A data delivery method and device

Abstract

The embodiment of the present application discloses a data delivery method and device, wherein the method includes: when a data access request initiated by a target user is received, verifying the identity of the target user; when the identity of the target user is verified to be a legal identity, determining the data access level value and the data access column secret value range of the target user; obtaining structured data corresponding to the data access request; determining the sensitivity level corresponding to each data column in the structured data through a sensitivity level classification model; selecting sensitive data columns from the structured data, and encrypting the sensitive data columns to obtain the column secret values ​​corresponding to the sensitive data columns; configuring the minimum column secret value for other data columns in the structured data except the sensitive data columns; and determining the target data columns accessible to the target user in the structured data according to the data access level value and the data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data delivery method and device. Background Art

[0002] Today's society is an information age. Data is not only the core asset of an enterprise, but ensuring data security is the foundation of an enterprise. Modern enterprise information applications involve a large amount of structured data, which contains a large amount of personal information and various sensitive information derived from personal information. If access rights to such information are not controlled, sensitive information is likely to be mistakenly accessed, leading to problems such as sensitive information leakage. Summary of the invention

[0003] The embodiments of the present application provide a data delivery method and device, which can effectively control user access to sensitive information and prevent problems such as leakage of sensitive information.

[0004] In view of this, the first aspect of the present application provides a data delivery method, the method comprising:

[0005] When receiving a data access request initiated by a target user, verifying the identity of the target user; and when the target user's identity is determined to be legitimate, determining the data access level value and data access column secret value range of the target user;

[0006] Obtaining structured data corresponding to the data access request;

[0007] Determine, based on the structured data, the sensitivity level corresponding to each data column in the structured data using a sensitivity level classification model;

[0008] According to the sensitivity level corresponding to each data column in the structured data, a sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain a column encryption value corresponding to the sensitive data column; a minimum column encryption value is configured for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column;

[0009] The target data columns accessible to the target user in the structured data are determined according to the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data.

[0010] Optionally, selecting sensitive data columns from the structured data according to the sensitivity levels corresponding to the data columns in the structured data includes:

[0011] According to the sensitivity levels corresponding to the data columns in the structured data, determining the data columns whose corresponding sensitivity levels meet the preset sensitivity conditions as reference data columns, and determining the other data columns in the structured data except the reference data columns as comparison data columns;

[0012] For each of the comparison data columns, calculating the correlation degree between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column;

[0013] The comparison data column and the reference data column whose corresponding association degree meets the preset association condition are determined as the sensitive data column.

[0014] Optionally, before calculating the degree of association between each comparison data column and the reference data column, the method further includes:

[0015] A dimensionless process is performed on each data column in the structured data.

[0016] Optionally, the calculating, for each of the comparison data columns, a degree of association between the comparison data column and the reference data column as the degree of association corresponding to the comparison data column includes:

[0017] For each of the comparison data columns, a grey correlation analysis algorithm is used to calculate a grey correlation coefficient between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column.

[0018] Optionally, determining the sensitivity level corresponding to each data column in the structured data according to the structured data by using a sensitivity level classification model includes:

[0019] The sensitivity level corresponding to each data column in the structured data is determined according to the structured data by using a gradient boosted decision tree (GBDT) model.

[0020] Optionally, determining the target data column accessible to the target user in the structured data according to the data access level value and the data access column secret value range of the target user and the sensitivity level and column secret value corresponding to each column of data in the structured data includes:

[0021] For each data column in the structured data, determine a data column whose corresponding sensitivity level is less than or equal to the data access level value of the target user and whose corresponding column encryption value is within the data access column encryption value range of the target user as the target data column.

[0022] A second aspect of the present application provides a data delivery device, the device comprising:

[0023] The identity authentication module is used to verify the identity of the target user when receiving a data access request initiated by the target user; and when the identity of the target user is verified to be a legal identity, determine the data access level value and data access column secret value range of the target user;

[0024] A data acquisition module, used to acquire structured data corresponding to the data access request;

[0025] A level determination module, configured to determine the sensitivity level corresponding to each data column in the structured data according to the structured data through a sensitivity level classification model;

[0026] A column encryption value configuration module is used to select a sensitive data column from the structured data according to the sensitivity level corresponding to each data column in the structured data, and encrypt the sensitive data column to obtain a column encryption value corresponding to the sensitive data column; configure a minimum column encryption value for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column;

[0027] The accessible data determination module is used to determine the target data columns accessible to the target user in the structured data based on the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data.

[0028] Optionally, the column secret value configuration module is specifically used to:

[0029] According to the sensitivity levels corresponding to the data columns in the structured data, determining the data columns whose corresponding sensitivity levels meet the preset sensitivity conditions as reference data columns, and determining the other data columns in the structured data except the reference data columns as comparison data columns;

[0030] For each of the comparison data columns, calculating the correlation degree between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column;

[0031] The comparison data column and the reference data column whose corresponding association degree meets the preset association condition are determined as the sensitive data column.

[0032] Optionally, the column secret value configuration module is further used to:

[0033] Before calculating the correlation between each comparison data column and the reference data column, dimensionless processing is performed on each data column in the structured data.

[0034] Optionally, the column secret value configuration module is specifically used to:

[0035] For each of the comparison data columns, a grey correlation analysis algorithm is used to calculate a grey correlation coefficient between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column.

[0036] Optionally, the level determination module is specifically used to:

[0037] The sensitivity level corresponding to each data column in the structured data is determined according to the structured data by using a gradient boosted decision tree (GBDT) model.

[0038] Optionally, the accessible data determination module is specifically used to:

[0039] For each data column in the structured data, determine a data column whose corresponding sensitivity level is less than or equal to the data access level value of the target user and whose corresponding column encryption value is within the data access column encryption value range of the target user as the target data column.

[0040] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0041] An embodiment of the present application provides a data delivery method, which includes: when a data access request initiated by a target user is received, the identity of the target user is verified; and when the identity of the target user is determined to be a legal identity through verification, the data access level value and the data access column secret value range of the target user are determined; then, the structured data corresponding to the data access request is obtained; and, through a sensitivity level classification model, the sensitivity level corresponding to each data column in the structured data is determined according to the structured data; then, according to the sensitivity level corresponding to each data column in the structured data, the sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain the column secret value corresponding to the sensitive data column; and, the other data columns in the structured data except the sensitive data column are configured with a minimum column secret value as the column secret value corresponding to the data column; finally, according to the data access level value and the data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data, the target data column accessible to the target user in the structured data is determined. The above method determines the corresponding sensitivity level for each column of structured data that the user wants to access, and determines the corresponding column secret value for each column of data in the structured data. Users can access the corresponding column data only if they meet the corresponding sensitivity level and column secret value. In this way, it can effectively prevent low-authority users from making erroneous access operations to highly sensitive data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a data delivery method provided in an embodiment of the present application;

[0043] Figure 2 A working principle diagram of a data delivery system provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of a data delivery device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] In order to improve the security of sensitive data and prevent users without access rights from mistakenly accessing sensitive data, an embodiment of the present application provides a data delivery method. In the data delivery method, when a data access request initiated by a target user is received, the identity of the target user is verified; and when the identity of the target user is verified to be a legal identity, the data access level value and the data access column secret value range of the target user are determined; then, the structured data corresponding to the data access request is obtained; and, through the sensitivity level classification model, according to the structured data, the sensitivity level corresponding to each data column in the structured data is determined; then, according to the sensitivity level corresponding to each data column in the structured data, the sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain the column secret value corresponding to the sensitive data column; and the lowest column secret value is configured for other data columns in the structured data except the sensitive data column as the column secret value corresponding to the data column; finally, according to the data access level value and the data access column secret value range of the target user, the sensitivity level and column secret value corresponding to each column of data in the structured data, the target data column accessible to the target user in the structured data is determined. The above method determines the corresponding sensitivity level for each column of structured data that the user wants to access, and determines the corresponding column secret value for each column of data in the structured data. Users can access the corresponding column data only if they meet the corresponding sensitivity level and column secret value. In this way, it can effectively prevent low-authority users from making erroneous access operations to highly sensitive data.

[0048] The data delivery method provided by this application is introduced in detail below through a method embodiment.

[0049] See also Figure 1 , Figure 1 The following is a flow chart of the data delivery method provided in the embodiment of the present application. For the convenience of description, the following embodiment is introduced by taking the execution subject of the method as a server as an example. Figure 1 As shown, the data delivery method includes:

[0050] Step 101: When a data access request initiated by a target user is received, the identity of the target user is verified; and when the identity of the target user is determined to be legal, the data access level value and data access column confidentiality value range of the target user are determined.

[0051] After receiving the data access request initiated by the target user through the data delivery system, the server can verify the identity of the target user; for example, verify whether the target user is a legitimate user who can initiate the data access request.

[0052] If the target user's identity is verified to be a legitimate identity, the target user's data access level value and data access column secret value range can be further determined. It should be understood that the user's data access level value and data access column secret value range can be assigned to the user based on the user's identity information when the user registers on the data delivery system. Different users are configured with their corresponding data access level values ​​and data access column secret value ranges; Table 1 shows the data access level values ​​and data access column secret value ranges corresponding to the users stored in the database. In the embodiment of the present application, the higher the corresponding data access level value and the wider the column secret value range, the more data the user can access, and the user can access more sensitive data.

[0053] User ID Level Value Column secret value range User1 5 1-100 User2 4 1-80 …… …… …… UserN 1 1-20

[0054] Table 1

[0055] Step 102: Obtain structured data corresponding to the data access request.

[0056] After verifying that the target user's identity is legitimate, the server can further obtain the structured data corresponding to the data access request initiated by the target user. That is, the data access request initiated by the target user can carry the index information of the data to be accessed. Accordingly, after verifying that the target user's identity is legitimate, the server can further retrieve the structured data corresponding to the data access request in the database according to the index information carried in the data access request initiated by the target user.

[0057] Structured data can also be called row data. It is mainly stored and managed through relational databases. It is a two-dimensional (row, column) table structure, and each field has its own data format and length specification.

[0058] Step 103: Determine the sensitivity level corresponding to each data column in the structured data according to the structured data through a sensitivity level classification model.

[0059] After the server obtains the structured data requested by the target user, it can input the structured data into a pre-trained sensitivity level classification model. After analyzing and processing the input structured data, the sensitivity level classification model will output the sensitivity level corresponding to each data column in the structured data.

[0060] In a possible implementation, the server may use a machine learning classification model as the above-mentioned sensitivity level classification model, and the machine learning classification model may specifically be a Gradient Boosting Decision Tree (GBDT) model. That is, the server may determine the sensitivity level corresponding to each data column in the structured data according to the structured data through the GBDT model.

[0061] It should be noted that the GBDT model is a decision tree model trained using the Gradient Boosting strategy. The result of the model is a set of regression classification trees: T1, T2, …, Ti, …, Tn-1, Tn. The core idea is that through multiple rounds of iterations, each tree Ti learns the residual of the previous tree Ti-1, and finally makes the residual continuously decrease in the direction of its gradient, thereby obtaining the optimal solution.

[0062] Step 104: According to the sensitivity level corresponding to each data column in the structured data, a sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain a column encryption value corresponding to the sensitive data column; a minimum column encryption value is configured for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column.

[0063] After the server determines the sensitivity level corresponding to each data column in the structured data through the above sensitivity level classification model, it can select the data column containing relatively sensitive data (i.e., sensitive data column) from the structured data, and obtain the column encryption value corresponding to the sensitive data column by encrypting the sensitive data column. For other data columns in the structured data except the sensitive data column, the server can determine by default that the data contained therein is not sensitive, and therefore can configure the lowest column encryption value for these data columns as the column encryption value corresponding to the data column.

[0064] In one possible implementation, the server may select sensitive data columns from the structured data according to the sensitivity levels corresponding to the data columns in the structured data in the following manner: according to the sensitivity levels corresponding to the data columns in the structured data, determine the data whose corresponding sensitivity levels satisfy a preset sensitivity condition as a reference data column, and determine the other data columns in the structured data except the reference data column as comparison data columns; then, calculate the correlation between each comparison data column and the reference data column as the correlation corresponding to the comparison data column; and further, determine the comparison data columns whose corresponding correlations satisfy the preset correlation conditions and the reference data columns as sensitive data columns.

[0065] Optionally, when the units corresponding to the data columns included in the structured data are different, the server may also perform dimensionless processing on the data columns in the structured data. Specifically, the server may select a data column from the structured data as a reference data column, and then, for each data column in the structured data except the reference data column, perform dimensionless conversion processing on the data in the data column according to the conversion method between the units corresponding to the data column and the units corresponding to the reference data column, so that the data in the data column is converted into data that can be measured using the units corresponding to the reference data column.

[0066] In order to facilitate understanding of the implementation method of selecting sensitive data columns, the following is an example of using the gray correlation analysis algorithm to calculate the correlation corresponding to the comparison data column to introduce the above implementation method. Specifically, after the server determines the sensitivity level corresponding to each data column in the structured data through the sensitivity level classification model, it can select several data columns with higher sensitivity levels from the structured data as reference data columns, or it can also select data columns with sensitivity levels higher than the preset sensitivity level threshold from the structured data as reference data columns. The reference data columns selected here are sensitive data columns; accordingly, the server can use other data columns in the structured data except the above reference data columns as comparison data columns. Then, the server can perform dimensionless processing on each data column in the structured data. After completing the dimensionless processing of each data column, the server can use the gray correlation analysis algorithm to calculate the gray correlation coefficient between the comparison data column and the reference data column for each comparison data column as the correlation corresponding to the comparison data column. Furthermore, the server may arrange the corresponding correlation degrees of each comparison data column in order from large to small, and determine several comparison data columns with the highest order as sensitive data columns; or, the server may also determine comparison data columns whose corresponding correlation degrees are higher than a preset correlation degree threshold as sensitive data columns.

[0067] It should be understood that when the server selects multiple reference data columns from the structured data, the server can calculate the correlation between each comparison data column and each reference data column, and then add the correlation between the comparison data column and each reference data column to obtain the sensitivity corresponding to the comparison data column.

[0068] After the server determines the sensitive data column in the structured data in the above manner, it can use the md5 encryption algorithm to encrypt the sensitive data column, thereby obtaining the column encryption value corresponding to the sensitive data column. For other data columns in the structured data except the sensitive data column, the server can directly configure the lowest column encryption value (such as 1) for them so that users can directly access the data column.

[0069] Step 105: Determine the target data columns accessible to the target user in the structured data according to the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data.

[0070] After the server determines the data access level value and data access column secret value range corresponding to the target user, and determines the sensitivity level and column secret value corresponding to each data column in the structured data that the target user wants to access, the server can determine the target data columns in the structured data that the target user can access based on the data access level value and data access column secret value range corresponding to the target user, and the sensitivity level and column secret value corresponding to each data column in the structured data.

[0071] Specifically, the server can determine, for each data column in the structured data, a data column whose corresponding sensitivity level is less than or equal to the data access level value of the target user and whose corresponding column secret value is within the data access column secret value range of the target user as the target data column. For example, assuming that the data access level value corresponding to the target user is 4 and the data access column secret value range is 1-80, the server can determine a data column whose corresponding sensitivity level in the structured data is less than or equal to 4 and whose corresponding column secret value is between 1-80 as the data column accessible to the target user.

[0072] An embodiment of the present application provides a data delivery method, which includes: when a data access request initiated by a target user is received, the identity of the target user is verified; and when the identity of the target user is determined to be a legal identity through verification, the data access level value and the data access column secret value range of the target user are determined; then, the structured data corresponding to the data access request is obtained; and, through a sensitivity level classification model, the sensitivity level corresponding to each data column in the structured data is determined according to the structured data; then, according to the sensitivity level corresponding to each data column in the structured data, the sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain the column secret value corresponding to the sensitive data column; and, the other data columns in the structured data except the sensitive data column are configured with a minimum column secret value as the column secret value corresponding to the data column; finally, according to the data access level value and the data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data, the target data column accessible to the target user in the structured data is determined. The above method determines the corresponding sensitivity level for each column of structured data that the user wants to access, and determines the corresponding column secret value for each column of data in the structured data. Users can access the corresponding column data only if they meet the corresponding sensitivity level and column secret value. In this way, it can effectively prevent low-authority users from making erroneous access operations to highly sensitive data.

[0073] The present application also provides a data delivery system. The working principle diagram of the data delivery system is as follows: Figure 2 As shown. In practical applications, the data delivery system is used to implement the above data delivery method. After receiving the data access request sent by the user, the data delivery system first verifies the legitimacy of the user and determines the data access level value and data access column secret value range corresponding to the user. Then, the structured data preparation processing is performed; that is, the structured data requested by the user is obtained from the database, and the structured data is delivered to the data delivery system. Then, the structured data is classified and graded; that is, the sensitivity level corresponding to each data column in the structured data is determined through the sensitivity level classification model; then, according to the sensitivity level corresponding to each data column in the structured data, the sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain its corresponding column secret value, and the data column other than the sensitive data column in the structured data is configured with the minimum column secret value. Finally, according to the data access level value and data access column secret value range corresponding to the user, and the sensitivity level and column secret value corresponding to each data column in the structured data, the target data column accessible to the user in the structured data is determined, and the data in the target data column is encrypted and delivered to the user.

[0074] The present application also provides a data delivery device, see Figure 3 , Figure 3 This is a schematic diagram of the structure of the data delivery device provided in the embodiment of the present application. Figure 3 As shown, the data delivery device includes:

[0075] The identity authentication module 301 is used to verify the identity of the target user when receiving a data access request initiated by the target user; and when the identity of the target user is determined to be a legitimate identity, determine the data access level value and data access column secret value range of the target user;

[0076] A data acquisition module 302 is used to acquire structured data corresponding to the data access request;

[0077] A level determination module 303 is used to determine the sensitivity level corresponding to each data column in the structured data according to the structured data through a sensitivity level classification model;

[0078] The column encryption value configuration module 304 is used to select a sensitive data column from the structured data according to the sensitivity level corresponding to each data column in the structured data, and encrypt the sensitive data column to obtain a column encryption value corresponding to the sensitive data column; configure a minimum column encryption value for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column;

[0079] The accessible data determination module 305 is used to determine the target data columns accessible to the target user in the structured data according to the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data.

[0080] Optionally, the column secret value configuration module 304 is specifically used to:

[0081] According to the sensitivity levels corresponding to the data columns in the structured data, determining the data columns whose corresponding sensitivity levels meet the preset sensitivity conditions as reference data columns, and determining the other data columns in the structured data except the reference data columns as comparison data columns;

[0082] For each of the comparison data columns, calculating the correlation degree between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column;

[0083] The comparison data column and the reference data column whose corresponding association degree meets the preset association condition are determined as the sensitive data column.

[0084] Optionally, the column secret value configuration module 304 is further used to:

[0085] Before calculating the correlation between each comparison data column and the reference data column, dimensionless processing is performed on each data column in the structured data.

[0086] Optionally, the column secret value configuration module 304 is specifically used to:

[0087] For each of the comparison data columns, a grey correlation analysis algorithm is used to calculate a grey correlation coefficient between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column.

[0088] Optionally, the level determination module 303 is specifically used to:

[0089] The sensitivity level corresponding to each data column in the structured data is determined according to the structured data by using a gradient boosted decision tree (GBDT) model.

[0090] Optionally, the accessible data determination module 305 is specifically used for:

[0091] For each data column in the structured data, determine a data column whose corresponding sensitivity level is less than or equal to the data access level value of the target user and whose corresponding column encryption value is within the data access column encryption value range of the target user as the target data column.

[0092] The above-mentioned data delivery device determines the corresponding sensitivity level for each column of structured data that the user wants to access, and determines the corresponding column secret value for each column of data in the structured data. The user can access the corresponding column of data only if the user meets the corresponding sensitivity level and column secret value. In this way, it can effectively prevent low-authority users from making erroneous access operations to highly sensitive data.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store computer programs.

[0098] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data delivery method, characterized in that: The method comprises: When a data access request initiated by a target user is received, the identity of the target user is verified; and when the identity of the target user is determined to be a legitimate identity, the data access level value and the data access column secret value range of the target user are determined; the data access level value and the data access column secret value range are assigned to the target user according to the identity information of the target user; Obtaining structured data corresponding to the data access request; Determine, based on the structured data, the sensitivity level corresponding to each data column in the structured data using a sensitivity level classification model; According to the sensitivity level corresponding to each data column in the structured data, a sensitive data column is selected from the structured data, and the sensitive data column is encrypted to obtain a column encryption value corresponding to the sensitive data column; a minimum column encryption value is configured for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column; Determine the target data column accessible to the target user in the structured data according to the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data; The selecting a sensitive data column from the structured data according to the sensitivity level corresponding to each data column in the structured data includes: According to the sensitivity levels corresponding to the data columns in the structured data, determining the data columns whose corresponding sensitivity levels meet the preset sensitivity conditions as reference data columns, and determining the other data columns in the structured data except the reference data columns as comparison data columns; For each of the comparison data columns, calculating the correlation degree between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column; The comparison data column and the reference data column whose corresponding association degree meets the preset association condition are determined as the sensitive data column.

2. The method according to claim 1, characterized in that Before calculating the correlation between each comparison data column and the reference data column, the method further includes: A dimensionless process is performed on each data column in the structured data.

3. The method according to claim 1, characterized in that The step of calculating the degree of association between each comparison data column and the reference data column as the degree of association corresponding to the comparison data column includes: For each of the comparison data columns, a grey correlation analysis algorithm is used to calculate a grey correlation coefficient between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column.

4. The method according to claim 1, characterized in that: Determining the sensitivity level corresponding to each data column in the structured data according to the structured data by using the sensitivity level classification model includes: The sensitivity level corresponding to each data column in the structured data is determined according to the structured data by using a gradient boosted decision tree (GBDT) model.

5. The method according to claim 1, characterized in that The determining, according to the data access level value and data access column secret value range of the target user, the sensitivity level and column secret value corresponding to each column of data in the structured data, the target data column accessible to the target user in the structured data comprises: For each data column in the structured data, determine a data column whose corresponding sensitivity level is less than or equal to the data access level value of the target user and whose corresponding column encryption value is within the data access column encryption value range of the target user as the target data column.

6. A data delivery device, characterized in that: The device comprises: The identity authentication module is used to verify the identity of the target user when receiving a data access request initiated by the target user; and when the identity of the target user is verified to be a legal identity, determine the data access level value and the data access column secret value range of the target user; the data access level value and the data access column secret value range are assigned to the target user according to the identity information of the target user; A data acquisition module, used to acquire structured data corresponding to the data access request; A level determination module, configured to determine the sensitivity level corresponding to each data column in the structured data according to the structured data through a sensitivity level classification model; A column encryption value configuration module is used to select a sensitive data column from the structured data according to the sensitivity level corresponding to each data column in the structured data, and encrypt the sensitive data column to obtain a column encryption value corresponding to the sensitive data column; configure a minimum column encryption value for other data columns in the structured data except the sensitive data column as the column encryption value corresponding to the data column; An accessible data determination module, used to determine the target data column accessible to the target user in the structured data according to the data access level value and data access column secret value range of the target user, and the sensitivity level and column secret value corresponding to each column of data in the structured data; The column secret value configuration module is specifically used for: According to the sensitivity levels corresponding to the data columns in the structured data, determining the data columns whose corresponding sensitivity levels meet the preset sensitivity conditions as reference data columns, and determining the other data columns in the structured data except the reference data columns as comparison data columns; For each of the comparison data columns, calculating the correlation degree between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column; The comparison data column and the reference data column whose corresponding association degree meets the preset association condition are determined as the sensitive data column.

7. The device according to claim 6, characterized in that The column secret value configuration module is also used for: Before calculating the correlation between each comparison data column and the reference data column, dimensionless processing is performed on each data column in the structured data.

8. The device according to claim 6, characterized in that The column secret value configuration module is specifically used for: For each of the comparison data columns, a grey correlation analysis algorithm is used to calculate a grey correlation coefficient between the comparison data column and the reference data column as the correlation degree corresponding to the comparison data column.

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