A data detection method and apparatus

Abstract

The application discloses a data detection method and device, and relates to the technical field of computers. A specific implementation manner of the method comprises the following steps: obtaining a plurality of categories contained in detection category information and a preset prompt word, and identifying a target category corresponding to to-be-detected data; further determining a target item corresponding to the target category; detecting a sensitive item and sensitive data corresponding to the sensitive item through a preset sensitive condition; and finally generating a detection result. The implementation manner can automatically complete the detection of sensitive data and obtain an accurate detection result, thereby improving the efficiency of data detection.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a data detection method and apparatus. Background Technology

[0002] As businesses rapidly expand, their existing markets can no longer meet the growing demands. Therefore, companies often seek opportunities in other regions. However, regulations and requirements vary significantly between regions. Violating these regulations can negatively impact business development. Therefore, before expanding, it's crucial to review and verify business data according to the specific regulations of each region to identify and correct any non-compliant data.

[0003] Currently, data verification is usually conducted manually by professional auditors according to different regulations, which is a lengthy and inefficient process. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a data detection method and apparatus that can achieve intelligent data detection and improve data detection efficiency.

[0005] To achieve the above objectives, according to one aspect of the present invention, a data detection method is provided, comprising:

[0006] Obtain category information for data detection. The category information includes multiple categories and preset prompt words for each category.

[0007] Based on multiple categories and preset prompts corresponding to each category, the data to be detected is identified to obtain the target category corresponding to the data to be detected.

[0008] Based on the data to be detected, query the target entries among multiple entries corresponding to the target category;

[0009] Sensitive entries are detected from target entries based on preset sensitive conditions;

[0010] Sensitive data is identified in the data to be detected based on sensitive entries, and detection results are generated based on the sensitive entries and sensitive data.

[0011] Optionally, before identifying the target category corresponding to the data to be detected based on multiple categories and preset prompts corresponding to each category, the method further includes:

[0012] By identifying the data to be tested, the corresponding business scenario can be determined.

[0013] Based on the recognition model associated with the business scenario, the data to be detected is detected to obtain business-sensitive data;

[0014] The data to be tested is filtered based on business-sensitive data.

[0015] Optionally, the method further includes:

[0016] Add business-sensitive data to the detection results.

[0017] Optionally, before identifying the business scenario corresponding to the data to be detected, the method further includes:

[0018] The data to be detected is analyzed based on a general sensitive word database to obtain general sensitive data.

[0019] The data to be tested is filtered based on general sensitive data.

[0020] Optionally, the method further includes:

[0021] Add common sensitive data to the detection results.

[0022] Optionally, sensitive data in the data to be detected is identified based on sensitive entries, and detection results are generated based on the sensitive entries and sensitive data, including:

[0023] The sensitive data is processed based on the sensitive data modification rules corresponding to the sensitive entries, and a detection result containing the sensitive data, the sensitive data modification rules, and the processed sensitive data is generated.

[0024] According to another aspect of the present invention, a data detection apparatus is provided, comprising:

[0025] The acquisition module is used to acquire category information for data detection. The category information includes multiple categories and preset prompt words corresponding to each category.

[0026] The recognition module is used to identify the data to be detected based on multiple categories and preset prompts corresponding to each category, and to obtain the target category corresponding to the data to be detected.

[0027] The query module is used to query the target entry among multiple entries corresponding to the target category based on the data to be detected.

[0028] The detection module is used to detect sensitive entries from target entries based on preset sensitive conditions;

[0029] The generation module is used to identify sensitive data in the data to be detected based on sensitive entries, and to generate detection results based on the sensitive entries and sensitive data.

[0030] According to another aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data detection method provided in the embodiments of the present invention.

[0031] According to another aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the data detection method provided in the embodiments of the present invention.

[0032] According to another aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the data detection method provided in the embodiments of the present invention.

[0033] One embodiment of the above invention has the following advantages or beneficial effects: it can determine the target category corresponding to the data to be detected based on the acquired category information, and then determine the corresponding target item, determine the sensitive item based on the preset sensitive conditions, and finally generate the detection result based on the sensitive item and the corresponding sensitive data, thereby realizing intelligent detection of sensitive data based on data category and item, and improving the efficiency and accuracy of data detection.

[0034] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0035] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0036] Figure 1 This is a schematic diagram of the main steps of the data detection method according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the main modules of a data detection device according to an embodiment of the present invention;

[0038] Figure 3 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied;

[0039] Figure 4 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0040] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0041] It should be noted that the technical solutions disclosed in this invention, regarding the collection, updating, analysis, processing, use, transmission, and storage of user personal information, all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0042] Figure 1 This is a schematic diagram of the main steps of the data detection method according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes steps S101 to S105.

[0043] Step S101: Obtain the category information for the data detection performed by the user. The category information includes multiple categories and preset prompt words corresponding to each category.

[0044] The system retrieves all category information maintained by the system for data detection, including multiple categories based on preset prompts for each category. Specifically, the category information can include different categories depending on the business scenario, such as logistics information, merchant information, and user information. Each category has a corresponding preset prompt, used to subsequently determine whether the data to be detected belongs to the corresponding category, thereby identifying the category corresponding to the data to be detected.

[0045] Optionally, the category information maintained by the system is stored according to different regions. Specifically, based on different regions, it can be divided into multiple category sets, each with a corresponding region identifier, used for selection based on the region corresponding to the data to be detected.

[0046] Step S102: Identify the data to be detected based on multiple categories and the preset prompt words corresponding to each category, and obtain the target category corresponding to the data to be detected.

[0047] The large model is invoked, enabling it to identify each data item in the test data based on multiple acquired categories and pre-defined prompts for each category. This process determines all target categories corresponding to the test data, i.e., all target categories involved in the test data. Specifically, the large model needs to identify the category to which each data item belongs based on the pre-defined prompts and then summarize these findings to obtain all target categories corresponding to the test data.

[0048] Optionally, before the large model performs recognition, it is necessary to first select a set of categories based on the identifiers of the regions corresponding to the data to be detected, and then use the large model to perform recognition based on the selected categories. Here, the region corresponding to the data to be detected is the region where the data is to be applied; for example, if the data to be detected is business data, the corresponding region is the region where the business is specifically implemented.

[0049] Step S103: Based on the data to be detected, query the target entries among multiple entries corresponding to the target category.

[0050] After determining the target category of the data to be tested, since each category corresponds to multiple specific entries, it is necessary to further identify the specific entry information involved. Specifically, after obtaining the target category output by the large model, multiple entries under that target category are obtained according to the pre-set binding relationship between the category and the entries. Then, the corresponding target entries are determined based on the specific content of the data in the data to be tested. For example, after determining that the target category is user information, all entries under user information are obtained, including user PIN (personal identification code), name, ID card number, email address, and nickname. In the data to be tested, if the data is "Zhang San", then the corresponding entry is determined to be "name".

[0051] Optionally, after determining the target entry corresponding to each piece of data, a query result of target category - target entry - data format to be detected can be obtained.

[0052] Step S104: Sensitive entries are detected from the target entries based on preset sensitive conditions.

[0053] After obtaining the query results, retrieve the corresponding preset sensitivity conditions. Specifically, these are the preset sensitivity conditions for the target category corresponding to the data to be detected. These preset sensitivity conditions are predefined sensitive item contents; for example, they specify which items are considered sensitive, including names, ID numbers, etc. The preset sensitivity conditions for the target category are the preset sensitivity conditions for all items under that target category.

[0054] Optionally, the obtained preset sensitive conditions can also be the sensitive condition information of the target item, that is, the preset sensitive condition is whether the corresponding item is a sensitive item. Sensitive items can be preset with sensitive indicators, for example, 1 represents a sensitive item and 0 represents a non-sensitive item.

[0055] Step S105: Determine sensitive data in the data to be detected based on sensitive entries, and generate detection results based on sensitive entries and sensitive data.

[0056] After identifying the sensitive entries in the target list, the corresponding sensitive data can be determined, and then the corresponding sensitive categories can be obtained, forming the final detection result data in the format of sensitive category - sensitive entry - sensitive data.

[0057] Optionally, when generating the final detection result data, corresponding sensitive fields may also be included, such as: User Information—Name—name—Zhang San. The specific content of either the field or the sensitive data can be displayed, or both can be displayed.

[0058] The data detection method provided by the present invention can determine the target category corresponding to the data to be detected based on the acquired category information, and then determine the corresponding target item. Sensitive items are determined from the target category based on preset sensitive conditions. Finally, based on the sensitive items, the corresponding sensitive data is used to generate detection results. This realizes intelligent detection of sensitive data based on data category and item, and improves the efficiency and accuracy of data detection.

[0059] Optionally, before identifying the target category corresponding to the data to be detected based on multiple categories and preset prompts corresponding to each category, the method further includes: determining the business scenario corresponding to the data to be detected by identifying the data to be detected; detecting the data to be detected according to the recognition model associated with the business scenario to obtain business-sensitive data; and filtering the data to be detected based on the business-sensitive data. Before identifying the target category corresponding to the data to be detected, a large model can also be used to identify the business scenario based on the data to be detected, in order to determine which small model to select for identifying general sensitive data in the data to be detected. Different business scenarios have different small models bound to them, such as product models, order models, etc.; the large models for identifying different business scenarios are pre-trained with the professional terminology of the corresponding business scenarios. After determining the corresponding small model, the data to be detected is detected according to the business-sensitive data under that business scenario, and after detecting the corresponding business-sensitive data, it is filtered to obtain the data to be detected after removing the business-sensitive data.

[0060] Optionally, business-sensitive data can be general-purpose sensitive data applicable to different business scenarios. For example, in a business expansion scenario, general-purpose sensitive data may include specific algorithm names, etc.

[0061] Optionally, the method also includes adding business-sensitive data to the detection results. After business-sensitive data is detected, it can be added to the detection results and displayed as filtered data to help users identify business-sensitive data and prevent such sensitive data from appearing in subsequent data.

[0062] The data detection method according to embodiments of the present invention can identify business-sensitive data by using a corresponding small model based on the identified business scenario, thereby achieving accurate identification of business-sensitive data.

[0063] Optionally, before identifying the business scenario corresponding to the data to be detected, the method further includes: detecting the data to be detected based on a general sensitive word database to obtain general sensitive data; and filtering the data to be detected based on the general sensitive data. Before identifying business sensitive data, the method also includes calling a small model to identify general sensitive data in the data to be detected. During the identification process, a pre-set general sensitive database is used as the basis for identification to determine the general sensitive data in the data to be detected, and the determined general sensitive data is filtered to obtain the data to be detected with that type of sensitive data removed.

[0064] Optionally, when identifying general sensitive data, for image-type data in the data to be detected, text recognition technology can be used to extract text information from the image. Then, the text information is identified through a general sensitive database to filter out the general sensitive data it contains.

[0065] Optionally, general sensitive data can be added to the detection results. After general sensitive data is detected, it can be added to the detection results for user reference.

[0066] According to the data detection method of the present invention, the method can first identify and filter general sensitive data in the data to be detected, thereby achieving preliminary detection of the data, removing general sensitive data, reducing the amount of data, facilitating subsequent identification of more refined sensitive data, and reducing the amount of data for subsequent sensitive data identification.

[0067] Optionally, before identifying general sensitive data, it is also necessary to extract the data to be detected using natural language recognition technology based on a large model, and generate data in a standard format for subsequent data detection processes.

[0068] Optionally, sensitive data in the data to be detected is identified based on sensitive entries, and detection results are generated based on the sensitive entries and sensitive data. This includes processing the sensitive data according to the sensitive data modification rules corresponding to the sensitive entries, and generating detection results containing the sensitive data, the sensitive data modification rules, and the processed sensitive data. When generating detection results for the data to be detected, modification suggestions can be added to the identified sensitive data. Specifically, for the identified sensitive data, the modification rules corresponding to its respective entries are obtained, and corresponding modification suggestions are generated based on these rules. Different entries have different modification rules based on their compliance level (also known as sensitivity). For example, for the entry "phone number," its compliance level is generally low, and its corresponding modification rule is to retain the first and last four digits and replace the middle digits with specific characters. For the entry "plaintext password," its compliance level is very low, and its corresponding modification rule is to delete this data. The modification suggestions generated based on these rules can be specific modification methods or modified data; the choice is flexible and not limited by the actual situation.

[0069] The data detection method provided by the present invention can generate modification suggestions for sensitive data based on the entries of the sensitive data after the sensitive data is identified, so as to allow users to modify the sensitive data and provide modification reference for modification after data detection.

[0070] Optionally, the generated test results can be output as a PDF (portable file format) report and sent to the manual processing stage for a second manual confirmation process and a second manual review of the test results.

[0071] Optionally, after a second manual review, risk points in the data to be tested, i.e. the most sensitive data, can be marked based on the review results.

[0072] Optionally, the data to be tested is data generated by the system being tested. Specifically, the system being tested can upload the data to be tested through the reporting SDK (Software Development Kit). When the system being tested generates a testing request, it will report it uniformly through a specific interface for cleaning and processing of the data to be tested.

[0073] Optionally, for the system under test that has been found to contain sensitive data, its interface needs to be modified by adjusting its input and output parameters to ensure that it outputs compliant data.

[0074] An example of data detection is as follows: the model input combined with the preset prompt is "You are a rule detection expert with rich experience in compliance review. Here is a piece of data (xxxxxxxxxxxx), please determine whether it contains sensitive data such as yyyyyyy. If yes, please point out the sensitive data; if no, directly return that no sensitive data exists". The corresponding output is "According to the provided data, the following fields may contain sensitive data: nationality — Han."

[0075] Optionally, while generating the detected sensitive data, a corresponding description of the sensitive data will also be generated, for example, violating Article B of the AAAA rule. A corresponding basis for determining the sensitive data can also be generated.

[0076] According to the data detection method provided in the embodiments of the present invention, the large model can identify the target entry corresponding to the to-be-detected data, so as to determine the corresponding target entry, determine the sensitive entry and the sensitive data through the preset sensitive condition, and finally generate the detection result based on the sensitive entry and the sensitive data, realizing the data detection process based on rule categories and entries, and improving the efficiency and accuracy of data detection. At the same time, modification suggestions for the sensitive data are generated, which provides a suggestion basis for the modification of the sensitive data.

[0077] Figure 2 is a schematic diagram of main modules of the data detection apparatus according to the embodiment of the present invention, as Figure 2 shown in the figure, comprising an acquisition module 201, an identification module 202, a query module 203, a detection module 204, and a generation module 205.

[0078] The acquisition module 201 is configured to acquire category information for data detection, where the category information includes a plurality of categories and a preset prompt corresponding to each category;

[0079] The identification module 202 is configured to identify the to-be-detected data based on the plurality of categories and the preset prompt corresponding to each category, so as to obtain the target category corresponding to the to-be-detected data;

[0080] The query module 203 is configured to query the target entry among the plurality of entries corresponding to the target category based on the to-be-detected data;

[0081] The detection module 204 is configured to detect and obtain a sensitive entry from the target entry based on a preset sensitive condition;

[0082] The generation module 205 is configured to determine the sensitive data in the to-be-detected data based on the sensitive entry, and generate a detection result based on the sensitive entry and the sensitive data.

[0083] The data detection device provided in this embodiment of the invention can determine the target category corresponding to the data to be detected based on the acquired category information, and then determine the corresponding target item. It can also determine the sensitive item based on preset sensitive conditions, and finally generate a detection result based on the sensitive item and the corresponding sensitive data. This realizes intelligent detection of sensitive data based on data category and item, and improves the efficiency and accuracy of data detection.

[0084] Optionally, the device further includes: a determining module 206 (not shown in the figure), used to determine the business scenario corresponding to the data to be detected by identifying the data to be detected; a detecting module 204, used to detect the data to be detected according to the identification model associated with the business scenario to obtain business-sensitive data; and a filtering module 207 (not shown in the figure), used to filter the data to be detected based on the business-sensitive data.

[0085] Optionally, the device also includes an adding module 208 (not shown in the figure) for adding business-sensitive data to the detection results.

[0086] Optionally, the detection module 204 is further configured to detect the data to be detected based on a general sensitive word library to obtain general sensitive data; the filtering module 207 is further configured to filter the data to be detected based on the general sensitive data.

[0087] Optionally, module 208 is also used to add general sensitive data to the detection results.

[0088] Optionally, the generation module 205 is also used to process the sensitive data based on the sensitive data modification rules corresponding to the sensitive entries, and generate a detection result containing the sensitive data, the sensitive data modification rules, and the processed sensitive data.

[0089] The data detection device provided in this embodiment of the invention can identify target entries corresponding to the data to be detected based on a large model, thereby determining the corresponding target entries. It also determines sensitive entries and sensitive data through preset sensitive bars, and finally generates detection results based on the sensitive entries and sensitive data. This realizes a data detection process based on rule categories and entries, improving the efficiency and accuracy of data detection. Simultaneously, it generates modification suggestions for sensitive data, providing a basis for modifying the sensitive data.

[0090] Figure 3 An exemplary system architecture 300 is shown that can be applied to the data detection method or data detection apparatus of the present invention.

[0091] like Figure 3As shown, system architecture 300 may include terminal devices 301, 302, and 303, a network 304, and a server 305. Network 304 serves as the medium for providing communication links between terminal devices 301, 302, and 303 and server 305. Network 304 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0092] Users can use terminal devices 301, 302, and 303 to interact with server 305 via network 304 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 301, 302, and 303, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0093] Terminal devices 301, 302, and 303 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0094] Server 305 can be a server that provides various services, such as a backend management server that supports shopping websites browsed by users using terminal devices 301, 302, and 303 (for example only). The backend management server can analyze and process data such as received detection requests, and feed back the processing results (such as detection results - for example only) to the terminal devices.

[0095] It should be noted that the data detection method provided in this embodiment of the invention is generally executed by server 305, and correspondingly, the data detection device is generally set in server 305.

[0096] It should be understood that Figure 3 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0097] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a computer system 400 suitable for implementing terminal devices or servers of the present invention. Figure 4 The terminal device or server shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0098] like Figure 4As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the system 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0099] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0100] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined above in the system of this invention.

[0101] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. 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 thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0103] The units or modules described in the embodiments of the present invention can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including an acquisition module, an identification module, a query module, a detection module, and a generation module. The names of these units or modules do not necessarily limit the specific unit or module itself; for example, an acquisition module can also be described as "a module for acquiring category information used for data detection."

[0104] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0105] Obtain category information for data detection. The category information includes multiple categories and preset prompt words for each category.

[0106] Based on multiple categories and preset prompts corresponding to each category, the data to be detected is identified to obtain the target category corresponding to the data to be detected.

[0107] Based on the data to be detected, query the target entries among multiple entries corresponding to the target category;

[0108] Sensitive entries are detected from target entries based on preset sensitive conditions;

[0109] Sensitive data is identified in the data to be detected based on sensitive entries, and detection results are generated based on the sensitive entries and sensitive data.

[0110] According to the technical solution of the present invention, the target category corresponding to the data to be detected can be determined based on the acquired category information, and then the corresponding target item can be determined. Sensitive items can be determined from the target category based on preset sensitive conditions. Finally, based on the sensitive items, the corresponding sensitive data can be used to generate detection results. This realizes intelligent detection of sensitive data based on data category and item, and improves the efficiency and accuracy of data detection.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data detection method, characterized in that, include: Obtain category information for data detection, the category information including multiple categories and preset prompt words corresponding to each category; Based on the multiple categories and the preset prompt words corresponding to each category, the data to be detected is identified, and the target category corresponding to the data to be detected is obtained. Based on the data to be detected, query the target entries among multiple entries corresponding to the target category; Sensitive entries are detected from the target entries based on preset sensitive conditions; Sensitive data in the data to be detected is determined based on the sensitive entries, and a detection result is generated based on the sensitive entries and the sensitive data.

2. The method according to claim 1, characterized in that, Before identifying the data to be detected based on the multiple categories and the preset prompt words corresponding to each category, and obtaining the target category corresponding to the data to be detected, the method further includes: By identifying the data to be detected, the business scenario corresponding to the data to be detected is determined; The data to be detected is obtained by detecting the data based on the recognition model associated with the business scenario; The data to be detected is filtered based on the business-sensitive data.

3. The method according to claim 2, characterized in that, The method further includes: Add the business-sensitive data to the detection results.

4. The method according to claim 2, characterized in that, Before determining the business scenario corresponding to the data to be detected by identifying the data to be detected, the method further includes: The data to be detected is analyzed using a general sensitive word database to obtain general sensitive data. The data to be detected is filtered based on the general sensitive data.

5. The method according to claim 4, characterized in that, The method further includes: Add the general sensitive data to the detection results.

6. The method according to any one of claims 1-5, characterized in that, The step of determining sensitive data in the data to be detected based on the sensitive entries, and generating a detection result based on the sensitive entries and the sensitive data, includes: The sensitive data is processed based on the sensitive data modification rules corresponding to the sensitive entries, and a detection result containing the sensitive data, the sensitive data modification rules, and the processed sensitive data is generated.

7. A data detection device, characterized in that, include: The acquisition module is used to acquire category information for data detection, the category information including multiple categories and preset prompt words corresponding to each category; The identification module is used to identify the data to be detected based on the multiple categories and the preset prompt words corresponding to each category, and to obtain the target category corresponding to the data to be detected; The query module is used to query the target entry among multiple entries corresponding to the target category based on the data to be detected; The detection module is used to detect sensitive entries from the target entries based on preset sensitive conditions; The generation module is used to determine sensitive data in the data to be detected based on the sensitive entries, and to generate detection results based on the sensitive entries and the sensitive data.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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