Data security level determination method, device and server

Abstract

The present invention provides a method, device and server for determining data security level, comprising: obtaining a data set to be classified; inputting the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with multiple data evaluation indicators; and determining the data security level of the data set to be classified based on the data evaluation indicators through the data classification model. The present invention can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device and server for determining a data security level. Background Art

[0002] Data security classification is crucial in data security governance. It is the basis for the compilation of internal management systems, the implementation of technical support systems, and the rational allocation of energy and efforts during operation and maintenance. In addition, by determining the data security level, it is also convenient to achieve different degrees of security protection according to different data security levels during the subsequent use of data. At present, the data security classification methods provided by relevant technologies have many problems such as large computational complexity, low accuracy, and high time cost. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a method, device and server for determining a data security level, which can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

[0004] In a first aspect, an embodiment of the present invention provides a method for determining a data security level, comprising: obtaining a data set to be classified; inputting the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with a plurality of data evaluation indicators; and determining the data security level of the data set to be classified based on the data evaluation indicators through the data classification model.

[0005] In one embodiment, the step of determining the data security level of the data set to be classified based on the data evaluation indicators includes: performing feature extraction on the data set to be classified to obtain data features of the data set to be classified; determining, based on the data features, the target indicator level of the data set to be classified relative to each of the data evaluation indicators; and determining the data security level of the data set to be classified based on each of the target indicator levels.

[0006] In one embodiment, the step of determining the target indicator level of the data set to be classified relative to each of the data evaluation indicators based on the data characteristics includes: for each of the data evaluation indicators, screening out the target characteristic corresponding to the data evaluation indicator from the data characteristics; and determining the target indicator level of the data set to be classified relative to the data evaluation indicator based on the data evaluation indicator and the target characteristic corresponding to the data evaluation indicator.

[0007] In one embodiment, the data evaluation index includes an importance index, the importance index includes a correspondence between an index level and a first keyword, and the target feature corresponding to the importance index includes a first keyword feature; the step of determining the target index level of the data set to be classified relative to the data evaluation index based on the data evaluation index and the target feature corresponding to the data evaluation index includes: determining the index level corresponding to the first keyword feature as the target index level of the data set to be classified relative to the importance index.

[0008] In one embodiment, the data evaluation index includes a sensitivity index, and the target feature corresponding to the sensitivity index includes a second keyword feature; the step of determining the target indicator level of the data set to be classified relative to the data evaluation index based on the data evaluation index and the target feature corresponding to the data evaluation index includes: if the second keyword feature is screened out from the data features, determining that the target indicator level of the data set to be classified relative to the sensitivity index is a first level; if the second keyword feature is not screened out from the data features, determining that the target indicator level of the data set to be classified relative to the sensitivity index is a second level; wherein the second level is lower than the first level.

[0009] In one embodiment, the data evaluation indicator includes a data volume indicator, the data volume indicator includes a correspondence between an indicator level and a data volume threshold interval, and the target feature corresponding to the data volume indicator includes a data volume feature; the step of determining the target indicator level of the data set to be classified relative to the data evaluation indicator based on the data evaluation indicator and the target feature corresponding to the data evaluation indicator includes: determining the indicator level corresponding to the data volume threshold interval in which the data volume feature is located as the target indicator level of the data set to be classified relative to the data volume indicator.

[0010] In one embodiment, the step of determining the data security level of the data set to be classified based on each of the target indicator levels includes: obtaining a mapping relationship between pre-configured indicator levels and security levels; and determining the security level that matches each of the target indicator levels in the mapping relationship as the data security level of the data set to be classified.

[0011] In a second aspect, an embodiment of the present invention also provides a device for determining a data security level, comprising: a data acquisition module for acquiring a data set to be classified; a data input module for inputting the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with multiple data evaluation indicators; and a level determination module for determining the data security level of the data set to be classified based on the data evaluation indicators through the data classification model.

[0012] In a third aspect, an embodiment of the present invention further provides a server, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0014] The embodiments of the present invention provide a method, device and server for determining data security level, which first obtains a data set to be classified, then inputs the data set to be classified into a data classification model configured with multiple data evaluation indicators, and finally determines the data security level of the data set to be classified based on the data evaluation indicators through the data classification model. The above method uses the data evaluation indicators in the data classification model to evaluate the security level of the data set to be classified, which can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a method for determining a data security level provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of a data classification model provided by an embodiment of the present invention;

[0020] Figure 3A schematic diagram of the structure of a device for determining a data security level provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the structure of a server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] At present, the data security classification method provided by the relevant technology has many problems such as large amount of calculation, low accuracy, and high time cost. Based on this, the implementation of the present invention provides a method, device and server for determining the data security level, which can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

[0024] To facilitate understanding of this embodiment, a method for determining a data security level disclosed in an embodiment of the present invention is first described in detail. Figure 1 The flowchart of a method for determining a data security level is shown in FIG. 1 , and the method mainly includes the following steps S102 to S106:

[0025] Step S102, obtaining a data set to be classified. The data set to be classified may be medical data, government data, or financial data. In one embodiment, a data upload channel may be provided for the user, so that the user can upload the data set to be classified through the data upload channel; in another embodiment, the data set to be classified may also be called from a related system (such as a medical system) through an interface.

[0026] Step S104, input the data set to be classified into a pre-built data classification model. The data classification model is configured with multiple data evaluation indicators, which can be used to measure the importance, sensitivity and data volume of the data to be classified. In one embodiment, the input of the data classification model is the data set to be classified, and the output is the data security level.

[0027] Step S106, using the data classification model, determine the data security level of the data set to be classified based on the data evaluation index. In one embodiment, the target index level of the data set to be classified relative to each data evaluation index can be determined, and then the data security level that matches each target index level can be found in the pre-configured mapping relationship between the index level and the security level.

[0028] For the method for determining the data security level provided in the aforementioned embodiment, using the data evaluation indicators in the data classification model to evaluate the security level of the data set to be classified can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

[0029] To facilitate understanding of the above step S106, an embodiment of the present invention provides an implementation method for determining the data security level of a data set to be classified based on a data evaluation index, see the following steps 1 to 3:

[0030] Step 1, extract features from the dataset to be classified to obtain data features of the dataset to be classified. The data features may include data identifiers, keywords, target objects, data volume, etc. Exemplarily, the dataset to be classified may carry an identifier for characterizing the purpose of the dataset; or, if the dataset to be classified contains text data, keywords in the text data may be extracted; or, if the dataset to be classified contains image data or video data, image recognition, target detection, etc. may be performed on the image data or video data to monitor the target objects contained in the image data or video data; or, the data volume of the dataset to be classified may be counted.

[0031] Step 2: Determine the target indicator level of the data set to be classified relative to each data evaluation indicator based on the data characteristics. The embodiment of the present invention exemplarily provides an implementation method for determining the target indicator level of the data set to be classified relative to each data evaluation indicator based on the data characteristics, see the following steps 2.1 to 2.2:

[0032] Step 2.1, for each data evaluation indicator, filter out the target feature corresponding to the data evaluation indicator from the data features. In one embodiment, assuming that the data classification model is configured with an importance index, a sensitivity index, and a data volume index, it is necessary to filter out target features related to importance, target features related to sensitivity, and target features related to data volume from the data features. Exemplarily, the target features related to importance can be data features that affect social security, public interests, or the legitimate rights and interests of individuals and organizations, the target features related to sensitivity can be data features related to personal identity and personal privacy, and the target features related to data volume are the number of data items contained in the data set to be classified.

[0033] Step 2.2, based on the data evaluation indicator and the target feature corresponding to the data evaluation indicator, determine the target indicator level of the data set to be classified relative to the data evaluation indicator.

[0034] In one embodiment, the data classification model is configured with multiple data evaluation indicators, and the data evaluation indicators include one or more of importance indicators, sensitivity indicators and data volume indicators. The importance indicators, sensitivity indicators and data volume indicators correspond to multiple indicator levels respectively. For example, the importance indicators correspond to three indicator levels of general data, important data and core data, the sensitivity indicators correspond to two indicator levels of sensitive data and non-sensitive data, and the data volume indicators correspond to three indicator levels of small amount, medium amount and large amount. In actual applications, the data evaluation indicators included in the data classification model can be set based on actual needs, and the indicator level corresponding to each data evaluation indicator can also be set based on actual needs. The embodiments of the present invention are not limited to this.

[0035] The embodiments of the present invention exemplarily provide Figure 2 A schematic diagram of a data classification model is shown, which illustrates: (1) indicator levels corresponding to importance indicators: general data, important data, core data; (2) indicator levels corresponding to sensitivity indicators: sensitive data, non-sensitive data; (3) small amount, medium amount, and large amount corresponding to data quantity indicators.

[0036] exist Figure 2 On the basis of the importance index, sensitivity index and data volume index, the embodiments of the present invention respectively provide some implementation methods for determining the target index level of the data set to be classified relative to the data evaluation index according to the data evaluation index and the target feature corresponding to the data evaluation index, see the following methods 1 to 3:

[0037] Method 1: If the data evaluation index includes an importance index, the importance index includes a correspondence between the index level and the first keyword, and the target feature corresponding to the importance index includes the first keyword feature. On this basis, when determining the target index level of the data set to be classified relative to the data evaluation index, the index level corresponding to the first keyword feature can be determined as the target index level of the data set to be classified relative to the importance index. For example, the correspondence between the first keyword and the index level can be set in advance according to the impact and importance of the data on social security, public interests, or the legitimate rights and interests of individuals and organizations, such as "disease-core data", "drugs-important data", "dietary recommendations-general data", so as to find the target index level corresponding to the above-mentioned first keyword feature in the correspondence. In actual applications, if the data set to be classified contains multiple first keyword features, the highest index level can be selected as the target index level among the index levels corresponding to each first keyword feature.

[0038] Method 2: If the data evaluation index includes a sensitivity index, the target feature corresponding to the sensitivity index includes a second keyword feature. On this basis, when determining the target indicator level of the data set to be classified relative to the data evaluation index, the following (1) to (2) may be included: (1) If the second keyword feature is screened out from the data feature, the target indicator level of the data set to be classified relative to the sensitivity index is determined to be the first level; if the second keyword feature is not screened out from the data feature, the target indicator level of the data set to be classified relative to the sensitivity index is determined to be the second level. The second level is lower than the first level. Optionally, the first level may be sensitive data, and the second level may be non-sensitive data. In one embodiment, it may be determined whether the second keyword feature is screened out from the data feature, and the second keyword feature may be privacy data that identifies an individual's identity, such as an identity card, a medical insurance, etc. If the judgment result is yes, the amount of data to be classified may be determined as sensitive data; if the judgment result is no, the amount of data to be classified may be determined as non-sensitive data.

[0039] Method three: If the data evaluation index includes a data volume index, the data volume index includes the correspondence between the index level and the data volume threshold interval, and the target feature corresponding to the data volume index includes the data volume feature. On this basis, when determining the target index level of the data set to be classified relative to the data evaluation index, the index level corresponding to the data volume threshold interval in which the data volume feature is located can be determined as the target index level of the data set to be classified relative to the data volume index. For example, the data volume threshold interval corresponding to a small amount is: 0 to 50, the data volume threshold interval corresponding to a medium amount is: 50 to 1000, and the data volume threshold interval corresponding to a small amount is: above 1000. Assuming that there are 800 data volume features, it is determined that the data volume feature is in the data volume threshold interval of 50 to 1000, and then the target index level of the data set to be classified is determined to be a medium amount.

[0040] Step 3, determine the data security level of the data set to be classified based on each target indicator level. In one embodiment, the mapping relationship between the pre-configured indicator level and the security level can be obtained, and then the security level that matches each target indicator level in the mapping relationship is determined as the data security level of the data set to be classified. For ease of understanding, the embodiment of the present invention exemplarily provides a mapping relationship as shown in Table 1, which divides health care data into 4 levels, and the data is classified according to the three dimensions of importance, sensitivity and data volume. Assuming that the target indicator levels of the data set to be classified include: core data, sensitive data, and a small amount, it can be determined that the security level of the data set to be classified is level 4. In actual applications, more security levels can be set according to actual needs, and the embodiment of the present invention does not limit this.

[0041] Table 1

[0042] Importance Sensitivity Data volume Data security level Core Data sensitive few Level 4 Core Data sensitive medium Level 4 Core Data sensitive many Level 4 Core Data Non-sensitive few Level 4 Core Data Non-sensitive medium Level 4 Core Data Non-sensitive many Level 4 Important data sensitive few Level 3 Important data sensitive medium Level 3 Important data sensitive many Level 4 Important data Non-sensitive few Level 2 Important data Non-sensitive medium Level 3 Important data Non-sensitive many Level 4 General Data sensitive few Does not exist General Data sensitive medium Does not exist General Data sensitive many Does not exist General Data Non-sensitive few Level 1 General Data Non-sensitive medium Level 1 General Data Non-sensitive many Level 2

[0043] To sum up, the method for determining the data security level provided in the embodiment of the present invention uses a three-dimensional data classification model based on importance, sensitivity and data volume to determine the data security level. When the data set to be classified is medical data, the embodiment of the present invention can better solve the data classification problem in the health care industry, thereby ensuring the safe use of medical data.

[0044] For the data security level determination method provided in the above embodiment, the present invention provides a data security level determination device, see Figure 3 The structure diagram of a device for determining a data security level is shown in FIG. 1 , and the device mainly includes the following parts:

[0045] A data acquisition module 302 is used to acquire a data set to be classified;

[0046] The data input module 304 is used to input the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with multiple data evaluation indicators;

[0047] The level determination module 306 is used to determine the data security level of the data set to be classified based on the data evaluation index through the data classification model.

[0048] An embodiment of the present invention provides a device for determining the data security level as described above, which uses the data evaluation indicators in the data classification model to evaluate the security level of the data set to be classified, which can effectively reduce the amount of calculation and time cost required to determine the data security level, and can also significantly improve the accuracy of the data security level.

[0049] In one embodiment, the level determination module 306 is also used to: perform feature extraction on the data set to be classified to obtain data features of the data set to be classified; determine the target indicator level of the data set to be classified relative to each data evaluation indicator based on the data features; and determine the data security level of the data set to be classified based on each target indicator level.

[0050] In one embodiment, the level determination module 306 is also used to: for each data evaluation indicator, filter out the target feature corresponding to the data evaluation indicator from the data features; and determine the target indicator level of the data set to be graded relative to the data evaluation indicator based on the data evaluation indicator and the target feature corresponding to the data evaluation indicator.

[0051] In one embodiment, the data evaluation index includes an importance index, the importance index includes a correspondence between an index level and a first keyword, and the target feature corresponding to the importance index includes a first keyword feature; the level determination module 306 is also used to: determine the index level corresponding to the first keyword feature as the target index level of the data set to be graded relative to the importance index.

[0052] In one embodiment, the data evaluation index includes a sensitivity index, and the target feature corresponding to the sensitivity index includes a second keyword feature; the level determination module 306 is also used to: if the second keyword feature is screened out from the data features, determine that the target indicator level of the data set to be classified relative to the sensitivity index is the first level; if the second keyword feature is not screened out from the data features, determine that the target indicator level of the data set to be classified relative to the sensitivity index is the second level; wherein the second level is lower than the first level.

[0053] In one embodiment, the data evaluation index includes a data volume index, the data volume index includes a correspondence between an index level and a data volume threshold interval, and the target feature corresponding to the data volume index includes a data volume feature; the level determination module 306 is also used to: determine the index level corresponding to the data volume threshold interval in which the data volume feature is located as the target index level of the data set to be graded relative to the data volume index.

[0054] In one embodiment, the level determination module 306 is further used to: obtain a mapping relationship between pre-configured indicator levels and security levels; and determine the security level that matches each target indicator level in the mapping relationship as the data security level of the data set to be classified.

[0055] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0056] An embodiment of the present invention provides a server. Specifically, the server includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned embodiments.

[0057] Figure 4 A structural diagram of a server provided in an embodiment of the present invention, the server 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0058] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0059] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0060] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0061] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0062] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0063] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for 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 methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0064] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining a data security level, characterized in that: include: Obtain the data set to be classified; Input the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with a plurality of data evaluation indicators; wherein the data evaluation indicators include an importance indicator, a sensitivity indicator, and a data volume indicator, wherein the importance indicator includes a corresponding relationship between an indicator level and a first keyword, a target feature corresponding to the importance indicator includes a first keyword feature, a target feature corresponding to the sensitivity indicator includes a second keyword feature, the data volume indicator includes a corresponding relationship between an indicator level and a data volume threshold interval, and the target feature corresponding to the data volume indicator includes a data volume feature; The data security level of the data set to be classified is determined based on the data evaluation index through the data classification model.

2. The method according to claim 1, characterized in that The step of determining the data security level of the to-be-classified data set based on the data evaluation index comprises: Extracting features of the data set to be classified to obtain data features of the data set to be classified; Determining, according to the data characteristics, the target indicator level of the to-be-classified data set relative to each of the data evaluation indicators; Based on the levels of each target indicator, the data security level of the data set to be classified is determined.

3. The method according to claim 2, characterized in that The step of determining the target indicator level of the to-be-classified data set relative to each of the data evaluation indicators according to the data characteristics comprises: For each of the data evaluation indicators, a target feature corresponding to the data evaluation indicator is selected from the data features; According to the data evaluation indicator and the target feature corresponding to the data evaluation indicator, a target indicator level of the to-be-classified data set relative to the data evaluation indicator is determined.

4. The method according to claim 3, characterized in that The step of determining the target indicator level of the to-be-classified data set relative to the data evaluation indicator according to the data evaluation indicator and the target feature corresponding to the data evaluation indicator comprises: The index level corresponding to the first keyword feature is determined as the target index level of the to-be-classified data set relative to the importance index.

5. The method according to claim 3, characterized in that: The step of determining the target indicator level of the to-be-classified data set relative to the data evaluation indicator according to the data evaluation indicator and the target feature corresponding to the data evaluation indicator comprises: If the second keyword feature is screened out from the data features, determining that the target index level of the to-be-classified data set relative to the sensitivity index is the first level; If the second keyword feature is not screened out from the data features, it is determined that the target index level of the to-be-classified data set relative to the sensitivity index is a second level; wherein the second level is lower than the first level.

6. The method according to claim 3, characterized in that The step of determining the target indicator level of the to-be-classified data set relative to the data evaluation indicator according to the data evaluation indicator and the target feature corresponding to the data evaluation indicator comprises: The index level corresponding to the data volume threshold interval in which the data volume feature is located is determined as the target index level of the data set to be classified relative to the data volume index.

7. The method according to claim 2, characterized in that The step of determining the data security level of the data set to be classified based on the levels of each target indicator includes: Obtain the mapping relationship between pre-configured indicator levels and security levels; The security level that matches each of the target indicator levels in the mapping relationship is determined as the data security level of the data set to be classified.

8. A device for determining a data security level, characterized in that: include: A data acquisition module, used to acquire the data set to be classified; A data input module, used for inputting the data set to be classified into a pre-built data classification model; wherein the data classification model is configured with a plurality of data evaluation indicators; wherein the data evaluation indicators include an importance indicator, a sensitivity indicator, and a data volume indicator, wherein the importance indicator includes a corresponding relationship between an indicator level and a first keyword, a target feature corresponding to the importance indicator includes a first keyword feature, a target feature corresponding to the sensitivity indicator includes a second keyword feature, the data volume indicator includes a corresponding relationship between an indicator level and a data volume threshold interval, and the target feature corresponding to the data volume indicator includes a data volume feature; The level determination module is used to determine the data security level of the data set to be classified based on the data evaluation index through the data classification model.

9. A server, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

Images