Transaction data encryption method and device, equipment and storage medium
Financial transaction data is encrypted using a double perturbation noise encryption method, which solves the problem of large computing resources occupied by machine learning encryption methods and achieves efficient and secure data protection.
Patent Information
- Application Number
- CN202510790418.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-13
- Publication Date
- 2025-09-30
AI Technical Summary
The existing financial transaction data encryption method based on machine learning consumes a lot of computing resources, resulting in low encryption efficiency.
Initial perturbation parameters and a random array are used to generate the initial perturbation noise, and the initial encrypted data is generated by combining the initial perturbation noise and transaction data. Then, the initial perturbation parameters and a random array are used to generate the secondary perturbation noise, and the initial encrypted data is encrypted again to form the target encrypted data.
It improves the encryption security of financial transaction data, reduces the risk of data leakage, avoids excessive occupation of computing resources, and improves encryption efficiency.
Smart Images
Figure CN120729562A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of financial technology, and in particular to a transaction data encryption method, device, equipment and storage medium. Background Art
[0002] Users will use the banking system to conduct financial transactions based on their own needs. Corresponding financial transaction data will be generated during the financial transaction process. Since financial transaction data is raw data and there is a certain risk of leakage, in order to protect the privacy of users, the generated financial transaction data needs to be encrypted. In this way, financial institutions can analyze and process users' financial transaction data without directly accessing users' financial transaction data. This can reduce the risk of leakage of users' financial transaction data and help financial institutions to conduct risk control on financial transaction data.
[0003] Currently, encryption algorithms based on machine learning are often used to encrypt financial transaction data. However, machine learning itself takes up a lot of computing resources, and combining it with encryption technology will further increase the amount of computing resources occupied. The computing resources allocated by financial institutions to encryption algorithms based on machine learning are limited, resulting in low efficiency in encrypting financial transaction data based on machine learning. Summary of the Invention
[0004] In order to improve the efficiency of encrypting financial transaction data, the present application provides a transaction data encryption method, apparatus, device and storage medium.
[0005] In a first aspect, the present application provides a transaction data encryption method, comprising:
[0006] Generating an initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution;
[0007] generating initial encrypted data based on the initial disturbance noise and transaction data;
[0008] generating secondary disturbance noise based on the initialization disturbance parameter and the random array;
[0009] Target encrypted data is generated based on the initial encrypted data and the secondary disturbance noise.
[0010] Through the above implementation, the primary disturbance noise is first generated to achieve the initial encryption of the transaction data. At this time, the security of the encrypted transaction data still needs to be improved, so the secondary disturbance noise is generated again, and the transaction data after the initial encryption is re-encrypted by the secondary disturbance noise, thereby greatly reducing the risk of transaction data leakage. In addition, the above method does not require the help of machine learning, and the encryption steps are simple, without occupying a large amount of computing resources, thereby facilitating the improvement of the efficiency of encrypting financial transaction data.
[0011] Preferably, generating secondary disturbance noise based on the initialization disturbance parameter and the random array includes:
[0012] Generate a first random number and a second random number based on the initialization perturbation parameter and the random array;
[0013] A secondary disturbance noise is generated based on the first random number and the second random number.
[0014] Through the above implementation, secondary disturbance noise is generated based on the generation of primary disturbance noise; this facilitates double encryption of transaction data through the primary disturbance noise and the secondary disturbance noise, thereby improving the security of transaction data.
[0015] Preferably, generating target encrypted data based on the initial encrypted data and the secondary disturbance noise includes:
[0016] generating intermediate encrypted data based on the initial encrypted data and a preset data coefficient;
[0017] Target encrypted data is generated based on the intermediate encrypted data and the secondary disturbance noise.
[0018] Through the above implementation, by encrypting the intermediate encrypted data with secondary perturbation noise, double encryption of transaction data can be achieved to improve the security of transaction data.
[0019] Preferably, after generating target encrypted data based on the initial encrypted data and the secondary disturbance noise, the method further includes:
[0020] generating an initial training set based on the target encrypted data;
[0021] Based on the initial training set and the preset initial security detection model, a target security detection model is obtained;
[0022] The target encrypted data is detected based on the target security detection model.
[0023] Through the above implementation, the target security detection model is obtained by training the initial security detection model with the target encrypted data; this facilitates further detection of whether there are transaction risks in the subsequently generated transaction data by generating the target security detection model.
[0024] Preferably, obtaining a target security detection model based on the initial training set and a preset initial security detection model includes:
[0025] Preprocessing the initial training set to obtain a target training set;
[0026] Training a preset initial security detection model based on the target training set to obtain an intermediate security detection model;
[0027] A target safety detection model is obtained based on the intermediate safety detection model and the initialization disturbance parameters.
[0028] Through the above implementation, the target safety detection model is determined by the intermediate safety detection model and the initialization disturbance parameters, so that the intermediate safety detection model can be optimized by the initialization disturbance parameters.
[0029] Preferably, obtaining a target safety detection model based on the intermediate safety detection model and the initialization disturbance parameter includes:
[0030] In response to the performance of the intermediate safety detection model not meeting the standard, adjusting the initialization disturbance parameter to obtain an intermediate disturbance parameter;
[0031] generating a new intermediate safety detection model based on the intermediate disturbance parameters;
[0032] In response to the new intermediate safety detection model achieving performance standards, a target safety detection model is obtained.
[0033] Through the above implementation, the initialization perturbation parameters are adjusted according to the performance of the intermediate safety detection model, and the intermediate safety detection model is regenerated using the adjusted initialization perturbation parameters until the target safety detection model is obtained; this makes it easier to ensure the detection performance of the target safety detection model.
[0034] In a second aspect, the present application provides a transaction data encryption device, comprising:
[0035] An initial noise generation module, configured to generate an initial disturbance noise based on an initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution;
[0036] A primary encryption module, configured to generate initial encrypted data based on the primary disturbance noise and transaction data;
[0037] A secondary noise generating module, configured to generate secondary disturbance noise based on the initialization disturbance parameter and the random array;
[0038] The secondary encryption module is configured to generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0039] Through the above implementation, the primary disturbance noise is first generated to achieve the initial encryption of the transaction data. At this time, the security of the encrypted transaction data still needs to be improved, so the secondary disturbance noise is generated again, and the transaction data after the initial encryption is re-encrypted by the secondary disturbance noise, thereby greatly reducing the risk of transaction data leakage. In addition, the above method does not require the help of machine learning, and the encryption steps are simple, without occupying a large amount of computing resources, thereby facilitating the improvement of the efficiency of encrypting financial transaction data.
[0040] In a third aspect, the present application provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above method when executing the computer program.
[0041] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method when executed by a processor.
[0042] In a fifth aspect, the present application further provides a computer program product, which includes a computer program that implements the steps of any of the above method embodiments when executed by a processor.
[0043] The above-mentioned transaction data encryption method, device, equipment and storage medium generate primary disturbance noise based on initialization disturbance parameters and a random array; wherein, the array elements in the random array conform to a uniform distribution; generate initial encrypted data based on the primary disturbance noise and transaction data; generate secondary disturbance noise based on the initialization disturbance parameters and the random array; and generate target encrypted data based on the initial encrypted data and the secondary disturbance noise. Through the above implementation, the primary disturbance noise is first generated to achieve the initial encryption of the transaction data. At this time, the security of the encrypted transaction data still needs to be improved, so the secondary disturbance noise is generated again, and the transaction data after the initial encryption is re-encrypted by the secondary disturbance noise, thereby greatly reducing the risk of transaction data leakage. In addition, the above method does not require the help of machine learning, and the encryption steps are simple, and do not require a large amount of computing resources, thereby facilitating the improvement of the efficiency of encrypting financial transaction data.
[0044] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.
[0046] Figure 1 This is a flow chart of a transaction data encryption method provided in an embodiment of the present application;
[0047] Figure 2 This is a structural diagram of a transaction data encryption device provided in an embodiment of the present application;
[0048] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application;
[0049] Figure 4 This is a diagram of the internal structure of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION
[0050] In order to make the purpose, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not intended to limit the present disclosure.
[0051] It should be noted that the terms "first," "second," and the like in the specification and claims herein and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or devices.
[0052] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" could mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the related objects.
[0053] Example 1
[0054] Figure 1This is a flowchart of a transaction data encryption method provided in Example 1 of this application, refer to Figure 1 The method may be performed by a device for performing the method, and the device may be implemented by software and / or hardware. The method includes:
[0055] S110 , generating an initial disturbance noise based on the initial disturbance parameters and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0056] Among them, when a user conducts a financial transaction through the banking system, corresponding transaction data will be generated in the banking system. In order to prevent the transaction data from being leaked, the banking system needs to encrypt the transaction data so that it can analyze and process the user's financial transaction data without directly accessing the user's financial transaction data. This embodiment further superimposes the set noise on the transaction data to achieve encryption of the transaction data. To generate the noise, this embodiment presets initialization disturbance parameters, which include sensitivity and privacy parameters, wherein the sensitivity is used to affect the size of the noise, and the greater the sensitivity, the greater the noise; the privacy parameter is used to characterize the acceptable amount of data leakage; this embodiment generates a random array each time, and the generated random array can be used to determine the calculation method for further calculating the sensitivity and privacy parameters in the initialization disturbance parameters. By calculating the sensitivity and privacy parameters, the noise data corresponding to the random array generated this time can be obtained; and the multiple noise data corresponding to the multiple random arrays generated are recorded as the initial disturbance noise.
[0057] It should be noted that the elements in the random array are array elements, and the array elements in the random array all conform to a uniform distribution; this uniform distribution ensures the randomness generated by each array element in the random array; the initial disturbance noise generated by the random array also has corresponding randomness, and the initial disturbance noise is subsequently used to encrypt transaction data. This randomness facilitates improving the security of the encrypted transaction data.
[0058] Specifically, the sensitivity in the initialization perturbation parameter is denoted as f, the privacy parameter is denoted as ω, and the ratio of the sensitivity f to the privacy parameter ω is denoted as t; the above random array contains two array elements, denoted as r1 and r2 respectively; after generating a random array, if r1 is not greater than 0.62, then the noise corresponding to the random array noisy = -t*ln(1-r2); if r1 is greater than 0.62, then the noise corresponding to the random array noisy = t*ln(r2).
[0059] S120: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0060] The initial disturbance noise is used to perform bitwise AND operation with the transaction data, thereby encrypting the transaction data, and the encrypted data obtained after encrypting the transaction data with the initial disturbance noise is recorded as the initial encrypted data.
[0061] S130 , generating secondary disturbance noise based on the initialization disturbance parameter and the random array.
[0062] Among them, the initialization disturbance parameter also includes a failure probability p, which is an initialized preset value; this embodiment generates a random array each time, and the random array contains two array elements; the failure probability p in the initialization disturbance parameter is processed by the random array, and a noise data corresponding to the random array can be calculated, and the noise data corresponding to each random array is recorded as secondary disturbance noise.
[0063] S140: Generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0064] The secondary perturbation noise is used to perform bitwise AND operation with the initial encrypted data, thereby realizing encryption operation of the initial encrypted data by the secondary perturbation noise, and the encrypted data obtained by the encryption operation of the initial encrypted data by the secondary perturbation noise is recorded as target encrypted data.
[0065] It should be noted that this embodiment generates a primary disturbance noise based on an initialization disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution; generates initial encrypted data based on the primary disturbance noise and transaction data; generates secondary disturbance noise based on the initialization disturbance parameter and the random array; and generates target encrypted data based on the initial encrypted data and the secondary disturbance noise. Through the above implementation, the primary disturbance noise is first generated to achieve the initial encryption of the transaction data. At this time, the security of the encrypted transaction data still needs to be improved, so the secondary disturbance noise is generated again, and the transaction data after the initial encryption is re-encrypted by the secondary disturbance noise, thereby greatly reducing the risk of transaction data leakage. In addition, the above method does not require the help of machine learning, and the encryption steps are simple, and do not require a large amount of computing resources, thereby facilitating the improvement of the efficiency of encrypting financial transaction data.
[0066] Example 2
[0067] A transaction data encryption method provided in a second embodiment of the present application optimizes the "generating secondary disturbance noise based on the initialization disturbance parameter and the random array" in the first embodiment. It should be noted that for portions not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:
[0068] S210 , generating an initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0069] S220: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0070] S231. Generate a first random number and a second random number based on the initialization perturbation parameter and the random number array.
[0071] In this embodiment, the initialized perturbation parameters include sensitivity f, privacy parameter denoted as ω, and failure probability p; the random array contains two array elements, denoted as r1 and r2 respectively; a random number can be calculated through the two array elements r1 and r2, and the random number is denoted as the first random number z1; the second random number z2 can be calculated by initializing the sensitivity f, privacy parameter denoted as ω, and failure probability p in the perturbation parameters; it should be noted that the first random number and the second random number are subsequently used to calculate a noise corresponding to the random array generated this time.
[0072] Specifically, the calculation formula of the first random number z1 is as follows:
[0073] z1=sqrt(-2*ln(r1)*cos(2*r2*π));
[0074] Specifically, the calculation formula of the second random number z2 is as follows:
[0075] z2=sqrt(2*ln(1.25 / p)*f / ω).
[0076] S232: Generate secondary disturbance noise based on the first random number and the second random number.
[0077] Taking the random array generated once as an example, the random array calculates its corresponding first random number z1 and second random number z2 through the above steps; further, the product of the first random number z1 and the second random number z2 is calculated, and the product is recorded as the noise corresponding to the random array; and the multiple noises corresponding to the random array generated each time are recorded as secondary perturbation noise.
[0078] S240: Generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0079] Example 3
[0080] A transaction data encryption method is provided in a third embodiment of the present application. This method optimizes the "generating target encrypted data based on the initial encrypted data and the secondary disturbance noise" in the first embodiment. It should be noted that for portions not described in detail in this embodiment, reference may be made to the descriptions in other embodiments. The method includes:
[0081] S310 , generating an initial disturbance noise based on the initial disturbance parameters and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0082] S320: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0083] S330: Generate secondary disturbance noise based on the initialization disturbance parameter and the random array.
[0084] S341. Generate intermediate encrypted data based on the initial encrypted data and preset data coefficients.
[0085] The intermediate encrypted data is the data obtained by multiplying each bit of the initial encrypted data by a preset data coefficient. The preset data coefficient α is specifically 0.65 in this embodiment, and is not specifically limited in other embodiments.
[0086] S342. Generate target encrypted data based on the intermediate encrypted data and the secondary disturbance noise.
[0087] The target encrypted data is the data obtained by bitwise summing the intermediate encrypted data and the secondary perturbation noise.
[0088] Example 4
[0089] A transaction data encryption method is provided in a fourth embodiment of the present application. This method supplements the steps after "generating target encrypted data based on the initial encrypted data and the secondary disturbance noise" in the first embodiment. It should be noted that for portions not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:
[0090] S410 , generating an initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0091] S420: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0092] S430: Generate secondary disturbance noise based on the initialization disturbance parameter and the random array.
[0093] S440: Generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0094] S450: Generate an initial training set based on the target encrypted data.
[0095] Among them, the user's transaction data may be data with certain security risks. In order to facilitate the judgment of whether the user's transaction data is safe through the transaction data, this application can train the security detection model through the target encrypted data authorized by the user. To this end, it is necessary to separate the training data used for model training from the target encrypted data, and record the training data as the initial training set.
[0096] S460: Obtain a target security detection model based on the initial training set and a preset initial security detection model.
[0097] Among them, the above-mentioned untrained security detection model is recorded as the initial security detection model; the initial security detection model can be trained through the initial training set, so that the trained initial security detection model has good security detection capabilities, and the new model obtained after the initial security detection model is trained is recorded as the target security detection model.
[0098] S470: Detect the target encrypted data based on the target security detection model.
[0099] The target security detection model is used to detect the target encrypted data to determine whether the transaction data corresponding to the target encrypted data is safe.
[0100] Example 5
[0101] A transaction data encryption method is provided in a fifth embodiment of the present application. This method optimizes the "obtaining a target security detection model based on the initial training set and a preset initial security detection model" in the fourth embodiment. It should be noted that for portions not described in detail in this embodiment, reference may be made to the descriptions in other embodiments. This method includes:
[0102] S510 , generating an initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0103] S520: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0104] S530: Generate secondary disturbance noise based on the initialization disturbance parameter and the random array.
[0105] S540: Generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0106] S550: Generate an initial training set based on the target encrypted data.
[0107] S561: Preprocess the initial training set to obtain a target training set.
[0108] It should be noted that some data in the initial training set may have problems such as missing data, data duplication, and data anomalies. Therefore, the initial training set needs to be preprocessed to fill in missing data, remove duplicate data, and correct abnormal data; and the new training set obtained after the preprocessing of the initial training set is recorded as the target training set.
[0109] S562: Train a preset initial security detection model based on the target training set to obtain an intermediate security detection model.
[0110] The target training set is used to iteratively train the initial security detection model, and the new model obtained after the iterative training of the initial security detection model is recorded as the intermediate security detection model.
[0111] S563: Obtain a target safety detection model based on the intermediate safety detection model and the initialization disturbance parameter.
[0112] Among them, the initialization perturbation parameters can be used to tune the intermediate safety detection model to ensure that the model performance of the intermediate safety detection model reaches the best, and the tuned intermediate safety detection model is recorded as the target safety detection model.
[0113] S570: Detect the target encrypted data based on the target security detection model.
[0114] Example 6
[0115] A transaction data encryption method is provided in Example 6 of the present application. This method optimizes the "obtaining a target security detection model based on the intermediate security detection model and the initialization perturbation parameter" in Example 5. It should be noted that for portions not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. This method includes:
[0116] S610: Generate initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution.
[0117] S620: Generate initial encrypted data based on the initial disturbance noise and transaction data.
[0118] S630: Generate secondary disturbance noise based on the initialization disturbance parameter and the random array.
[0119] S640: Generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0120] S650: Generate an initial training set based on the target encrypted data.
[0121] S661. Preprocess the initial training set to obtain a target training set.
[0122] S662: Train a preset initial security detection model based on the target training set to obtain an intermediate security detection model.
[0123] S663A: In response to the performance of the intermediate safety detection model not meeting the standards, adjust the initialization disturbance parameter to obtain an intermediate disturbance parameter.
[0124] Among them, the target encrypted data can not only be divided into training data, but also into corresponding test data. The test data is used to determine whether the performance of the intermediate security detection model meets the standards. If it does not meet the standards, it means that the intermediate security detection model cannot be used for security detection; at this time, it is necessary to re-optimize and adjust the initialization perturbation parameters, and the optimized and adjusted initialization perturbation parameters are recorded as intermediate perturbation parameters.
[0125] S663B. Generate a new intermediate safety detection model based on the intermediate disturbance parameters.
[0126] The intermediate disturbance parameters may be used as new initialization disturbance parameters, and the corresponding new intermediate safety detection model may be regenerated according to steps S610-S662.
[0127] S663C: In response to the new intermediate safety detection model meeting the performance standard, a target safety detection model is obtained.
[0128] Among them, if it is determined through test data that the performance of the new intermediate safety detection model meets the requirements, the new intermediate safety detection model obtained at this time can be recorded as the target safety detection model.
[0129] S670: Detect the target encrypted data based on the target security detection model.
[0130] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.
[0131] Example 7
[0132] Based on the same inventive concept, this embodiment also provides a transaction data encryption device for implementing the transaction data encryption method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more transaction data encryption device embodiments provided below can be found in the limitations of the transaction data encryption method described above and will not be further elaborated here.
[0133] In this embodiment, Figure 2 As shown, a transaction data encryption device is provided, comprising:
[0134] An initial noise generation module, configured to generate an initial disturbance noise based on an initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution;
[0135] A primary encryption module, configured to generate initial encrypted data based on the primary disturbance noise and transaction data;
[0136] A secondary noise generating module, configured to generate secondary disturbance noise based on the initialization disturbance parameter and the random array;
[0137] The secondary encryption module is configured to generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
[0138] Each module in the transaction data encryption device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.
[0139] It should be noted that this embodiment generates a primary disturbance noise based on an initialization disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution; generates initial encrypted data based on the primary disturbance noise and transaction data; generates secondary disturbance noise based on the initialization disturbance parameter and the random array; and generates target encrypted data based on the initial encrypted data and the secondary disturbance noise. Through the above implementation, the primary disturbance noise is first generated to achieve the initial encryption of the transaction data. At this time, the security of the encrypted transaction data still needs to be improved, so the secondary disturbance noise is generated again, and the transaction data after the initial encryption is re-encrypted by the secondary disturbance noise, thereby greatly reducing the risk of transaction data leakage. In addition, the above method does not require the help of machine learning, and the encryption steps are simple, and do not require a large amount of computing resources, thereby facilitating the improvement of the efficiency of encrypting financial transaction data.
[0140] In an optional embodiment, in terms of generating secondary disturbance noise based on the initialization disturbance parameter and the random array, the secondary noise generation module is specifically configured to:
[0141] Generate a first random number and a second random number based on the initialization perturbation parameter and the random array;
[0142] A secondary disturbance noise is generated based on the first random number and the second random number.
[0143] In an optional embodiment, in terms of generating target encrypted data based on the initial encrypted data and the secondary disturbance noise, the secondary encryption module is specifically configured to:
[0144] generating intermediate encrypted data based on the initial encrypted data and a preset data coefficient;
[0145] Target encrypted data is generated based on the intermediate encrypted data and the secondary disturbance noise.
[0146] In an optional embodiment, the transaction data encryption device further includes:
[0147] A training set generation module, configured to generate an initial training set based on the target encrypted data;
[0148] A target model determination module is used to obtain a target safety detection model based on the initial training set and a preset initial safety detection model;
[0149] A data detection module is used to detect the target encrypted data based on the target security detection model.
[0150] In an optional embodiment, in terms of obtaining a target security detection model based on the initial training set and a preset initial security detection model, the target model determination module is specifically configured to:
[0151] Preprocessing the initial training set to obtain a target training set;
[0152] Training a preset initial security detection model based on the target training set to obtain an intermediate security detection model;
[0153] A target safety detection model is obtained based on the intermediate safety detection model and the initialization disturbance parameters.
[0154] In an optional embodiment, in terms of obtaining the target safety detection model based on the intermediate safety detection model and the initialization disturbance parameter, the target model determination module is specifically configured to:
[0155] In response to the performance of the intermediate safety detection model not meeting the standard, adjusting the initialization disturbance parameter to obtain an intermediate disturbance parameter;
[0156] generating a new intermediate safety detection model based on the intermediate disturbance parameters;
[0157] In response to the new intermediate safety detection model achieving performance standards, a target safety detection model is obtained.
[0158] Example 8
[0159] In this embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a transaction data encryption method.
[0160] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.
[0161] Example 9
[0162] In this embodiment, a computer readable storage medium is provided. Figure 4 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
[0163] Example 10
[0164] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0165] It should be noted that the collected user data involved in this disclosure is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.
[0166] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.
[0167] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in the present disclosure may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this disclosure may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in each embodiment provided in this disclosure may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc.
[0168] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0169] The above-described embodiments merely represent several implementation methods of the present disclosure. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present disclosure. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present disclosure, all of which fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be determined by the appended claims.
Claims
1. A transaction data encryption method, characterized in that: include: Generating an initial disturbance noise based on the initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution; generating initial encrypted data based on the initial disturbance noise and transaction data; generating secondary disturbance noise based on the initialization disturbance parameter and the random array; Target encrypted data is generated based on the initial encrypted data and the secondary disturbance noise.
2. The method according to claim 1, characterized in that Generating secondary disturbance noise based on the initialization disturbance parameter and the random array includes: Generate a first random number and a second random number based on the initialization perturbation parameter and the random array; A secondary disturbance noise is generated based on the first random number and the second random number.
3. The method according to claim 1, characterized in that The generating target encrypted data based on the initial encrypted data and the secondary disturbance noise includes: generating intermediate encrypted data based on the initial encrypted data and a preset data coefficient; Target encrypted data is generated based on the intermediate encrypted data and the secondary disturbance noise.
4. The method according to claim 1, wherein After generating target encrypted data based on the initial encrypted data and the secondary disturbance noise, the method further includes: generating an initial training set based on the target encrypted data; Based on the initial training set and the preset initial security detection model, a target security detection model is obtained; The target encrypted data is detected based on the target security detection model.
5. The method according to claim 4, characterized in that The step of obtaining a target security detection model based on the initial training set and a preset initial security detection model includes: Preprocessing the initial training set to obtain a target training set; Training a preset initial security detection model based on the target training set to obtain an intermediate security detection model; A target safety detection model is obtained based on the intermediate safety detection model and the initialization disturbance parameters.
6. The method according to claim 5, characterized in that The obtaining of a target safety detection model based on the intermediate safety detection model and the initialization disturbance parameter includes: In response to the performance of the intermediate safety detection model not meeting the standard, adjusting the initialization disturbance parameter to obtain an intermediate disturbance parameter; generating a new intermediate safety detection model based on the intermediate disturbance parameters; In response to the new intermediate safety detection model achieving performance standards, a target safety detection model is obtained.
7. A transaction data encryption device, characterized in that: The device comprises: An initial noise generation module, configured to generate an initial disturbance noise based on an initial disturbance parameter and a random array; wherein the array elements in the random array conform to a uniform distribution; A primary encryption module, configured to generate initial encrypted data based on the primary disturbance noise and transaction data; A secondary noise generating module, configured to generate secondary disturbance noise based on the initialization disturbance parameter and the random array; The secondary encryption module is configured to generate target encrypted data based on the initial encrypted data and the secondary disturbance noise.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.