Large model data desensitization method and related device

Abstract

The embodiment of the invention provides a large model data desensitization method and a related device, and the method comprises the steps: obtaining an Internet address of a target device requesting large model data, and if the Internet address of the target device is in a preset Internet address range, sending the large model data to the target device according to the Internet address; and if the internet address of the target device is not in the preset internet address range, identifying sensitive data in the large model data, performing desensitization processing on the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitization data, and sending the desensitization data to the target device according to the internet address. By combining the internet address of the target device, the resource occupancy rate of the server for desensitization processing and the desensitization weight of the sensitive data, the strategy for desensitization processing of the large model data is comprehensively determined, the security requirement of the large model data can be met, and the timeliness requirement of the large model data during desensitization processing can also be met.

Description

Technical Field

[0001] The present invention relates to the field of computers, and particularly to a method for desensitizing large model data and related devices. Background Art

[0002] With the advent of the big data era, data, as an important information asset, its security and privacy protection have received increasing attention. Especially in the fields of artificial intelligence, machine learning, etc., the desensitization processing of large model data has become crucial.

[0003] However, the current desensitization processing of large model data desensitizes all large model data. When the timeliness requirement for large model data is high, the time for desensitizing all large model data is long, resulting in low desensitization efficiency and reducing the timeliness of data desensitization. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method for desensitizing large model data and related devices, which can improve the timeliness of data desensitization while ensuring the security of large model data.

[0005] To achieve the above purpose, this application has the following technical solutions:

[0006] This application provides a method for desensitizing large model data, including:

[0007] Obtain the Internet address of the target device requesting large model data;

[0008] If the Internet address of the target device is within the preset Internet address range, send the large model data to the target device according to the Internet address;

[0009] If the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, desensitize the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

[0010] Optionally, the desensitizing the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data includes:

[0011] If the server resource occupancy rate is greater than the preset consumption threshold, desensitize the first sensitive data with the highest desensitization weight in the sensitive data to obtain desensitized data.

[0012] Optionally, the desensitizing the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data includes:

[0013] If the server resource occupancy rate is less than the preset consumption threshold, desensitize the sensitive data with all desensitization weights to obtain desensitized data.

[0014] Optionally, the preset Internet address range includes a preset Internet Protocol (IP) address or a preset domain name; the method further includes:

[0015] Pre-configure the preset IP address or the preset domain name.

[0016] Optionally, the method further includes:

[0017] Sort the sensitive data in the large model data from high to low according to sensitivity to obtain a sensitive sequence;

[0018] Configure desensitization weights for the sensitive data in the sensitive sequence, and the desensitization weights gradually decrease.

[0019] Optionally, the method further includes:

[0020] Obtain the desensitization interface response duration. If the desensitization interface response duration is greater than the preset duration threshold, desensitize the sensitive data with different sensitivities according to the desensitization weights of the sensitive data to obtain desensitized data.

[0021] This application provides a large model data desensitization device, which is characterized by including:

[0022] An acquisition unit, configured to acquire the Internet address of a target device that requests large model data;

[0023] A sending unit, configured to, if the Internet address of the target device is within the preset Internet address range, send the large model data to the target device according to the Internet address;

[0024] A desensitization unit, configured to, if the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, desensitize the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weights of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

[0025] Optionally, the device further includes a processing unit; the processing unit is configured to:

[0026] Obtain the desensitization interface response duration. If the desensitization interface response duration is greater than the preset duration threshold, desensitize the sensitive data with different sensitivities according to the desensitization weights of the sensitive data to obtain desensitized data.

[0027] This application provides a large model data desensitization device, which is characterized in that the device includes: a processor and a memory;

[0028] The memory is used to store instructions;

[0029] The processor is used to execute the instructions in the memory and execute the method described in any one of the above.

[0030] This application provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer device, the computer device is enabled to execute the method described in any one of the above.

[0031] This application provides a large model data desensitization method. The method includes: obtaining the Internet address of a target device that requests large model data. If the Internet address of the target device is within a preset Internet address range, the large model data is sent to the target device according to the Internet address, that is, if the target device that requests large model data belongs to a device within the preset Internet address range that meets data security requirements, the large model data does not need to be desensitized, so that the large model data can be quickly sent to the target device to ensure the timeliness of the large model data; if the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, desensitize the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address, that is, if the target device that requests large model data does not belong to a device within the preset Internet address range that meets data security requirements, the sensitive data with different sensitivities can be desensitized according to the server resource occupancy rate and the desensitization weight of the sensitive data in the large model data, so as to realize desensitization of the sensitive data according to the desensitization weight composed of the sensitivity, and improve the timeliness of data desensitization while ensuring the security of the large model data. Based on this, the large model data desensitization method provided by this application comprehensively determines the strategy for desensitizing the large model data by combining the Internet address of the target device, the server resource occupancy rate for desensitization processing, and the desensitization weight of the sensitive data, so as to realize intelligent desensitization of the large model data, which can not only meet the security requirements for the large model data, but also meet the timeliness requirements for the large model data during desensitization processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1Shows a schematic flowchart of a large model data desensitization method provided by an embodiment of the present application;

[0034] Figure 2 Shows a schematic flowchart of the desensitization of a large model provided by an embodiment of the present application;

[0035] Figure 3 Shows a schematic structural diagram of an image deblurring device provided by an embodiment of the present application. Detailed implementation manners

[0036] To make the above objects, features, and advantages of the present application more obvious and understandable, the following detailed description of the specific implementation manners of the present application will be made in conjunction with the accompanying drawings.

[0037] In the following description, many specific details are set forth to facilitate a thorough understanding of the present application. However, the present application may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0038] The present application is described in detail in conjunction with the schematic diagrams. When describing the embodiments of the present application in detail, for the sake of convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present application herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0039] Currently, the desensitization process for large model data is to desensitize all large model data. When the timeliness requirement for large model data is relatively high, the time required to desensitize all large model data is relatively long, resulting in low desensitization efficiency and reducing the timeliness of data desensitization.

[0040] Based on this, the present application provides a method for desensitizing large model data. The method includes: obtaining the Internet address of the target device that requests large model data. If the Internet address of the target device is within a preset Internet address range, the large model data is sent to the target device according to the Internet address. That is, if the target device that requests large model data belongs to the devices within the preset Internet address range that meet data security requirements, the large model data does not need to be desensitized, and thus the large model data can be quickly sent to the target device to ensure the timeliness of the large model data. If the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, and desensitize the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address. That is, if the target device that requests large model data does not belong to the devices within the preset Internet address range that meet data security requirements, the sensitive data with different sensitivities can be desensitized according to the server resource occupancy rate and the desensitization weight of the sensitive data in the large model data, so as to realize the desensitization of the sensitive data according to the desensitization weight composed of the sensitivity, and improve the timeliness of data desensitization while ensuring the security of the large model data. Based on this, the method for desensitizing large model data provided by the present application comprehensively determines the strategy for desensitizing the large model data by combining the Internet address of the target device, the server resource occupancy rate for desensitization processing, and the desensitization weight of the sensitive data, so as to realize the intelligent desensitization of the large model data, which can not only meet the security requirements for the large model data, but also meet the timeliness requirements for the large model data during desensitization processing.

[0041] To better understand the technical solutions and technical effects of the present application, specific embodiments will be described in detail below with reference to the accompanying drawings.

[0042] Reference Figure 1 As shown, it is a flowchart of a method for desensitizing large model data provided by an embodiment of the present application. The method includes the following steps:

[0043] S101, obtain the Internet address of the target device that requests large model data.

[0044] In the embodiment of the present application, the target device requests to obtain large model data. To ensure the security of the large model data, the Internet address of the target device that requests large model data can be obtained. Through the Internet address of the target device, the network environment where the target device is located, such as the intranet environment or the extranet environment, can be determined, so as to assist the subsequent desensitization processing of the large model data.

[0045] Specifically, the Internet address may include an Internet Protocol (IP) address or a domain name.

[0046] S102. If the Internet address of the target device is within the preset Internet address range, send the large model data to the target device according to the Internet address.

[0047] In the embodiments of the present application, after obtaining the Internet address of the target device, it can be determined whether the Internet address of the target device is within the preset Internet address range. If the Internet address of the target device is within the preset Internet address range, send the large model data to the target device according to the Internet address. That is to say, the preset Internet address range represents a relatively secure network environment, such as an intranet environment. The fact that the Internet address of the target device is within the preset Internet address range means that the target device is a device in the intranet environment, and it is possible to directly send the large model data to the target device for application without performing desensitization processing on the large model data, thereby improving the timeliness of the large model data.

[0048] Specifically, the preset Internet address range includes a preset Internet Protocol (IP) address or a preset domain name. The preset IP address or preset domain name can be pre-configured, so as to facilitate subsequent determination of whether the target device meets the preset IP address or preset domain name, and ensure the security of the large model data.

[0049] S103. If the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, perform desensitization processing on the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

[0050] In the embodiments of the present application, after obtaining the Internet address of the target device, it can be determined whether the Internet address of the target device is within the preset Internet address range. If the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, perform desensitization processing on the sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address. That is to say, the fact that the Internet address of the target device is not within the preset Internet address range means that the target device is a device in the external network environment, and it is necessary to use the server to perform desensitization processing on the large model data. It is possible to first identify the sensitive data in the large model data, and then perform desensitization processing on the sensitive data with different sensitivities in combination with the server resource occupancy rate and the desensitization weight of the sensitive data, so as to achieve desensitization processing of the sensitive data according to the desensitization weight composed of the sensitivity, obtain desensitized data, and send the large model data including the desensitized data to the target device, while ensuring the security of the large model data, improving the timeliness of data desensitization.

[0051] Among them, the sensitive data is the data that needs to be protected in the large model data. Sensitive data has different sensitivities, and different sensitivities correspond to different data security levels. The higher the sensitivity, the higher the requirement for data security. Based on the different sensitivities of sensitive data, the desensitization weights of sensitive data are also different.

[0052] As a possible implementation, the sensitive data in the large model data is sorted from high to low according to the sensitivity to obtain a sensitive sequence; desensitization weights are configured for the sensitive data in the sensitive sequence, and the desensitization weights gradually decrease. That is to say, when configuring the desensitization weights of sensitive data, the higher the sensitivity, the higher the configured desensitization weight of the sensitive data, and the higher the priority for desensitizing the sensitive data, so as to achieve the security of sensitive data with high sensitivity.

[0053] Based on data security, sensitive data is divided into different sensitivities, and each sensitivity corresponds to a desensitization weight. For example, the sensitive data includes three sensitivities, namely high sensitivity, medium sensitivity, and low sensitivity. High sensitivity corresponds to a high sensitive weight, medium sensitivity corresponds to a medium sensitive weight, and low sensitivity corresponds to a low sensitive weight.

[0054] In the embodiments of the present application, in addition to the desensitization weight of sensitive data affecting the desensitization process of sensitive data, the server resource occupancy rate also affects the desensitization process of sensitive data. When the server resource occupancy rate is relatively high, it means that the server resources are scarce, and the sensitive data with different sensitivities can be desensitized hierarchically according to the desensitization weights of the sensitive data. When the server resource occupancy rate is relatively low and there are sufficient server resources to process sensitive data, all sensitive data can be desensitized.

[0055] As a possible implementation, the sensitive data includes first sensitive data, and the first sensitive data has the highest sensitivity, that is, the first sensitive data has the highest desensitization weight. If the server resource occupancy rate is greater than the preset consumption threshold, the first sensitive data with the highest desensitization weight in the sensitive data is desensitized to obtain desensitized data. That is to say, when the server resource occupancy rate is greater than or equal to the preset consumption threshold, it means that the server resource occupancy rate is relatively high and the server resources are scarce. At this time, the first sensitive data with the highest desensitization weight can be desensitized, that is, the security of the first sensitive data with the highest sensitivity is ensured first, and at the same time, the timeliness of the desensitization process of the large model data is ensured under the condition of limited server resources.

[0056] As another possible implementation, if the server resource occupancy rate is less than the preset consumption threshold, desensitize the sensitive data with all desensitization weights to obtain desensitized data. That is to say, when the server resource occupancy rate is less than the preset consumption threshold, it means that the server resource occupancy rate is low and there are more server resources. At this time, the sensitive data with all desensitization weights can be desensitized. That is, when there are more server resources, all sensitive data is desensitized to ensure the timeliness of the large model data desensitization process while further improving the security of the large model data.

[0057] Specifically, the server resource occupancy rate includes the central processing unit (CPU) occupancy rate or the memory occupancy rate. The preset consumption threshold is a value preset for determining the server resource consumption situation.

[0058] As an example, the server resource occupancy rate is greater than the preset consumption threshold when the duration for which the CPU occupancy rate reaches 100% is greater than 10s.

[0059] As another example, the server resource occupancy rate is greater than the preset consumption threshold when the memory occupancy rate is greater than 75%.

[0060] In practical applications, before desensitizing the sensitive data, the large model data can be cleaned, integrated, and formatted to ensure data standardization. Then, technologies such as natural language processing and machine learning are used to identify the sensitive data in the large model data.

[0061] In practical applications, the desensitization of sensitive data can include multiple processing modes. The first is to replace all or part of the sensitive data with special characters. For example, use the * symbol to replace the beginning and end parts or the middle part of the sensitive data. The second is to map the sensitive data to other data, and a mapping relationship needs to be preconfigured. For example, map a string of sensitive numbers to other numbers. The third is random desensitization processing, that is, randomly replace the sensitive data with other data. The fourth is scrambled desensitization processing, that is, scramble the order of the sensitive data. The fifth is deletion processing, that is, delete the sensitive data.

[0062] In the embodiments of the present application, in addition to the server resources and the desensitization weights of the sensitive data affecting the desensitization of the sensitive data, the desensitization interface response duration for the server to receive the large model data for desensitization processing can also affect the desensitization of the sensitive data.

[0063] Specifically, the desensitization interface response duration can be obtained. If the desensitization interface response duration is greater than the preset duration threshold, desensitize the sensitive data with different sensitivities according to the desensitization weights of the sensitive data to obtain desensitized data. That is to say, if the desensitization interface response duration is long, hierarchical desensitization processing of the sensitive data with different sensitivities can be performed according to the desensitization weights.

[0064] As a possible implementation, if the response duration of the desensitization interface is greater than or equal to the preset duration threshold, it means that the response duration of the desensitization interface is too long. The first sensitive data with the highest desensitization weight can be desensitized, that is, the security of the first sensitive data with the highest sensitivity is ensured first. At the same time, under the condition of limited server resources, the timeliness of the large model data desensitization process is ensured.

[0065] As another possible implementation, if the response duration of the desensitization interface is less than the preset duration threshold, it means that the response duration of the desensitization interface meets the timeliness requirements. Sensitive data with all desensitization weights can be desensitized, that is, when there are more server resources, all sensitive data is desensitized. While ensuring the timeliness of the large model data desensitization process, the security of the large model data is further improved.

[0066] In the embodiments of the present application, after desensitizing sensitive data to obtain desensitized data, the semantic clarity of the desensitized data can be judged. For example, it can be judged whether the desensitized data has unclear semantics, such as having grammar errors, so as to obtain the desensitization effect. If the desensitization effect is poor, the sensitive data can be desensitized again at this time to obtain updated desensitized data, and the semantic clarity of the updated desensitized data is judged again, and the desensitization effect is judged again until the desensitization effect meets the requirements. At this time, the desensitized data is output.

[0067] Reference Figure 2 As shown, it is a schematic flowchart of a large model data desensitization provided by the embodiments of the present application. First, a preset Internet address range and desensitization rules are preconfigured. Subsequently, it is determined whether the Internet address of the target device belongs to the preset Internet address range according to the preset Internet address range. If so, the large model data is directly sent to the target device, that is, the data is displayed in plain text; if not, the large model data is cleaned, sensitive data is identified, the server resource consumption situation is analyzed, and the desensitization weight of the sensitive data is configured. The data to be desensitized is determined by combining the server resource consumption situation and the desensitization weight of the sensitive data, and desensitization processing is performed according to the preconfigured desensitization rules. The desensitization effect is evaluated. If the desensitization effect is poor, desensitization processing is performed again. If the desensitization effect is good, the desensitized data is displayed; the response duration of the desensitization interface can also be recorded. If the response duration of the desensitization interface is [missing condition], the desensitization rules can be adjusted when performing desensitization processing.

[0068] It can be seen that the large model data desensitization method provided by the embodiments of the present application can pre-enter the network segments of Internet addresses that do not require desensitization, ensure the application of large model data in different scenarios, and support setting desensitization weights. It can perform intelligent dynamic desensitization based on server performance, ensuring data security while also guaranteeing the timeliness of big data desensitization, and having flexibility. By using advanced natural language processing and machine learning technologies, it can quickly and accurately desensitize large model data, improve data processing efficiency, and achieve the high efficiency of big data desensitization. The combination of multiple desensitization modes and sensitivity settings can ensure that the desensitized data not only meets the requirements of privacy protection but also does not damage the value and usage effect of the data, having accuracy. That is to say, the large model data desensitization method provided by the embodiments of the present application can automatically identify specific scenarios using Internet addresses, desensitize large model data after comprehensive consideration of desensitization weights, which is convenient for using large model data in the intranet environment, can ensure the security of large model data in the extranet environment, and at the same time ensure the timeliness of data desensitization.

[0069] Based on the large model data desensitization method provided in the above embodiments, the embodiments of the present application also provide a large model data desensitization device. Refer to Figure 3 As shown in the figure, it is a schematic structural diagram of a large model data desensitization device provided by the embodiments of the present application. The large model data desensitization device 200 provided by the embodiments of the present application includes:

[0070] An obtaining unit 210, configured to obtain the Internet address of a target device that requests large model data;

[0071] A sending unit 220, configured to, if the Internet address of the target device is within a preset Internet address range, send the large model data to the target device according to the Internet address;

[0072] A desensitization unit 230, configured to, if the Internet address of the target device is not within the preset Internet address range, identify sensitive data in the large model data, desensitize sensitive data with different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data to obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

[0073] As a possible implementation manner, the desensitization unit 230 is configured to:

[0074] If the server resource occupancy rate is greater than a preset consumption threshold, desensitize the first sensitive data with the highest desensitization weight in the sensitive data to obtain desensitized data.

[0075] As a possible implementation manner, the desensitization unit 230 is configured to:

[0076] If the server resource occupancy rate is less than the preset consumption threshold, desensitize the sensitive data with all desensitization weights to obtain desensitized data.

[0077] As a possible implementation, the preset Internet address range includes a preset Internet Protocol (IP) address or a preset domain name; the device further includes a first configuration unit; the first configuration unit is configured to:

[0078] Pre-configure the preset IP address or the preset domain name.

[0079] As a possible implementation, the device further includes a second configuration unit; the second configuration unit is configured to:

[0080] Sort the sensitive data in the large model data from high to low according to the sensitivity to obtain a sensitive sequence;

[0081] Configure desensitization weights for the sensitive data in the sensitive sequence, and the desensitization weights gradually decrease.

[0082] As a possible implementation, the device further includes a processing unit; the processing unit is configured to:

[0083] Obtain the response duration of the desensitization interface. If the response duration of the desensitization interface is greater than the preset duration threshold, desensitize the sensitive data with different sensitivities according to the desensitization weights of the sensitive data to obtain desensitized data.

[0084] Based on the large model data desensitization method provided in the above embodiments, the embodiments of the present application further provide a large model data desensitization device. The large model data desensitization device includes:

[0085] A processor and a memory. The number of processors can be one or more. In some embodiments of the present application, the processor and the memory can be connected by a bus or other means.

[0086] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or subsets thereof, or extended sets thereof, where the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic services and processing hardware-based tasks.

[0087] The processor controls the operation of the terminal device, and the processor may also be referred to as a CPU.

[0088] The method disclosed in the embodiments of the present application can be applied to a processor or implemented by a processor. The processor can be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above-mentioned processor can be a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed 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 art such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0089] The embodiments of the present application also provide a computer-readable storage medium for storing program code, and the program code is used to execute any one of the methods in the foregoing various embodiments.

[0090] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0091] It should be noted that the computer-readable medium described above in the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0092] When introducing the elements of the various embodiments of the present application, the articles "a", "an", "this", and "the" are all intended to mean that there is one or more elements. The words "comprising", "including", and "having" are all inclusive and mean that there may be other elements in addition to the listed elements.

[0093] It should be noted that those of ordinary skill in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0094] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0095] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description of the method embodiments. The device embodiments described above are only illustrative. The units and modules described as separate components may or may not be physically separated. In addition, some or all of the units and modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0096] The above is only the preferred embodiment of this application. Although this application has been disclosed above with preferred embodiments, it is not intended to limit this application. Any person skilled in the art can make many possible changes and modifications to the technical solution of this application by using the methods and technical contents disclosed above, or modify it into equivalent embodiments with equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of this application without departing from the technical solution of this application still fall within the scope of the protection of the technical solution of this application.

Claims

1. A large model data desensitization method, characterized in that: include: Obtaining the Internet address of the target device requesting the large model data; If the Internet address of the target device is within a preset Internet address range, sending the large model data to the target device according to the Internet address; If the Internet address of the target device is not within the preset Internet address range, identify the sensitive data in the large model data, desensitize the sensitive data of different sensitivities according to the server resource occupancy rate and the desensitizing weight of the sensitive data, obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

2. The method according to claim 1, characterized in that Desensitizing sensitive data of different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data, and obtaining desensitized data includes: If the server resource occupancy rate is greater than a preset consumption threshold, the first sensitive data with the highest desensitization weight among the sensitive data is desensitized to obtain desensitized data.

3. The method according to claim 1, characterized in that Desensitizing sensitive data of different sensitivities according to the server resource occupancy rate and the desensitization weight of the sensitive data, and obtaining desensitized data includes: If the server resource occupancy rate is less than the preset consumption threshold, all sensitive data with desensitization weights are desensitized to obtain desensitized data.

4. The method according to claim 1, characterized in that: The preset Internet address range includes a preset Internet Protocol IP address or a preset domain name; the method further includes: The preset IP address or the preset domain name is preconfigured.

5. The method according to claim 1, characterized in that The method further comprises: Sort the sensitive data in the large model data from high to low sensitivity to obtain a sensitive sequence; A desensitization weight is configured for the sensitive data in the sensitive sequence, and the desensitization weight is gradually reduced.

6. The method according to claim 1, characterized in that The method further comprises: The response time of the desensitizing interface is obtained. If the response time of the desensitizing interface is greater than the preset time threshold, the sensitive data of different sensitivities are desensitized according to the desensitization weight of the sensitive data to obtain desensitized data.

7. A large model data desensitization device, characterized in that: include: an acquisition unit, used for acquiring an Internet address of a target device requesting large model data; a sending unit, configured to send the large model data to the target device according to the Internet address if the Internet address of the target device is within a preset Internet address range; A desensitizing unit is used to identify sensitive data in the large model data if the Internet address of the target device is not within a preset Internet address range, desensitize sensitive data of different sensitivities according to server resource occupancy and desensitizing weights of sensitive data, obtain desensitized data, and send the desensitized data to the target device according to the Internet address.

8. The device according to claim 7, characterized in that The device further comprises a processing unit, the processing unit being configured to: The response time of the desensitizing interface is obtained. If the response time of the desensitizing interface is greater than the preset time threshold, the sensitive data of different sensitivities are desensitized according to the desensitization weight of the sensitive data to obtain desensitized data.

9. A large model data desensitization device, characterized in that: The device comprises: a processor and a memory; The memory is used to store instructions; The processor is used to execute the instructions in the memory to perform the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is executed on a computer device, the computer device executes the method according to any one of claims 1 to 6.

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