table are treated as plaintext, and the key corresponding to each key is used as the ciphertext. The plaintext is then encrypted using the ECC encryption algorithm to obtain the ciphertext. To decrypt the ciphertext, the corresponding key in the hash table is decrypted using the key associated with each key.

[0084] The use of ECC encryption algorithm for encryption and decryption are well-known technologies and will not be elaborated further.

[0085] This improves the security and confidentiality of the financial data processing system.

[0086] This application also provides a financial data processing system for improving security and confidentiality. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for improving security and confidentiality in financial data processing.

[0087] Based on the same inventive concept as the methods described above, embodiments of this application also provide a financial data processing and storage medium to enhance security and confidentiality. Please refer to [link to relevant documentation]. Figure 2 , Figure 2This is a schematic diagram of the structure of a financial data processing storage medium for improving security and confidentiality, provided in one embodiment of this application. In this embodiment, the units included in the financial data processing storage medium are used to execute the steps in the embodiment corresponding to the financial data processing method for improving security and confidentiality. See also... Figure 2 The financial data processing and storage medium includes: an encrypted data extraction module, a local similarity evaluation module, an encryption strength evaluation module, and an encryption module.

[0088] The encrypted data extraction module is used to extract tuition payment details and convert them into a hash table;

[0089] The local similarity evaluation module is used to determine the local similarity of different students with adjacent student IDs based on the differences and similarities between the key values ​​of all the same key in the hash table.

[0090] The encryption strength evaluation module is used to determine the global similarity of the same key based on the selection similarity of different students with adjacent student IDs across all keys, and to calculate the encryption level adjustment factor of the student at the same key based on the local similarity of different students with adjacent serial numbers and the global similarity of the students at the same key.

[0091] The encryption module is used to adjust the encryption factor according to the student's encryption level for each key, obtain the key value of the student for each key, and encrypt the key value and the key value to improve the security and confidentiality of the financial data processing system.

[0092] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0093] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0094] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A method for processing financial data to improve security and confidentiality, characterized in that, The method includes the following steps: Extract tuition payment details and convert them into a hash table; the tuition payment details record the personal information of each student in each class of the school; Based on the differences and similarities between the key values ​​of different students with adjacent student IDs at all keys in the hash table, the key value differences and key value similarities of different students with adjacent student IDs at the same key are determined. Based on the key value differences and key value similarities of different students with adjacent student IDs at all keys, the selection similarity of different students with adjacent student IDs at all keys is calculated. The mean of the selection similarities of different students with adjacent student IDs at all keys is denoted as the local similarity of different students with adjacent student IDs. The specific process for obtaining the key value differences and key value similarities is as follows: the differences between the key values ​​of different students with adjacent student IDs at the same key in the hash table are denoted as the key value differences of different students with adjacent student IDs at the same key; the similarities between the key values ​​of different students with adjacent student IDs at the same key in the hash table are denoted as the key value similarity of different students with adjacent student IDs at the same key. The method for calculating the selection similarity is as follows: in, Indicates the first The student and the first The nth student in the hash table The key value difference of each key; Indicates the first The student and the first The student in the first Key-value similarity of each key; Indicates the first The student and the first The nth student in the hash table Selection similarity of individual keys; Based on the similarity of different students with adjacent student IDs across all the same key selections, the global similarity of the same key is determined. Based on the local similarity of different students with adjacent serial numbers and the global similarity of students at the same key, the encryption level adjustment factor of the student at the same key is calculated. Based on the encryption level adjustment factor of each student for each key, the key value of each student's key for each key is obtained, and encryption is performed based on the key value and the key value, thereby improving the security and confidentiality of the financial data processing system; The formula for calculating the encryption level adjustment factor for the student using the same key is: in, Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; Indicates the first The student and the first Local similarity of individual students; Indicates the first The nth student in the hash table Global similarity of each key; This represents the normalization function.

2. The financial data processing method for improving security and confidentiality as described in claim 1, characterized in that, The keys of the hash table are the field names of the tuition payment details.

3. The financial data processing method for improving security and confidentiality as described in claim 1, characterized in that, The specific process for calculating the global similarity of the same key is as follows: Based on the selection similarity of all students with adjacent student IDs across all keys, a selection similarity sequence for the same key is established. A constant similarity sequence of the same length as the selection similarity sequence for the same key is also established. The cosine similarity between the selection similarity sequence for the same key and the constant similarity sequence is denoted as the global similarity of the same key.

4. The financial data processing method for improving security and confidentiality as described in claim 1, characterized in that, The specific formula for calculating the key value of each key for the student is as follows: in, Indicates the first The nth student in the hash table Logistic mapping control parameters for each key; Indicates the first preset parameter; This indicates the second preset parameter; Indicates the first The nth student in the hash table The encryption level adjustment factor for each key; and These represent the Logistic mappings at the 1st, 2nd, and 3rd stages, respectively. Second and third The state of the Logistic mapping at the next iteration; Indicates the first The nth student in the hash table The key of the key value of each key; Indicates the preset precision coefficient; Indicates the number of iterations in the Logistic mapping; Indicates the modulo symbol; This indicates the floor function.

5. A financial data processing system for improving security and confidentiality, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-4.

6. A financial data processing storage medium implementing the method as described in any one of claims 1-4, characterized in that, The financial data processing and storage medium includes: The encrypted data extraction module is used to extract tuition payment details and convert them into a hash table; The local similarity evaluation module is used to determine the local similarity of different students with adjacent student IDs based on the differences and similarities between the key values ​​of all the same key in the hash table. The encryption strength evaluation module is used to determine the global similarity of the same key based on the selection similarity of different students with adjacent student IDs across all keys, and to calculate the encryption level adjustment factor of the student at the same key based on the local similarity of different students with adjacent serial numbers and the global similarity of the students at the same key. The encryption module is used to adjust the encryption factor according to the student's encryption level for each key, obtain the key value of the student for each key, and encrypt the key value and the key value to improve the security and confidentiality of the financial data processing system.

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