Method and device for identifying sensitive data of CSV file, equipment and storage medium

Abstract

The invention provides a method, device and equipment for identifying sensitive data of a CSV file and a storage medium, and relates to the technical field of network security, the method comprises the following steps: obtaining data of at least one data column in CSV file data, the data of the data column comprising a column name of the data column and at least one column data; for each target column in the at least one data column, combining and splicing each column data of the target column with the column name of the target column to obtain spliced data corresponding to the target column; matching the spliced data with sensitive data rules in a sensitive rule base to obtain an identification result corresponding to the spliced data; the sensitive data rule is used for identifying sensitive data; and determining whether the spliced data comprises sensitive data or not based on the identification result. According to the method, the efficiency and accuracy of identifying the sensitive data of the CSV file can be improved.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular, to a method, device, equipment and storage medium for identifying sensitive data in a CSV file. Background Art

[0002] With the development of the Internet and the digital economy, the data traffic has increased exponentially, and the usage scenarios have become more and more diverse. The existing data security protection methods can no longer meet the current security requirements, and the core sensitive data identification technology in data security has attracted more and more attention.

[0003] The existing technologies for identifying sensitive data in Comma-Separated Values (CSV) files mainly include string matching methods, regular expression recognition methods, and manual screening methods. However, the string matching method or the regular expression recognition method can only identify a small part of the data with a strong standard format and cannot identify most of the statistical data. The manual screening method is for staff to search for and determine sensitive data based on personal experience. However, in the face of large-capacity and relatively complex data, the traditional method of manual sorting is slow, the recognition efficiency is low, and the same person may have different judgments on the same data at different times, and different people may also have different judgments on the same data, which makes the sensitive data recognition results different and leads to a low accuracy rate of sensitive data recognition.

[0004] It can be seen that there are problems of low efficiency and accuracy in identifying sensitive data in CSV files in the prior art. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a method, device, equipment and storage medium for identifying sensitive data in a CSV file.

[0006] The present invention provides a method for identifying sensitive data in a CSV file, including:

[0007] Obtain the data of at least one data column in the CSV file data, and the data of the data column includes the column name of the data column and at least one column data;

[0008] For each target column in the at least one data column, combine and splice each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column;

[0009] Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data;

[0010] Based on the recognition result, determine whether the spliced data includes sensitive data.

[0011] According to a method for identifying sensitive data in a CSV file provided by the present invention, the obtaining of data of at least one data column in the CSV file data includes:

[0012] Parse the collected network traffic data to obtain the CSV file data;

[0013] According to the CSV file format feature, parse the CSV file data to obtain data of at least one data column included in the CSV file data.

[0014] According to a method for identifying sensitive data in a CSV file provided by the present invention, when there are multiple spliced data corresponding to the target column, the method further includes:

[0015] According to the recognition results corresponding to the multiple spliced data corresponding to the target column, determine the statistical quantity of the spliced data including sensitive data among the multiple spliced data;

[0016] According to the statistical quantity, update the sensitive data rule in the sensitive rule library.

[0017] According to a method for identifying sensitive data in a CSV file provided by the present invention, updating the sensitive data rule in the sensitive rule library according to the statistical quantity includes:

[0018] Obtain the statistical quantity of the spliced data including sensitive data among the multiple spliced data corresponding to the target column;

[0019] When the statistical quantity is greater than the first threshold, determine that all data of the target column are sensitive warning objects; analyze the spliced data that does not include sensitive data among the sensitive warning objects, extract new sensitive data rules; add the new sensitive data rules to the sensitive rule library;

[0020] Or, when the statistical quantity is greater than the second threshold, display the spliced data that does not include sensitive data among the multiple spliced data in the display area; receive the supplementary sensitive data rules input by the user, and add the supplementary sensitive data rules to the sensitive rule library; the second threshold is less than the first threshold; the sensitive data rules are used to identify the spliced data that does not include sensitive data among the multiple spliced data;

[0021] Alternatively, when the statistical quantity is less than a third threshold, analyze the spliced data including sensitive data among the multiple spliced data to obtain a sensitive exclusion rule; add the sensitive exclusion rule to the sensitive rule library; the sensitive exclusion rule is used to identify the spliced data misidentified as sensitive data in the spliced data as non-sensitive data; the third threshold is less than the second threshold.

[0022] According to a method for identifying sensitive data in a CSV file provided by the present invention, the method further includes:

[0023] When the spliced data includes sensitive data, desensitize the sensitive data to obtain desensitized data.

[0024] According to a method for identifying sensitive data in a CSV file provided by the present invention, the combining and splicing each column data of the target column with the column name of the target column to obtain the spliced data corresponding to the target column includes:

[0025] Determine the column name of the target column according to the data of the first data row in the target column;

[0026] Determine the data of other multiple data rows in the target column as each column data of the target column;

[0027] For each column data in each column data of the target column, combine and splice the column data with the column name of the target column to obtain a combined content;

[0028] Convert the combined content into a preset format to obtain the spliced data corresponding to the column data.

[0029] The present invention also provides a device for identifying sensitive data in a CSV file, including:

[0030] An acquisition module, configured to acquire data of at least one data column in the CSV file data, where the data of the data column includes the column name of the data column and at least one column data;

[0031] A splicing module, configured to, for each target column in the at least one data column, combine and splice each column data of the target column with the column name of the target column to obtain the spliced data corresponding to the target column;

[0032] A matching module, configured to match the spliced data with a sensitive data rule in a sensitive rule library to obtain an identification result corresponding to the spliced data; the sensitive data rule is used to identify sensitive data;

[0033] A determination module, configured to determine whether the spliced data includes sensitive data based on the identification result.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for identifying sensitive data in a CSV file as described in any one of the above is implemented.

[0035] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for identifying sensitive data in a CSV file as described in any one of the above is implemented.

[0036] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for identifying sensitive data in a CSV file as described in any one of the above is implemented.

[0037] The method, device, equipment, and storage medium for identifying sensitive data in a CSV file provided by the present invention can improve the efficiency and accuracy of identifying sensitive data in a CSV file by first combining and splicing the column name of a target column and each column data of the target column included in the CSV file data to obtain a plurality of data to be identified, and then identifying the sensitive data included in the data to be identified based on sensitive data rules on this basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention 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 drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 is one of the flow diagrams of the method for identifying sensitive data in a CSV file provided by the present invention;

[0040] Figure 2 is another flow diagram of the method for identifying sensitive data in a CSV file provided by the present invention;

[0041] Figure 3 is the structural diagram of the device for identifying sensitive data in a CSV file provided by the present invention;

[0042] Figure 4 is the entity structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0044] The following will describe Figure 1 and Figure 2 a method, device, equipment, and storage medium for identifying sensitive data in a CSV file according to the present invention.

[0045] Figure 1 is one of the schematic flowcharts of the method for identifying sensitive data in a CSV file provided by the present invention. As Figure 1 shown, the method includes steps 101 - 104, where:

[0046] Step 101: Obtain the data of at least one data column in the CSV file data, where the data of the data column includes the column name of the data column and at least one column of data.

[0047] It should be noted that the method for identifying sensitive data in a CSV file provided by the present invention can be applied to scenarios where sensitive data identification and monitoring are required. The execution subject of this method can be a device for identifying sensitive data in a CSV file or an identification engine, such as an electronic device, a server, or a control module in the device for executing the method for identifying sensitive data in a CSV file provided by the present invention.

[0048] Sensitive data mainly refers to data that may cause serious harm to society or individuals after leakage. For example, sensitive data can include personal privacy data, such as name, ID number, address, phone number, bank account number, email, password, medical information, and educational background, etc.; sensitive data can also include data that is not suitable for disclosure by enterprises or social institutions, such as the business situation of enterprises, the network structure of enterprises, and the list of IP addresses, etc.

[0049] Optionally, the implementation method of obtaining the data of at least one data column in the CSV file data in step 101 above may include steps a and b:

[0050] Step a: Analyze the collected network traffic data to obtain the CSV file data;

[0051] Step b: Analyze the CSV file data according to the CSV file format characteristics to obtain the data of at least one data column included in the CSV file data; where the data of each data column includes a column name and at least one column of data corresponding to the one column name.

[0052] Optionally, the present invention obtains corresponding context basic features by performing context analysis on the data of the CSV file.

[0053] For example, according to the CSV file format features, the data of the CSV file is parsed, and the data features in the CSV file data are extracted and summarized. The content of the first data row or the topmost row in the CSV file data is extracted as the column name, and the content of each cell below the cell where the column name is located in each column is the specific column data. The column name row and the data row are strictly aligned in format.

[0054] As shown in Table 1 for the CSV file, the first row includes multiple column names.

[0055] Table 1

[0056]

[0057] Step 102: For each target column in the at least one data column, the respective column data of the target column are respectively combined and spliced with the column name of the target column to obtain the spliced data corresponding to the target column.

[0058] Optionally, the implementation manner of respectively combining and splicing the respective column data of the target column with the column name of the target column in the above step 102 to obtain one or more spliced data corresponding to the target column may include steps 1) to 4):

[0059] Step 1): Determine the column name of the target column according to the data of the first data row in the target column;

[0060] Step 2): Determine the respective column data of the target column as the data of the other multiple data rows in the target column;

[0061] Step 3): For each column data in the respective column data of the target column, the column data is combined and spliced with the column name of the target column to obtain combined content;

[0062] Step 4): Convert the combined content into a preset format to obtain the spliced data corresponding to the column data.

[0063] Optionally, for each column in the at least one data column included in the CSV file data, such as the target column, the column name of the target column and the respective column data below the column name are extracted, the column name is combined and spliced with a single column data to obtain corresponding combined content, and the combined content is converted into a preset format to obtain the spliced data corresponding to the column data. The preset format is, for example, the form format.

[0064] Optionally, extract the data of the first data row of the target column as the keyword (key) of the data column, then extract the data of the second row corresponding to the target column as the value, and then combine and splice the key and value of the corresponding column to obtain the corresponding combined content.

[0065] For the data of the third row corresponding to the target column, use the data of the third row as the value, and then combine and splice the key and value of the corresponding column to obtain the corresponding combined content. For the data of other rows in the target column, complete the combination and splicing in this way in turn, and store the spliced data.

[0066] Step 103: Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data.

[0067] Step 104: Based on the recognition result, determine whether the spliced data includes sensitive data.

[0068] Optionally, based on the sensitive data rules in the sensitive rule library, identify whether the spliced data includes sensitive data, and also determine whether the spliced data hits the sensitive data rules.

[0069] Optionally, when it is determined based on the recognition result that the spliced data includes sensitive data, desensitize the sensitive data to obtain desensitized data, and retain and record it in the sensitive log.

[0070] By analyzing the data context of the CSV file data, the basic characteristics of the data context are obtained, and based on the data context characteristics, the data is effectively spliced, and the fragmented table data is spliced, and finally the purpose of identifying sensitive information is achieved. The present invention greatly improves the recognition efficiency and accuracy of sensitive data in CSV files.

[0071] Optionally, after performing sensitive data recognition on the multiple spliced data corresponding to the target column, according to the recognition results corresponding to the multiple spliced data corresponding to the target column, determine the statistical quantity of the spliced data including sensitive data among the multiple spliced data; according to the statistical quantity, update the sensitive data rules in the sensitive rule library.

[0072] For example, the implementation manner of updating the sensitive data rules in the sensitive rule library according to the statistical quantity may include the following steps:

[0073] When the statistical quantity is greater than the first threshold, it is determined that the data in the target column are all sensitive alarm objects; analyze the spliced data that does not include sensitive data among the sensitive alarm objects, and extract new sensitive data rules; add the new sensitive data rules to the sensitive rule library;

[0074] Alternatively, when the statistical quantity is greater than the second threshold, display the spliced data that does not include sensitive data among the multiple spliced data in the display area; receive the supplementary sensitive data rules input by the user, and add the supplementary sensitive data rules to the sensitive rule library; the second threshold is less than the first threshold; the sensitive data rules are used to identify the spliced data that does not include sensitive data among the multiple spliced data;

[0075] Alternatively, when the statistical quantity is less than the third threshold, analyze the spliced data that includes sensitive data among the multiple spliced data to obtain sensitive exclusion rules; add the sensitive exclusion rules to the sensitive rule library; the sensitive exclusion rules are used to identify the spliced data that is misidentified as sensitive data in the spliced data as non-sensitive data; the third threshold is less than the second threshold.

[0076] It can be seen that by analyzing the recognition results or sensitive alarm situations of the respective column data corresponding to the target column, summarizing the rules, the function of recognizing unknown sensitive data is realized.

[0077] The method for recognizing sensitive data in a CSV file provided by the present invention, by first combining and splicing the column name of the target column with each column data of the target column for the data included in the CSV file to obtain multiple data to be recognized, and then on this basis, recognizing the sensitive data included in the data to be recognized based on the sensitive data rules, can improve the efficiency and accuracy of recognizing sensitive data in the CSV file.

[0078] Figure 2 is the second flow chart of the method for recognizing sensitive data in a CSV file provided by the present invention, as Figure 2 shown, the method includes steps 201 - step 211, where:

[0079] Step 201, parse the collected network traffic data to obtain the CSV file data.

[0080] Step 202, according to the CSV file format characteristics, parse the CSV file data to obtain the data of at least one data column included in the CSV file data; wherein, the data of each data column includes a column name and at least one column data corresponding to the one column name.

[0081] Step 203: For each target column in the at least one data column, determine the column name of the target column according to the data in the first data row of the target column.

[0082] Step 204: Determine the respective column data of the target column from the data in the other multiple data rows of the target column.

[0083] Step 205: For each column data in the respective column data of the target column, combine and splice the column data with the column name of the target column to obtain combined content.

[0084] Step 206: Convert the combined content into a preset format to obtain the spliced data corresponding to the column data.

[0085] Step 207: Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data.

[0086] Step 208: Based on the recognition result, determine whether the spliced data includes sensitive data.

[0087] Step 209: When the spliced data includes sensitive data, desensitize the sensitive data to obtain desensitized data.

[0088] By extracting the column name and the specific data below, splicing the column name and the specific data into the form format, and sending the spliced data into the detection system for feature matching, through the cooperation of each module, accurate recognition of known sensitive data is achieved, and the purpose of more quickly and accurately identifying sensitive data in the CSV file can be achieved.

[0089] Step 210: Obtain the statistical quantity of the spliced data including sensitive data among the multiple spliced data corresponding to the target column.

[0090] Step 211: When the statistical quantity is greater than the first threshold, determine that all the data in the target column are sensitive warning objects; analyze the spliced data that does not include sensitive data among the sensitive warning objects, and extract new sensitive data rules; add the new sensitive data rules to the sensitive rule library;

[0091] Or, when the statistical quantity is greater than the second threshold, display the spliced data that does not include sensitive data among the multiple spliced data in the display area; receive the supplementary sensitive data rules input by the user; add the supplementary sensitive data rules to the sensitive rule library; the second threshold is less than the first threshold; the sensitive data rules are used to identify the spliced data that does not include sensitive data among the multiple spliced data;

[0092] Alternatively, when the statistical quantity is less than the third threshold, analyze the spliced data including sensitive data among the multiple spliced data to obtain a sensitive exclusion rule; add the sensitive exclusion rule to the sensitive rule library; the sensitive exclusion rule is used to identify the spliced data misidentified as sensitive data in the spliced data as non-sensitive data; the third threshold is less than the second threshold.

[0093] For the identification of sensitive data, the present invention designs to extract specific data in the file according to the rules in the csv file traffic, strictly align the data, extract the data in the first row, i.e., the key row data, and the data in the following data rows, i.e., the value data, splice and combine the key value in each column with each value, combine the fragmented data in the table, send the combined data to the device, and through the cooperation between modules, achieve accurate identification of known sensitive data, analyze the identification results of each column of data, summarize and induce the identification rules for unknown sensitive data, so as to realize the identification function of unknown sensitive data.

[0094] Existing sensitive data identification devices either do not have or use the OCR image text recognition method to implement the identification function of the CSV file content, but the sensitive detection rate of the OCR method is very low and the effect is not good. The present invention can greatly improve the sensitive detection accuracy by analyzing, combining and splicing the specific content of the file to combine the separate fragmented data.

[0095] Existing sensitive data identification devices do not have the function of identifying unknown sensitive data; the present invention combines the analysis of the sensitive detection results of each column of data in the file, and then summarizes and induces the unknown sensitive identification rules to realize the identification function of unknown sensitive data.

[0096] Next, the device for identifying sensitive data in a CSV file provided by the present invention will be described. The device for identifying sensitive data in a CSV file described below can be correspondingly referred to the method for identifying sensitive data in a CSV file described above.

[0097] Figure 3 It is a schematic structural diagram of the device for identifying sensitive data in a CSV file provided by the present invention; the device 300 for identifying sensitive data in a CSV file includes: an acquisition module 301, a second splicing module 302, a matching module 303 and a determination module 304; wherein,

[0098] The acquisition module 301 is used to acquire the data of at least one data column in the CSV file data, and the data of the data column includes the column name of the data column and at least one column data;

[0099] The splicing module 302 is configured to, for each target column in the at least one data column, splice and combine each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column;

[0100] The matching module 303 is configured to match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data;

[0101] The determination module 304 is configured to determine whether the spliced data includes sensitive data based on the recognition result.

[0102] The device for identifying sensitive data in a CSV file provided by the present invention can improve the efficiency and accuracy of identifying sensitive data in a CSV file by first splicing and combining the column name of the target column with each column data of the target column for the target columns included in the CSV file data to obtain a plurality of data to be recognized, and then identifying the sensitive data included in the data to be recognized based on the sensitive data rules.

[0103] Optionally, the obtaining module 301 is specifically configured to:

[0104] Parse the collected network traffic data to obtain the CSV file data;

[0105] Parse the CSV file data according to the CSV file format characteristics to obtain the data of at least one data column included in the CSV file data.

[0106] Optionally, there are multiple spliced data corresponding to the target column; the device 300 for identifying sensitive data in a CSV file further includes:

[0107] An updating module is configured to determine the statistical quantity of the spliced data including sensitive data among the multiple spliced data according to the recognition results corresponding to the multiple spliced data corresponding to the target column; update the sensitive data rules in the sensitive rule library according to the statistical quantity.

[0108] Optionally, the updating module is specifically configured to:

[0109] When the statistical quantity is greater than a first threshold, determine that all the data of the target column are sensitive warning objects; analyze the spliced data that does not include sensitive data among the sensitive warning objects, extract new sensitive data rules; add the new sensitive data rules to the sensitive rule library;

[0110] Alternatively, when the statistical quantity is greater than a second threshold, display the spliced data that does not include sensitive data among the multiple spliced data in the display area; receive the supplementary sensitive data rule input by the user; add the supplementary sensitive data rule to the sensitive rule library; the second threshold is less than the first threshold; the sensitive data rule is used to identify the spliced data that does not include sensitive data among the multiple spliced data;

[0111] Alternatively, when the statistical quantity is less than a third threshold, analyze the spliced data that includes sensitive data among the multiple spliced data to obtain a sensitive exclusion rule; add the sensitive exclusion rule to the sensitive rule library; the sensitive exclusion rule is used to identify the spliced data that is misidentified as sensitive data in the spliced data as non-sensitive data; the third threshold is less than the second threshold.

[0112] Optionally, the device 300 for identifying sensitive data in a CSV file further includes:

[0113] A desensitization module, configured to desensitize the sensitive data when the spliced data includes sensitive data to obtain desensitized data.

[0114] Optionally, the splicing module 302 is specifically configured to:

[0115] Determine the column name of the target column according to the data of the first data row in the target column;

[0116] Determine the respective column data of the target column from the data of the other multiple data rows in the target column;

[0117] For each column data among the respective column data of the target column, combine and splice the column data with the column name of the target column to obtain a combined content;

[0118] Convert the combined content into a preset format to obtain the spliced data corresponding to the column data.

[0119] Figure 4 It is a schematic physical structure diagram of an electronic device provided by the present invention, as Figure 4 shown. The electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the method for identifying sensitive data in a CSV file, and the method includes: obtaining the data of at least one data column in the CSV file data, and the data of the data column includes the column name of the data column and at least one column data;

[0120] For each target column in the at least one data column, combine and splice each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column;

[0121] Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data;

[0122] Based on the recognition result, determine whether the spliced data includes sensitive data.

[0123] In addition, when the logical instructions in the above-mentioned memory 430 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0124] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for identifying sensitive data in a CSV file provided by the above-mentioned various methods. The method includes:

[0125] Obtain the data of at least one data column in the CSV file data. The data of the data column includes the column name of the data column and at least one column data;

[0126] For each target column in the at least one data column, combine and splice each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column;

[0127] Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data;

[0128] Based on the recognition result, determine whether the spliced data includes sensitive data.

[0129] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is used to implement the method for identifying sensitive data in a CSV file provided by the above-mentioned various methods. The method includes:

[0130] Obtain the data of at least one data column in the CSV file data, and the data of the data column includes the column name of the data column and at least one column data;

[0131] For each target column in the at least one data column, respectively combine and splice each column data of the target column with the column name of the target column to obtain the spliced data corresponding to the target column;

[0132] Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data;

[0133] Based on the recognition result, determine whether the spliced data includes sensitive data.

[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0135] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying sensitive data in a CSV file, characterized in that, Including: Obtain data of at least one data column in the CSV file data, where the data of the data column includes the column name of the data column and at least one column data; For each target column in the at least one data column, combine and splice each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column; Match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data; Based on the recognition result, determine whether the spliced data includes sensitive data.

2. The method for identifying sensitive data in a CSV file according to claim 1, wherein The obtaining data of at least one data column in the CSV file data includes: Analyze the collected network traffic data to obtain the CSV file data; According to the CSV file format characteristics, analyze the CSV file data to obtain data of at least one data column included in the CSV file data.

3. The method for identifying sensitive data in a CSV file according to claim 1, wherein When there are multiple spliced data corresponding to the target column, the method further includes: According to the recognition results corresponding to the multiple spliced data corresponding to the target column, determine the statistical quantity of the spliced data including sensitive data among the multiple spliced data; Update the sensitive data rules in the sensitive rule library according to the statistical quantity.

4. The method for identifying sensitive data in a CSV file according to claim 3, wherein Updating the sensitive data rules in the sensitive rule library according to the statistical quantity includes: When the statistical quantity is greater than the first threshold, determine that the data of the target column are all sensitive alarm objects; analyze the spliced data that do not include sensitive data among the sensitive alarm objects, and extract new sensitive data rules; add the new sensitive data rules to the sensitive rule library; Or, when the statistical quantity is greater than the second threshold, display the spliced data that do not include sensitive data among the multiple spliced data in the display area; receive the supplementary sensitive data rules input by the user, and add the supplementary sensitive data rules to the sensitive rule library; the second threshold is less than the first threshold; the sensitive data rules are used to identify the spliced data that do not include sensitive data among the multiple spliced data; Or, when the statistical quantity is less than the third threshold, analyze the spliced data including sensitive data among the multiple spliced data to obtain sensitive exclusion rules; add the sensitive exclusion rules to the sensitive rule library; the sensitive exclusion rules are used to identify the spliced data misidentified as sensitive data in the spliced data as non-sensitive data; the third threshold is less than the second threshold.

5. The method for identifying sensitive data in a CSV file according to claim 1, wherein The method further includes: When the spliced data includes sensitive data, desensitize the sensitive data to obtain desensitized data.

6. The method for identifying sensitive data in a CSV file according to claim 1, characterized in that, The combining and splicing each column data of the target column with the column name of the target column respectively to obtain the spliced data corresponding to the target column includes: Determine the column name of the target column according to the data of the first data row in the target column; Determine the data of the other multiple data rows in the target column as each column data of the target column; For each column data in the respective column data of the target column, combine and splice the column data with the column name of the target column to obtain combined content; Convert the combined content into a preset format to obtain the spliced data corresponding to the column data.

7. An apparatus for identifying sensitive data in a CSV file, characterized in that, It includes: An acquisition module, configured to acquire data of at least one data column in the CSV file data, where the data of the data column includes the column name of the data column and at least one column data; A splicing module, configured to, for each target column in the at least one data column, respectively combine and splice each column data of the target column with the column name of the target column to obtain the spliced data corresponding to the target column; A matching module, configured to match the spliced data with the sensitive data rules in the sensitive rule library to obtain the recognition result corresponding to the spliced data; the sensitive data rules are used to identify sensitive data; A determination module, configured to determine whether the spliced data includes sensitive data based on the recognition result.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for identifying sensitive data in a CSV file according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, it implements the method for identifying sensitive data in a CSV file according to any one of claims 1 to 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 for identifying sensitive data in a CSV file according to any one of claims 1 to 6.

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