Online payment security management method

Abstract

The invention discloses an online payment security management method, and the method comprises the steps: obtaining a payment data set generated by the online payment of a target user in a preset historical time period, dividing all payment data in the payment data set into corresponding time attribute groups based on the generation time nodes of the payment data, obtaining a first sensitive value of each time attribute group based on a first sensitive index algorithm; according to a variable coefficient sensitivity evaluation algorithm, obtaining a second sensitive value of each data type in the payment data set; based on the first sensitive value of the time attribute group and the second sensitive value of the data type, outputting M time sensitive classes and N type sensitive classes; and generating multiple classes of decryption information by using a preset cross-level class decryption mechanism, and matching the class decryption information as target decryption information according to the authorization levels of the target user and the payment service bank end. Therefore, the payment data sensitive degree is evaluated in a multi-dimensional cross mode, and the payment data sharing safety and reliability are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of online payment, and in particular to an online payment security management method. Background Art

[0002] Online payment, also known as electronic payment or digital payment, is a payment method completed through electronic channels such as the Internet. Online payment can greatly improve consumers' shopping experience and efficiency, saving time and energy. Since online payment may be affected by network security issues such as hacker attacks, data leakage, and transaction fraud, user transaction information is often leaked. Therefore, how to protect user privacy and information security is a very critical link. The shortcomings of existing user transaction data management methods are: low security in data storage and call.

[0003] The Chinese invention patent with patent application number 202311354951.4 discloses a security supervision method and system for online payment. The transaction bill data set of the target user is sent to the security supervision platform for encryption, and the information sensitivity analysis of the transaction bill data set is performed to obtain the sensitivity index. The level and quantity of multi-level decryption information are configured based on the sensitivity index. Different transaction bill information is displayed after each level of decryption. According to the authorization of the target user to each payroll bank, the first decryption information containing the decryption information level is determined and sent to the payroll bank, and the payroll bank receives the decryption information containing a specific level. Ensure the security and privacy protection of transaction bill data.

[0004] However, only focusing on the sensitivity analysis of different data items ignores the time point when the transaction bill is generated. The transaction habits of users may change dynamically at different times. The limitations of sensitivity analysis based only on data items may not be enough to fully reflect the sensitivity of transaction bills. For example, a user may frequently make large payments in a certain period of time, while mainly small payments in another period of time. If the time dimension is ignored and all data items that can be displayed in the decrypted information are shared with the bank, the user's sensitive information may be exposed at an inappropriate time. Summary of the invention

[0005] This application provides an online payment security management method to cross-evaluate the sensitivity of payment data in multiple dimensions, thereby further improving the security and reliability of payment data sharing.

[0006] This application provides an online payment security management method, including:

[0007] S101, obtaining a payment data set generated by online payments of a target user within a preset historical time period, the payment data set including payment data generated at different time nodes, the payment data at each time node including data corresponding to several data types, the data types including payment amount, product type, and payment duration;

[0008] S102, based on the preset time attribute groups, all payment data in the payment data set are divided into corresponding time attribute groups based on their generation time nodes, each time attribute group includes at least one payment data, each payment data corresponds to a payment time node, and a first sensitive value of each time attribute group is obtained based on a first sensitive indicator algorithm;

[0009] S103, obtaining a second sensitive value for each data type in the payment data set according to a preset coefficient of variation sensitivity evaluation algorithm;

[0010] S104, based on the first sensitive values ​​of all time attribute groups and the second sensitive values ​​of all data types, using a preset clustering algorithm to analyze and classify all time attribute groups and all data types, and output M time-sensitive classes and N type-sensitive classes respectively;

[0011] S105, uploading the payment data set of the target user to the security management platform to generate encryption information, using the encryption information to encrypt to obtain payment ciphertext, and using a preset cross-class decryption mechanism to generate multiple types of decryption information, each type of decryption information decrypts the payment ciphertext to obtain payment data corresponding to at least one data type;

[0012] S106, obtain the payment service bank end that has established a binding relationship with the target user, and according to the authorization level of the target user and the payment service bank end, match one or more types of decryption information among multiple types of decryption information as target decryption information and send them to the corresponding payment service bank end.

[0013] Preferably, the preset time attribute group is used to represent different payment time background types for target users, and based on the generation time node of each payment data, all payment data are classified according to the time attribute group to which it belongs.

[0014] Preferably, the first sensitive indicator algorithm is set as:

[0015]

[0016] Among them, SI is the first sensitive value of the time attribute group, SI i is the sensitive value of the i-th payment data in the time attribute group, w i is the weight factor of the i-th payment data, and n is the total number of payment time nodes in the time attribute group.

[0017] Preferably, the method for setting the weight factor of the i-th payment data in the time attribute group includes:

[0018] A1. Obtain all payment time nodes of the target user in the preset historical time period, perform cluster analysis on all payment time nodes using the K-means clustering algorithm, obtain K clusters, and use the center point of each cluster as the label of the cluster. Each cluster includes several payment time nodes;

[0019] A2. For each cluster, calculate the difference between its center point and the current time, and calculate the proportion of the difference between the center points of all clusters and the current time as the time value ratio of the cluster;

[0020] A3. Obtain the payment time node of the ith payment data, obtain the cluster to which it belongs, and use the time value ratio of the cluster to which it belongs as the weight factor of the ith payment data.

[0021] Preferably, the method for determining the sensitive value of the i-th payment data in the time attribute group includes:

[0022] B1. Obtain the payment amount, payment duration, and product type from the payment data to form a payment feature vector;

[0023] B2. Input the payment feature vector into the pre-trained payment sensitivity prediction model, and output the sensitivity value corresponding to the payment feature vector;

[0024] The method for obtaining the pre-trained payment-sensitive prediction model includes:

[0025] Collect payment feature vectors of a large number of payment time nodes belonging to the time attribute group in the history of the target user, and label each payment feature vector; the label content is set as: sensitive value;

[0026] The annotated payment feature vectors are used as training sets, and the pre-selected neural network structure is trained using the training sets to optimize the model parameters and obtain the final payment sensitivity prediction model.

[0027] Preferably, labeling each payment feature vector includes:

[0028] Statistics are collected for all push content received by the target user from the payment service bank within the preset historical time period, and the hit probability value of the product type in the payment feature vector in the push content is calculated. The sensitivity value marked by the payment feature vector is calculated according to the following formula:

[0029]

[0030] Among them, S is the sensitive value marked by the payment feature vector, p is the hit probability value of the product type in the payment feature vector, T is the payment duration, α and β are pre-set weight values, which are used to represent the influence of product type and payment duration on the sensitive value respectively.

[0031] Preferably, the preset coefficient of variation sensitive assessment algorithm specifically includes:

[0032] S201, digitizing all data in each data type according to a preset coding rule to generate a numerical sequence corresponding to each data type;

[0033] S202, based on the numerical sequence of each data type, calculate its coefficient of variation according to the following formula:

[0034]

[0035] Among them, CV is the coefficient of variation of the data type, μ is the average value of all values ​​in the numerical sequence of the data type, q is the total number of values ​​in the numerical sequence, and x is the average value of the data type. i is the i-th value in the numerical sequence;

[0036] S203: Use the coefficient of variation of each data type as its second sensitive value.

[0037] Preferably, in S104, each of the time-sensitive classes includes at least one time attribute group, and each of the type-sensitive classes includes at least one data type;

[0038] The preset clustering algorithm is set to a K-means clustering algorithm, which takes the center point of each time-sensitive class as the first representative level value of the time-sensitive class, and takes the center point of each type-sensitive class as the second representative level value of the type-sensitive class.

[0039] Preferably, the preset cross-class decryption mechanism specifically includes:

[0040] C1. Based on M time-sensitive classes and their corresponding first representative level values, N type-sensitive classes and their corresponding second representative level values, a time-type cross-class matrix of size M×N is generated, and the value of each element in the matrix is ​​set to the comprehensive sensitive value of the corresponding time-type cross-class: the weighted sum of the corresponding first representative level value and the second representative level value;

[0041] C2. Obtain the payment data associated with each element in the time-type cross-class matrix. The associated payment data is set to: the intersection of the payment data in the time-sensitive class corresponding to the element and the payment data summarized in the corresponding type-sensitive class;

[0042] C3. Based on all the elements in the time-type cross-class matrix, perform a merging process on them to obtain R elements, where R ≤ M × N;

[0043] C4. Based on the R elements and their respectively associated payment data and payment ciphertexts, generate R types of decryption information, where the R elements and the R types of decryption information are in one-to-one correspondence, and each type of decryption information is used to decrypt the payment ciphertext to obtain the payment data associated with the element corresponding to this type of decryption information.

[0044] Preferably, the authorization levels between the target user and the payment service bank side include low, medium, and high. The matching of one or more types of decryption information as the target decryption information from multiple types of decryption information according to the authorization level between the target user and the payment service bank side specifically includes:

[0045] According to the pre-set authorization level-sensitivity acceptance interval matching table, obtain the sensitivity acceptance interval corresponding to the authorization level of the payment service bank side, traverse the element values corresponding to multiple types of decryption information, and determine all types of decryption information whose element values fall within the sensitivity acceptance interval as the target decryption information;

[0046] Among them, the pre-set authorization level-sensitivity acceptance interval matching table is specifically: low - [0, a), medium - [a, b), high - [b, 1], 0 < a < b < 1, and the values of a and b are set according to the actual situation and are used to reflect the boundaries of different comprehensive sensitivity values.

[0047] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0048] By introducing the time attribute group, the consumption habits and privacy preferences of users in different time backgrounds are considered, and the sensitivity degree of payment data in the time dimension is more comprehensively reflected; by calculating the first sensitivity value of the time attribute group and the second sensitivity value of the data type, and using the payment sensitivity prediction model to evaluate the sensitivity degree of payment data, dynamic and accurate sensitivity evaluation is achieved; by generating multiple types of decryption information and matching and sending the target decryption information according to the authorization level, flexible data access control is achieved, which not only protects user privacy but also meets the specific information requirements of the payment service bank side.

[0049] A cross-class decryption mechanism is introduced to generate multiple types of decryption information, among which the time-type cross-class matrix plays a core role. The matrix organizes the payment data into a structured form with time attributes and data types as dimensions. Through the matrix, the distribution of payment data in different time backgrounds and of different types can be clearly seen; according to the element distribution and sensitivity assessment results in the matrix, specific decryption information can be generated for different data types and time backgrounds, which improves the pertinence of the decryption information, enhances the flexibility of data access control, and improves the security and efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of an online payment security management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0052] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are only for illustrative purposes and do not represent the only implementation method.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0054] Embodiment 1: Figure 1 It is a flow chart of the online payment security management method according to an embodiment of the present invention.

[0055] like Figure 1 As shown, an online payment security management method includes the following steps:

[0056] S101, obtaining a payment data set generated by online payments of target users within a preset historical time period, wherein the payment data set includes payment data generated at different time nodes, and the payment data at each time node includes data corresponding to several data types, including but not limited to payment amount, payment channel, product type, payment duration and other data.

[0057] Among them, the preset historical time period can be set to half a year, and can be specifically set according to the actual situation. It can be set according to the cycle of updating it to the payment security management platform, and the present invention will not elaborate on this.

[0058] S102, based on a pre-set time attribute group (which can be pre-set to a null value), all payment data in the payment data set are divided into corresponding time attribute groups based on their generation time nodes, each time attribute group includes at least one payment data, each payment data corresponds to a payment time node, and the first sensitive value of each time attribute group is obtained based on the first sensitive indicator algorithm.

[0059] In payment data analysis, time is an important dimension. Payment behaviors at different time nodes (such as weekdays, weekends, holidays, etc.) may differ significantly. These differences reflect the consumption habits and privacy preferences of target users in different time contexts. The division of time context can be set according to the target user. For example, if the target user is an employee of a company, the time context can be set to working hours, off-get off work hours, weekends, holidays, the time window after payday (used to reflect the payment habits of the target user within a fixed time window after payday), and the time window before payday (used to reflect the payment habits of the target user within a fixed time window before payday). Payment behaviors during working hours may be related to work. Users have more leisure time after get off work and on weekends, and payment behaviors may be more inclined to entertainment and personal consumption. Payment behaviors during holidays may be affected by the festive atmosphere and promotional activities.

[0060] Therefore, the preset time attribute group is used to represent different payment time background types for target users, and based on the generation time node of each payment data, all payment data are classified according to the time attribute group to which it belongs.

[0061] In some embodiments, the first sensitive indicator algorithm is set as:

[0062]

[0063] Among them, SI is the first sensitive value of the time attribute group, SI i is the sensitive value of the i-th payment data in the time attribute group, w i is the weight factor of the ith payment data, and n is the total number of payment time nodes in the time attribute group (it can also be understood as the total number of payment data, one payment time node corresponds to one payment data).

[0064] The method for setting the weight factor of the i-th payment data in the time attribute group includes:

[0065] A1. Obtain all payment time nodes of the target user in a preset historical time period, and use the existing clustering algorithm (K-means clustering algorithm) to perform cluster analysis on all payment time nodes (clustering is performed based on the difference between two payment time nodes, and the payment-intensive time period in the preset historical time period can be divided), and obtain K clusters. The center point (central payment time node) of each cluster is used as the label of the cluster, and each cluster includes several payment time nodes;

[0066] For example, randomly select K payment time nodes as the initial cluster centers, which represent the center points of K potential payment-intensive time periods; for each payment time node, calculate its distance to the K cluster centers; assign each payment time node to the cluster represented by the nearest cluster center; once all payment time nodes are assigned to each cluster, recalculate the center of each cluster by calculating the average value of all payment time nodes in the cluster; repeat the above assignment and update steps until the cluster center no longer changes significantly or reaches the preset number of iterations, which means that the algorithm has converged and the clustering results tend to be stable;

[0067] It should be noted that the working principle of the K-means clustering algorithm is not described in detail in the present invention, and reference may be made to the relevant prior art.

[0068] A2. Based on each cluster, calculate the difference between its center point and the current time, and calculate the proportion of the difference in the sum of the differences between the center points of all clusters and the current time as the time value ratio of the cluster.

[0069] A3. Obtain the payment time node of the i-th payment data, obtain the cluster to which it belongs, and use the time value ratio of the cluster to which it belongs as the weight factor of the i-th payment data. Therefore, the payment data closer to the current time can more reflect the user's latest payment behavior habits, and the payment data has a higher reference value in time and a higher sensitivity.

[0070] The method for determining the sensitive value of the i-th payment data in the time attribute group includes:

[0071] B1. Obtain the payment amount, payment duration, and product type from the payment data to form a payment feature vector;

[0072] B2. Input the payment feature vector into the pre-trained payment sensitivity prediction model, and output the sensitivity value corresponding to the payment feature vector.

[0073] The method for obtaining the pre-trained payment-sensitive prediction model includes:

[0074] Collect payment feature vectors of a large number of payment time nodes belonging to the time attribute group in the history of the target user, label each payment feature vector, and set the label content as: sensitive value; wherein, the labeling method of the payment feature vector is: count all the push content received by the target user from the payment service bank in the preset historical time period, calculate the hit probability value of the product type in the payment feature vector in the push content (the proportion of the push content related to the product type and all the push content can be calculated), and calculate the sensitive value labeled by the payment feature vector according to the following formula:

[0075]

[0076] Among them, S is the sensitive value marked by the payment feature vector, p is the hit probability value of the product type in the payment feature vector, T is the payment duration, α and β are pre-set weight values, which are used to represent the influence of product type and payment duration on the sensitive value respectively;

[0077] The annotated payment feature vectors are used as training sets, and the pre-selected neural network structure is trained using the training sets to optimize the model parameters and obtain the final payment sensitivity prediction model.

[0078] Therefore, by introducing the time dimension and setting time attribute groups to reflect users' consumption habits and privacy preferences under different time background types, the first sensitive value of each time attribute group is obtained for subsequent analysis and classification, thereby enabling a more comprehensive assessment of the sensitivity of payment data in the time dimension.

[0079] S103, obtaining a second sensitive value for each data type in the payment data set according to a preset coefficient of variation sensitivity evaluation algorithm.

[0080] In some embodiments, the preset coefficient of variation sensitive evaluation algorithm specifically includes:

[0081] S201, digitize all data in each data type according to a preset coding rule to generate a numerical sequence corresponding to each data type.

[0082] Among them, when the data in the data type is a numerical value, there is no need to digitize it, and a numerical sequence can be directly generated; when the data in the data type is not a numerical value, all data in the data type are digitized using a preset encoding rule.

[0083] For example, when the data type is payment amount, the amount value is used directly; when the data type is payment channel, the payment channel is converted into the numerical representation of the categorical variable, for example, bank card is 1, Alipay is 2, and WeChat Pay is 3; when the data type is product type, the product type is converted into the numerical representation of the categorical variable, for example, goods is 1, services is 2, and subscriptions is 3.

[0084] S202, based on the numerical sequence of each data type, calculate its coefficient of variation according to the following formula:

[0085]

[0086] Among them, CV is the coefficient of variation of the data type, μ is the average value of all values ​​in the numerical sequence of the data type, q is the total number of values ​​in the numerical sequence, and x is the average value of the data type. i is the i-th value in the numerical sequence.

[0087] S203, taking the coefficient of variation of each data type as its second sensitive value. The larger the coefficient of variation, the higher the discreteness of the data type and the higher the sensitivity.

[0088] S104: Based on the first sensitive values ​​of all time attribute groups and the second sensitive values ​​of all data types, a preset clustering algorithm is used to analyze and classify all time attribute groups and all data types, and M time-sensitive classes and N type-sensitive classes are output respectively. Each time-sensitive class includes at least one time attribute group, and each type-sensitive class includes at least one data type.

[0089] Specifically, the preset clustering algorithm is set to the K-means clustering algorithm, which takes the center point of each time-sensitive class (center first sensitive value) as the first representative level value of the time-sensitive class, and takes the center point of each type-sensitive class (center second sensitive value) as the second representative level value of the type-sensitive class.

[0090] For example, for all time attribute groups, randomly select M first sensitive values ​​as the initial cluster centers of the time sensitive class; for all data types, randomly select N second sensitive values ​​as the initial cluster centers of the type sensitive class; for the time attribute group, calculate the distance from the first sensitive value of each time attribute group to the M time sensitive class cluster centers, and assign it to the cluster with the closest distance; for the data type, calculate the distance from the second sensitive value of each data type to the N type sensitive class cluster centers, and assign it to the cluster with the closest distance; for each time sensitive class, recalculate the mean of the first sensitive values ​​of all time attribute groups it contains as the new cluster center; for each type sensitive class, recalculate the mean of the second sensitive values ​​of all data types it contains as the new cluster center; repeat the above allocation and update steps until the cluster center no longer changes significantly or reaches the preset number of iterations. The specific cluster analysis process can refer to the relevant prior art, and the present invention will not elaborate on this.

[0091] S105, upload the payment data set of the target user to the security management platform to generate encrypted information, use the encrypted information to encrypt to obtain the payment ciphertext, and use the preset cross-level decryption mechanism to generate multiple types of decryption information. After each type of decryption information decrypts the payment ciphertext, payment data corresponding to at least one data type is obtained.

[0092] In some embodiments, the preset cross-class decryption mechanism specifically includes:

[0093] C1. Based on M time-sensitive classes and their corresponding first representative level values, N type-sensitive classes and their corresponding second representative level values, a time-type cross-class matrix of size M×N is generated, and the value of each element in the matrix (a total of M×N elements) is set to the comprehensive sensitivity value of the corresponding time-type cross-class (the weighted sum of the corresponding first representative level value and the second representative level value);

[0094] C2. Obtain the payment data associated with each element in the time-type cross-class matrix. The associated payment data is set to: the intersection of the payment data in the time-sensitive class corresponding to the element and the payment data summarized in the corresponding type-sensitive class;

[0095] C3. Based on all the elements in the time-type cross-class matrix, merge them (merge elements with the same element value and merge their associated payment data) to obtain R elements, R≤M×N;

[0096] C4. Generate R types of decryption information based on the R elements and their respectively associated payment data and payment ciphertexts, where the R elements correspond one-to-one to the R types of decryption information, and each type of decryption information is used to decrypt the payment ciphertext to obtain the payment data associated with the element corresponding to the type of decryption information.

[0097] S106, obtain the payment service bank end that has established a binding relationship with the target user, and according to the authorization level of the target user and the payment service bank end, match one or more types of decryption information among multiple types of decryption information as target decryption information and send them to the corresponding payment service bank end.

[0098] Among them, the payment service bank end with which the target user establishes a binding relationship establishes communication with the security management platform. The security management platform is a system for transaction data security management, and is embedded with a data encryption module. The data encryption module is used to generate encrypted information according to the characteristics of the target user, and stores data encryption algorithms, such as: AES algorithm, DSA algorithm, SHA2 algorithm, etc., and technicians in this field can select an adaptive encryption algorithm according to actual needs. Multiple types of decryption information are configured according to the encrypted information of the target user, and each type of decryption information is used to decrypt the payment ciphertext, and the payment data after decryption of each type of decryption information is different.

[0099] In some embodiments, the authorization levels between the target user and the payment service bank end include low, medium, and high, which can be determined according to a pre-signed authorization agreement. According to the authorization level between the target user and the payment service bank end, one or more types of decryption information are matched as the target decryption information from multiple types of decryption information, specifically including:

[0100] According to a pre-set authorization level - sensitive acceptance interval matching table, obtain the sensitive acceptance interval corresponding to the authorization level of the payment service bank end, traverse the element values corresponding to multiple types of decryption information, and determine all types of decryption information whose element values fall within the sensitive acceptance interval as the target decryption information.

[0101] Among them, the pre-set authorization level - sensitive acceptance interval matching table is specifically:

[0102] Low - [0, a), Medium - [a, b), Medium - [b, 1], where 0 < a < b < 1, and the values of a and b are set according to the actual situation, which are used to reflect the boundaries of different comprehensive sensitivity values.

[0103] Thus, according to the authorization level of the target user, the target decryption information is matched from multiple types of decryption information and sent to the payment service bank end that meets the authorization level, ensuring that only authorized entities can access specific-level payment data. The payment service bank end receives the target decryption information that matches its authorization level, realizing secure payment data sharing, protecting user privacy, and at the same time meeting the specific information needs of the payment service bank end.

[0104] The technical solutions in the embodiments of the present application described above have at least the following technical effects or advantages:

[0105] By introducing the time attribute group, the consumption habits and privacy preferences of users in different time backgrounds are considered, and the sensitivity degree of payment data in the time dimension is more comprehensively reflected; by calculating the first sensitivity value of the time attribute group and the second sensitivity value of the data type, and using the payment sensitivity prediction model to evaluate the sensitivity degree of payment data, dynamic and accurate sensitivity evaluation is achieved; by generating multiple types of decryption information and matching and sending the target decryption information according to the authorization level, flexible data access control is realized, which not only protects user privacy but also meets the specific information needs of the payment service bank end.

[0106] A cross-class decryption mechanism is introduced to generate multiple types of decryption information, among which the time-type cross-class matrix plays a core role. The matrix organizes the payment data into a structured form with time attributes and data types as dimensions. Through the matrix, the distribution of payment data in different time backgrounds (such as working hours, off-get off work hours, weekends, etc.) and different types (such as payment amount, payment duration, product type, etc.) can be clearly seen; according to the element distribution in the matrix and the sensitivity assessment results, specific decryption information can be generated for different data types and time backgrounds, which improves the pertinence of decryption information, enhances the flexibility of data access control, and improves the security and efficiency of the system.

[0107] Embodiment 2: In traditional methods, the authorization levels of the target user and the payment service bank are fixed or determined in a pre-defined protocol. However, due to the complexity and variability of the online payment environment and the dynamic changes in the payment habits of the target users, their service experience with different payment service banks is also constantly changing. Therefore, the traditional fixed authorization level marking method has certain limitations.

[0108] Therefore, the embodiments of the present application are optimized to a certain extent based on the above embodiments.

[0109] In some embodiments, the method further comprises:

[0110] S107, according to the preset time interval, periodically execute steps S101 to S106 to realize periodic sharing of payment data for the payment service bank side bound to the target user, so that the bank side can send promotional content in a targeted manner based on the payment data of the target user, so that the bank side can send promotional content in a targeted manner based on the latest payment data.

[0111] In some embodiments, in step S106, the method for determining the authorization level of the target user and the payment service bank further includes:

[0112] S301, when entering the next cycle, obtain all payment data shared to the payment service bank in the previous cycle, extract all product types and their corresponding payment amounts in the payment data, and form a product feature vector for each product type and corresponding payment amount.

[0113] S302, collecting all product feature vectors to obtain a product feature vector set.

[0114] S303, counting all product types and their corresponding value amounts that the target user interacted with the push content of the payment service bank in the previous cycle, and generating a set of valid product feature vectors (each valid product feature vector is generated by a product type with which there is an interactive behavior and its corresponding value amount).

[0115] Among them, interactive behaviors can be set to click, collect, place an order, etc.

[0116] S304, based on the product feature vector set, screen out the valid product feature vector set (if the valid product feature vector does not exist in the product feature vector set, screen out), and obtain a new valid product feature vector set.

[0117] S305, obtain the pushed content of the payment service bank in the previous cycle, calculate the hit probability value of the new valid product feature vector set in the pushed content, and redefine its authorization level based on the hit probability value. The larger the hit probability value, the less invalid content the payment service bank pushes to the target user, and the higher its authorization level.

[0118] Specifically, when the hit probability value is greater than the upper limit of the preset hit probability threshold interval, the authorization level is set to high; when the hit probability value is less than the lower limit of the preset hit probability threshold interval, the authorization level is set to low; otherwise, the authorization level is set to medium. The hit probability threshold interval is set according to actual conditions and expert experience.

[0119] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0120] By periodically redefining the authorization level, the authorization level can be dynamically adjusted as the payment habits of target users and the content pushed by the payment service bank change, which improves the flexibility and adaptability of the authorization level and makes the authorization level more in line with the actual situation;

[0121] By redefining the authorization level based on the target user's payment data in the previous cycle and their interaction with the pushed content, the payment service bank can more accurately understand the preferences and needs of the target user, which helps the bank to send more targeted promotional content, improve promotion effects and user satisfaction;

[0122] For target users with higher authorization levels, the bank can invest more resources in precision marketing; for users with lower authorization levels, it can reduce resource investment to avoid waste; in addition, the bank's authorization level can be dynamically adjusted based on historical interaction data between the target user and the bank, which can further improve the security of user payment data management and share payment data of a specific level with the bank, avoiding the leakage of sensitive data and creating security risks for target users' payments.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online payment security management method, characterized in that: include: S101, obtaining a payment data set generated by online payments of a target user within a preset historical time period, the payment data set including payment data generated at different time nodes, the payment data at each time node including data corresponding to several data types, the data types including payment amount, product type, and payment duration; S102, based on the preset time attribute groups, all payment data in the payment data set are divided into corresponding time attribute groups based on their generation time nodes, each time attribute group includes at least one payment data, each payment data corresponds to a payment time node, and a first sensitive value of each time attribute group is obtained based on a first sensitive indicator algorithm; S103, obtaining a second sensitive value for each data type in the payment data set according to a preset coefficient of variation sensitivity evaluation algorithm; S104, based on the first sensitive values ​​of all time attribute groups and the second sensitive values ​​of all data types, using a preset clustering algorithm to analyze and classify all time attribute groups and all data types, and output M time-sensitive classes and N type-sensitive classes respectively; S105, uploading the payment data set of the target user to the security management platform to generate encryption information, using the encryption information to encrypt to obtain payment ciphertext, and using a preset cross-class decryption mechanism to generate multiple types of decryption information, each type of decryption information decrypts the payment ciphertext to obtain payment data corresponding to at least one data type; S106, obtain the payment service bank end that has established a binding relationship with the target user, and according to the authorization level of the target user and the payment service bank end, match one or more types of decryption information among multiple types of decryption information as target decryption information and send them to the corresponding payment service bank end.

2. The online payment security management method according to claim 1, characterized in that: The preset time attribute group is used to represent different payment time background types for target users. Based on the generation time node of each payment data, all payment data are classified according to the time attribute group to which it belongs.

3. The online payment security management method according to claim 1, characterized in that: The first sensitive indicator algorithm is set as: Among them, SI is the first sensitive value of the time attribute group, SI i is the sensitive value of the i-th payment data in the time attribute group, w i is the weight factor of the i-th payment data, and n is the total number of payment time nodes in the time attribute group.

4. The online payment security management method according to claim 3, characterized in that: The method for setting the weight factor of the i-th payment data in the time attribute group includes: A1. Obtain all payment time nodes of the target user in the preset historical time period, perform cluster analysis on all payment time nodes using the K-means clustering algorithm, obtain K clusters, and use the center point of each cluster as the label of the cluster. Each cluster includes several payment time nodes; A2. For each cluster, calculate the difference between its center point and the current time, and calculate the proportion of the difference between the center points of all clusters and the current time as the time value ratio of the cluster; A3. Obtain the payment time node of the ith payment data, obtain the cluster to which it belongs, and use the time value ratio of the cluster to which it belongs as the weight factor of the ith payment data.

5. The online payment security management method according to claim 3, characterized in that: The method for determining the sensitive value of the i-th payment data in the time attribute group includes: B1. Obtain the payment amount, payment duration, and product type from the payment data to form a payment feature vector; B2. Input the payment feature vector into the pre-trained payment sensitivity prediction model, and output the sensitivity value corresponding to the payment feature vector; The method for obtaining the pre-trained payment-sensitive prediction model includes: Collect payment feature vectors of a large number of payment time nodes belonging to the time attribute group in the history of the target user, and label each payment feature vector; the label content is set as: sensitive value; The annotated payment feature vectors are used as training sets, and the pre-selected neural network structure is trained using the training sets to optimize the model parameters and obtain the final payment sensitivity prediction model.

6. The online payment security management method according to claim 5, characterized in that: The labeling of each payment feature vector includes: Statistics are collected for all push content received by the target user from the payment service bank within the preset historical time period, and the hit probability value of the product type in the payment feature vector in the push content is calculated. The sensitivity value marked by the payment feature vector is calculated according to the following formula: Among them, S is the sensitive value marked by the payment feature vector, p is the hit probability value of the product type in the payment feature vector, T is the payment duration, α and β are pre-set weight values, which are used to represent the influence of product type and payment duration on the sensitive value respectively.

7. The online payment security management method according to claim 1, characterized in that: The preset coefficient of variation sensitivity assessment algorithm specifically includes: S201, digitizing all data in each data type according to a preset coding rule to generate a numerical sequence corresponding to each data type; S202, based on the numerical sequence of each data type, calculate its coefficient of variation according to the following formula: Among them, CV is the coefficient of variation of the data type, μ is the average value of all values ​​in the numerical sequence of the data type, q is the total number of values ​​in the numerical sequence, and x is the average value of the data type. i is the i-th value in the numerical sequence; S203: Use the coefficient of variation of each data type as its second sensitive value.

8. The online payment security management method according to claim 1, characterized in that: In S104, each of the time-sensitive classes includes at least one time attribute group, and each of the type-sensitive classes includes at least one data type; the preset clustering algorithm is set to a K-means clustering algorithm, and the center point of each time-sensitive class is used as the first representative level value of the time-sensitive class, and the center point of each type-sensitive class is used as the second representative level value of the type-sensitive class.

9. The online payment security management method according to claim 8, characterized in that: The preset cross-level decryption mechanism specifically includes: C1. Based on M time-sensitive classes and their corresponding first representative level values, N type-sensitive classes and their corresponding second representative level values, a time-type cross-class matrix of size M×N is generated, and the value of each element in the matrix is ​​set to the comprehensive sensitive value of the corresponding time-type cross-class: the weighted sum of the corresponding first representative level value and the second representative level value; C2. Obtain the payment data associated with each element in the time-type cross-class matrix. The associated payment data is set to: the intersection of the payment data in the time-sensitive class corresponding to the element and the payment data summarized in the corresponding type-sensitive class; C3, based on all the elements of the time-type cross-class matrix, merge them to obtain R elements, R ≤ M × N; C4. Generate R types of decryption information based on the R elements and their respectively associated payment data and payment ciphertexts, where the R elements correspond one-to-one to the R types of decryption information, and each type of decryption information is used to decrypt the payment ciphertext to obtain the payment data associated with the element corresponding to the type of decryption information.

10. The online payment security management method according to claim 9, characterized in that: The authorization levels between the target user and the payment service bank side include low, medium, and high. According to the authorization levels between the target user and the payment service bank side, one or more types of decryption information are matched as the target decryption information from multiple types of decryption information, specifically including: According to the pre-set authorization level-sensitive acceptance interval matching table, obtain the sensitive acceptance interval corresponding to the authorization level of the payment service bank side, traverse the element values corresponding to multiple types of decryption information respectively, and determine all types of decryption information whose element values fall within the sensitive acceptance interval as the target decryption information; Among them, the pre-set authorization level-sensitive acceptance interval matching table is specifically: low - [0, a), medium - [a, b), high - [b, 1], where 0 < a < b < 1, and the values of a and b are set according to the actual situation, and are used to reflect the boundaries of different comprehensive sensitivity values.

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