A database desensitization method based on data analysis

Abstract

The present invention relates to the field of data desensitization, and in particular to a database desensitization method based on data analysis, comprising: obtaining sensitive fields in a request text corresponding to a request task; if the request task has a reference task whose associated conflict coefficient is greater than a preset associated conflict coefficient, covering the overlapping sensitive fields in the request text corresponding to the current request task according to a preset covering rule; determining the request task state according to a reference value of the number of sensitive fields in the request text corresponding to the request task and a sensitive field distribution coefficient of each text paragraph; determining a task analysis method according to the request task state; performing regional desensitization on each sensitive field in a target paragraph according to a desensitization parameter; determining a desensitization method according to a characteristic state of the request task, the desensitization method comprising desensitizing the sensitive fields by using a regional desensitization method or a conventional desensitization method, and the present invention improves the readability of data after database desensitization is completed and the effectiveness of desensitization.

Description

Technical Field

[0001] The present invention relates to the field of data desensitization, and in particular to a database desensitization method based on data analysis. Background Art

[0002] Data desensitization refers to the data processing of certain sensitive information through specific desensitization rules to achieve reliable protection of sensitive privacy data and prevent sensitive privacy data from being accessed or leaked without authorization. However, the current database desensitization method, because some database data comes from different storage times and storage format requirements, there are often some text data with complex formats. Therefore, when desensitizing the text data in the database, there is little research on the readability analysis of the desensitized text data, resulting in the inability of conventional desensitization methods to effectively meet access requirements. Therefore, how to protect sensitive privacy data while ensuring the readability of the data after data desensitization.

[0003] Chinese patent publication number CN115964745A discloses a method for dynamically desensitizing sensitive information in a database, the method comprising: constructing a regular expression according to data features of sensitive data, and scanning the data in a target database according to the regular expression to determine the sensitive data in the target database, tracking access requests involving sensitive data, and determining whether a renamed field associated with the sensitive data appears during the access process; generating a derivative rule corresponding to the renamed field according to a source data rule of sensitive data associated with the sensitive data; and dynamically desensitizing the access data when the data output by the target database triggers the source data rule or the derivative rule. The invention improves the desensitization effect of sensitive data and effectively desensitizes the renamed data associated with the sensitive data, but the above scheme has the following problems: it fails to determine a targeted data desensitization method according to the different distribution situations of sensitive privacy data in the actual processing process, which leads to poor data readability after desensitization. Summary of the invention

[0004] To this end, the present invention provides a database desensitization method based on data analysis to overcome the problem that the prior art fails to determine a targeted data desensitization method according to the different distribution situations of sensitive privacy data in the actual processing process, which leads to poor data readability after desensitization.

[0005] To achieve the above object, the present invention provides a database desensitization method based on data analysis, comprising:

[0006] Obtain sensitive fields in the request text corresponding to the request task, and detect the correlation conflict coefficient between each reference task and the current request task;

[0007] If the request task has a reference task whose correlation conflict coefficient is greater than the preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rule;

[0008] Determine the request task status according to the reference value of the number of sensitive fields in the request text corresponding to the request task and the sensitive field distribution coefficient of each text paragraph;

[0009] Determine the task analysis method according to the request task status, the task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameter, or determine the desensitization method according to the feature status of the request task;

[0010] Determine the desensitization parameters of the corresponding target paragraphs according to the sensitive field distribution coefficient of each target paragraph, and adjust each desensitization parameter according to the preset security value of the request task;

[0011] Perform regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters;

[0012] The characteristic state is determined according to the proportion of a type of field in the sensitive fields in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task. The desensitization method includes desensitizing the sensitive fields by using a regional desensitization method or a conventional desensitization method.

[0013] Further, a correlation conflict coefficient between each reference task and the current request task is detected, wherein the correlation conflict coefficient is determined according to the number of overlapping sensitive fields in the request text corresponding to each reference task and the current request task;

[0014] The reference task is a request task within a detection cycle before the request time corresponding to the current request task.

[0015] Furthermore, if there is a reference task whose correlation conflict coefficient is greater than a preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are covered according to the preset covering rule.

[0016] Furthermore, the request task status is determined according to a reference value of the number of sensitive fields in the request text corresponding to the request task and a distribution coefficient of sensitive fields in each text paragraph. The request task status includes:

[0017] The first preset request task state in which the sensitive field quantity reference value is greater than the preset quantity reference value, or there is a text paragraph in which the sensitive field distribution coefficient is greater than the preset distribution coefficient;

[0018] The second preset request task state is that the sensitive field quantity reference value is less than or equal to the preset quantity reference value, or the sensitive field distribution coefficient of each text paragraph is less than or equal to the preset distribution coefficient.

[0019] Furthermore, the sensitive field distribution coefficient is determined according to a distribution coefficient analysis method, and the distribution coefficient analysis method includes:

[0020] Analyze the proportion of sensitive fields in each paragraph of the request text corresponding to the request task;

[0021] Determine the sensitive field distribution coefficient of the corresponding text paragraph according to the sensitive field ratio of each text paragraph and the sensitive field interval reference value;

[0022] The sensitive field distribution coefficient is determined according to formula (1):

[0023] X=V×ω V +E×ω E (1)

[0024] In formula (1), V is the percentage of sensitive fields in each text paragraph, E is the reference value of the sensitive field interval in each text paragraph, and ω V is the weight coefficient of the sensitive field ratio, ω E The weight coefficient of the reference value of the sensitive field interval.

[0025] Furthermore, a task analysis method is determined according to the requested task status. The task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters, or determining whether to use regional desensitization or conventional desensitization to desensitize sensitive fields according to the characteristic status of the requested task.

[0026] Furthermore, in the first preset request task state, a text paragraph whose sensitive field distribution coefficient is greater than a preset distribution coefficient is recorded as a target paragraph, and regional desensitization is performed on each sensitive field in the target paragraph according to the desensitization parameter, and conventional desensitization is performed on each sensitive field in the text paragraph other than the target paragraph;

[0027] The desensitization parameter is determined according to the sensitive field distribution coefficient of each target paragraph, and the value of the desensitization parameter is negatively correlated with the sensitive field distribution coefficient of each target paragraph.

[0028] Further, the desensitization parameters are increased and adjusted according to the preset safety value of the requested task;

[0029] The increase value of the desensitization parameter is negatively correlated with the preset safety value of the requested task.

[0030] Further, in the second preset request task state, the characteristic state is determined according to the proportion of a type of field in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task, and the desensitization method includes desensitizing the sensitive field by adopting a regional desensitization method, or desensitizing the sensitive field by adopting a conventional desensitization method;

[0031] The characteristic state includes a first-class characteristic state and a second-class characteristic state;

[0032] A characteristic state is that the proportion of a category of fields is greater than the preset proportion of a category of fields;

[0033] The second-category feature status is that the proportion of the first-category fields is less than or equal to the preset proportion of the first-category fields.

[0034] Furthermore, when the request text is in the first characteristic state, the conventional desensitization method is used to desensitize the sensitive fields; when the request text is in the second characteristic state, the regional desensitization method is used to desensitize the sensitive fields;

[0035] The desensitization parameter of the regional desensitization method is positively correlated with the proportion of a type of fields.

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention determines the request task status according to the reference value of the number of sensitive fields of the request text corresponding to the request task in the actual processing process and the sensitive field distribution coefficient of each text paragraph, and determines the task analysis method according to different request task states, so that the data desensitization method is more in line with the sensitive field distribution of the request text corresponding to the request task. The present invention improves the readability of the data after the database desensitization is completed.

[0037] Furthermore, the present invention determines the correlation conflict coefficient between each reference task and the current request task according to the overlap degree of sensitive fields in the request text corresponding to the current request task and each reference task; if there is a request task whose correlation conflict coefficient of the reference task is greater than a preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rules, thereby avoiding data desensitization failure caused by different desensitization processing methods for the overlapping sensitive fields. The present invention improves the effectiveness of database desensitization.

[0038] Furthermore, the present invention determines the sensitive field distribution coefficient of the corresponding text paragraph according to the proportion of sensitive fields in each text paragraph and the reference value of the sensitive field interval, and characterizes the distribution of sensitive fields in the text paragraph by the sensitive field distribution coefficient, thereby determining the status of the request task, thereby avoiding the readability of the request text corresponding to the request task affected by the excessive proportion of desensitized areas in the text paragraph. The present invention improves the readability of the data after the database is desensitized.

[0039] Furthermore, the present invention determines the desensitization parameters of the corresponding text paragraphs according to the sensitive field distribution coefficient of each text paragraph, and performs secondary adjustment on the desensitization parameters according to the preset security value of the request task, which not only avoids the impact of data desensitization on the readability of the request text corresponding to the request task, but also ensures the desensitization effect of sensitive privacy data to avoid repeated data processing. The present invention improves the readability of the data after the database desensitization is completed, the effectiveness of desensitization and the efficiency of desensitization processing.

[0040] Furthermore, the characteristic state is determined according to the proportion of a type of fields in sensitive fields in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task. Different desensitization methods are selected for different proportions of a type of fields in the request text, so that the desensitization process takes into account both the effectiveness of database desensitization and the readability of the data after database desensitization is completed.

[0041] Furthermore, the desensitization method used in the present invention is to mask sensitive fields, but the masking range is different. This desensitization method is more efficient when performing data desensitization processing, and the present invention overcomes the problem of poor readability of desensitized data. The present invention improves the processing efficiency of database desensitization and the readability of data after database desensitization is completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of a database desensitization method based on data analysis of the present invention;

[0043] Figure 2 A flowchart of the present invention for determining a task analysis method according to a requested task status;

[0044] Figure 3 This is a flow chart of the present invention for determining a feature state according to a proportion of a type of field in a request text corresponding to a request task;

[0045] Figure 4 The present invention is a flow chart of determining a desensitization method according to a characteristic state of a requested task. DETAILED DESCRIPTION

[0046] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0048] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figures 1 to 4 As shown, the present invention provides a database desensitization method based on data analysis, comprising:

[0051] Obtain sensitive fields in the request text corresponding to the request task, and detect the correlation conflict coefficient between each reference task and the current request task;

[0052] If the request task has a reference task whose correlation conflict coefficient is greater than the preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rule;

[0053] Determine the request task status according to the reference value of the number of sensitive fields in the request text corresponding to the request task and the sensitive field distribution coefficient of each text paragraph;

[0054] Determine the task analysis method according to the request task status, the task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameter, or determine the desensitization method according to the feature status of the request task;

[0055] Determine the desensitization parameters of the corresponding target paragraphs according to the sensitive field distribution coefficient of each target paragraph, and adjust each desensitization parameter according to the preset security value of the request task;

[0056] Perform regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters;

[0057] The characteristic state is determined according to the proportion of a type of field in the sensitive fields in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task. The desensitization method includes desensitizing the sensitive fields by using a regional desensitization method or a conventional desensitization method.

[0058] Among them, the request task in the present invention is the execution content corresponding to sending an access request to the database, the request text corresponding to the request task in the present invention is the storage text stored in the to-be-processed database that the request task needs to access, the to-be-processed database is the database that receives the access request of the request task, and the data stored in the database are all data that have not been desensitized. The request text is divided according to a preset number of characters, and the preset number of characters is set by the user according to actual needs. How to divide the request text is easy for technicians in this field to understand, and will not be elaborated here. In the present invention, the request text corresponding to the request task is compared with the sensitive field database to screen out the sensitive fields in the request text corresponding to the request task, and the user of the sensitive field database can adaptively set the type or content of the sensitive fields included according to actual needs. The sensitive field contains at least one sensitive character, and the sensitive characters include but are not limited to text, numbers, letters and symbols. The sensitive fields in the present invention include one type of field and two types of fields. The one type of field is the key information that can directly determine the inherent unique attribute of the information subject or the user's self-set, including but not limited to name, ID number, passport number, bank account number, and the second type of field is all sensitive fields except the one type of field, including but not limited to age, gender, date of birth, and region. The regional desensitization method is to use preset symbols to mask the masking range of the sensitive field, and the masking range is determined according to the desensitization parameters and the user can set the required masking area by himself. The conventional desensitization method is to use preset symbols to mask the entire area of ​​the sensitive field. The preset symbols used for masking in the two desensitization methods can be set by the user.

[0059] Specifically, the correlation conflict coefficient between each reference task and the current request task is detected, and the correlation conflict coefficient is determined according to the number of overlapping sensitive fields in the request text corresponding to each reference task and the current request task;

[0060] The reference task is a request task within a detection cycle before the request time corresponding to the current request task.

[0061] The correlation conflict coefficient = the number of overlapping sensitive fields in the request texts corresponding to each reference task / the number of sensitive fields in the request texts corresponding to each reference task, where the overlapping sensitive fields are completely consistent sensitive fields in the request texts corresponding to the reference task and the current request task;

[0062] The duration of the detection cycle can be set by the user according to actual needs and historical records. The higher the user's confidentiality requirements for the requested task, the longer the detection cycle duration. A method for determining the value of the detection cycle duration is provided, and the average value of the detection cycle durations in the historical records that meet the user's confidentiality requirements for sensitive fields in the request text corresponding to the requested task is set as the detection cycle duration.

[0063] Specifically, if there is a reference task whose correlation conflict coefficient is greater than a preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rule.

[0064] Among them, the user can set the preset association conflict coefficient according to actual needs and historical records. The higher the user's confidentiality requirements for the requested task, the smaller the preset association conflict coefficient. A method for determining the value of the preset association conflict coefficient is provided, and the maximum value of the association conflict coefficient in the historical records that meet the user's confidentiality requirements for the requested task and do not cover the overlapping sensitive fields is recorded as the preset association conflict coefficient;

[0065] The preset coverage rules include:

[0066] Select a virtual sensitive field of the same type as the overlapping sensitive field according to the virtual sensitive database to cover the original overlapping sensitive field;

[0067] The virtual sensitive database is set by the user according to the sensitive field type in the actual database, and each type in the virtual sensitive database contains a number of virtual sensitive fields.

[0068] Specifically, the request task status is determined according to the reference value of the number of sensitive fields in the request text corresponding to the request task and the sensitive field distribution coefficient of each text paragraph. The request task status includes:

[0069] The first preset request task state in which the sensitive field quantity reference value is greater than the preset quantity reference value, or there is a text paragraph in which the sensitive field distribution coefficient is greater than the preset distribution coefficient;

[0070] The second preset request task state is that the sensitive field quantity reference value is less than or equal to the preset quantity reference value, or the sensitive field distribution coefficient of each text paragraph is less than or equal to the preset distribution coefficient.

[0071] Specifically, the sensitive field distribution coefficient is determined according to a distribution coefficient analysis method, and the distribution coefficient analysis method includes:

[0072] Analyze the proportion of sensitive fields in each text paragraph in the request text corresponding to the request task;

[0073] Determine the sensitive field distribution coefficient of the corresponding text paragraph according to the sensitive field ratio of each text paragraph and the sensitive field interval reference value;

[0074] The sensitive field distribution coefficient is determined according to formula (1):

[0075] X=V×ω V +E×ω E (1)

[0076] In formula (1), V is the percentage of sensitive fields in each text paragraph, E is the reference value of the sensitive field interval in each text paragraph, and ω V is the weight coefficient of the sensitive field ratio, ω E The weight coefficient of the reference value of the sensitive field interval.

[0077] Among them, the reference value of the number of sensitive fields is the number of sensitive fields in the request text, the proportion of sensitive fields in each text paragraph = the total number of characters in the sensitive field / the total number of characters in the corresponding text paragraph, and the reference value of the sensitive field interval is the average of the number of characters between adjacent sensitive fields in each text paragraph. The user can set the preset number reference value and the preset distribution coefficient according to the actual situation and historical records. The higher the user's readability requirement for the request text corresponding to the request task after desensitization is completed, the smaller the preset number reference value and the smaller the preset distribution coefficient. A method for taking the preset number reference value is provided, and the historical records that meet the user's readability requirements for the request text corresponding to the request task after desensitization and use a conventional desensitization method are recorded as reference records, and the maximum value of the sensitive field number reference value in the reference record is recorded as the preset number reference value. A method for taking the preset distribution coefficient is provided, and the maximum value of the sensitive field distribution coefficient of the text paragraph in the reference record is set as the preset distribution coefficient;

[0078] The weight coefficient of the sensitive field ratio and the sensitive field interval reference value can be set by the user according to actual needs and historical records. A value of the weight coefficient of the sensitive field ratio and the sensitive field interval reference value is provided. The value of the weight coefficient of the sensitive field ratio is 50%, and the value of the weight coefficient of the sensitive field interval reference value is 50%.

[0079] Specifically, the task analysis method is determined according to the request task status. The task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters, or determining whether to use regional desensitization or conventional desensitization to desensitize sensitive fields according to the feature status of the request task.

[0080] Specifically, in the first preset request task state, the text paragraph whose sensitive field distribution coefficient is greater than the preset distribution coefficient is recorded as the target paragraph, and regional desensitization is performed on each sensitive field in the target paragraph according to the desensitization parameter, and conventional desensitization is performed on each sensitive field in the text paragraph other than the target paragraph;

[0081] The desensitization parameter is determined according to the sensitive field distribution coefficient of each target paragraph, and the value of the desensitization parameter is negatively correlated with the sensitive field distribution coefficient of each target paragraph.

[0082] Specifically, the desensitization parameter is increased and adjusted according to the preset safety value of the requested task;

[0083] The increase value of the desensitization parameter is negatively correlated with the preset safety value of the requested task.

[0084] The preset security value is used to characterize the confidentiality level of the requested task. The preset security value of each requested task is the sum of the initial security value and the weighted security value of each requested task. The initial security value of each requested task is equal, and the weighted security value is equal. ui is the number of sensitive fields that exceeds the preset number of sensitive fields for the i-th dangerous task, u0 is the preset number of sensitive fields, the dangerous task is a request task with a number of sensitive fields greater than the preset number of sensitive fields, and the weighted safety value of a request task with a number of sensitive fields less than or equal to the preset number of sensitive fields is 0;

[0085] The user can set the preset number of sensitive fields according to actual needs and historical records. The higher the user's requirements for the desensitization level of the requested task, the smaller the preset number of sensitive fields. A method for determining the preset number of sensitive fields is provided, and the average value of the number of sensitive fields in the historical records that meet the user's requirements for the desensitization level of the requested task is recorded as the preset number of sensitive fields.

[0086] Specifically, in the second preset request task state, the characteristic state is determined according to the proportion of a type of field in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task, and the desensitization method includes desensitizing sensitive fields by adopting a regional desensitization method, or desensitizing sensitive fields by adopting a conventional desensitization method;

[0087] The characteristic state includes a first-class characteristic state and a second-class characteristic state;

[0088] A characteristic state is that the proportion of a category of fields is greater than the preset proportion of a category of fields;

[0089] The second-category feature status is that the proportion of the first-category fields is less than or equal to the preset proportion of the first-category fields.

[0090] Among them, the proportion of one type of fields = (the number of one type of fields in the request text corresponding to the request task / the number of sensitive fields in the request text corresponding to the request task) × 100%. The value of the proportion of one type of fields is preset, and the user can set it according to actual needs and historical records. The higher the user's confidentiality requirements for the request text, the smaller the value of the preset proportion of one type of fields. A method for determining the value of the preset proportion of one type of fields is provided, and the minimum value of the proportion of one type of fields in the historical records that meet the user's confidentiality requirements for the request text and use a conventional desensitizing method is recorded as the preset proportion of one type of fields. A value of the proportion of one type of fields is provided, and the value of the proportion of one type of fields is 40%.

[0091] Specifically, when the request text is in the first-category feature state, the conventional desensitization method is used to desensitize the sensitive fields; when the request text is in the second-category feature state, the regional desensitization method is used to desensitize the sensitive fields;

[0092] The desensitization parameter of the regional desensitization method is positively correlated with the proportion of a type of fields.

[0093] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A database desensitization method based on data analysis, characterized in that: include: Obtain sensitive fields in the request text corresponding to the request task, and detect the correlation conflict coefficient between each reference task and the current request task; If the request task has a reference task whose correlation conflict coefficient is greater than the preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rule; Determine the request task status according to the reference value of the number of sensitive fields in the request text corresponding to the request task and the sensitive field distribution coefficient of each text paragraph; Determine the task analysis method according to the request task status, the task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameter, or determine the desensitization method according to the characteristic status of the request task; Determine the desensitization parameters of the corresponding target paragraphs according to the sensitive field distribution coefficient of each target paragraph, and adjust each desensitization parameter according to the preset security value of the request task; Perform regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters; Determine the characteristic status according to the proportion of a type of field in the sensitive fields in the request text corresponding to the request task, and determine the desensitization method according to the characteristic status of the request task. The desensitization method includes desensitizing the sensitive fields by using a regional desensitization method or a conventional desensitization method. The association conflict coefficient is determined according to the number of overlapping sensitive fields in the request texts corresponding to each reference task and the current request task, wherein the association conflict coefficient=the number of overlapping sensitive fields in the request texts corresponding to each reference task / the number of sensitive fields in the request texts corresponding to each reference task, where the overlapping sensitive fields are completely consistent sensitive fields in the request texts corresponding to the reference task and the current request task; The sensitive field distribution coefficient is determined according to a distribution coefficient analysis method, and the distribution coefficient analysis method includes: Analyze the proportion of sensitive fields in each paragraph of the request text corresponding to the request task; Determine the sensitive field distribution coefficient of the corresponding text paragraph according to the sensitive field ratio of each text paragraph and the sensitive field interval reference value; The sensitive field distribution coefficient is determined according to formula (1): ， In formula (1), V is the percentage of sensitive fields in each text paragraph, and E is the reference value of the sensitive field interval in each text paragraph. is the weight coefficient of the sensitive field ratio, is the weight coefficient of the reference value of the sensitive field interval; The preset security value is used to characterize the confidentiality level of the requested task. The preset security value of each requested task is the sum of the initial security value and the weighted security value of each requested task. The initial security value of each requested task is equal, and the weighted security value , ui is the number of sensitive fields that the i-th dangerous task exceeds the preset number of sensitive fields, u0 is the preset number of sensitive fields, the dangerous task is a request task with a number of sensitive fields greater than the preset number of sensitive fields, and the weighted safety value of the request task with a number of sensitive fields less than or equal to the preset number of sensitive fields is 0; One type of field is the key information that can directly determine the inherent unique attributes of the information subject or set by the user. One type of field includes: name, ID number, passport number and bank account.

2. The database desensitization method based on data analysis according to claim 1 is characterized in that: If there is a reference task whose correlation conflict coefficient is greater than the preset correlation conflict coefficient, the overlapping sensitive fields in the request text corresponding to the current request task are overwritten according to the preset overwriting rule.

3. The database desensitization method based on data analysis according to claim 2 is characterized in that: The request task status is determined based on the reference value of the number of sensitive fields in the request text corresponding to the request task and the sensitive field distribution coefficient of each text paragraph. The request task status includes: The first preset request task state in which the sensitive field quantity reference value is greater than the preset quantity reference value, or there is a text paragraph in which the sensitive field distribution coefficient is greater than the preset distribution coefficient; The second preset request task state is that the sensitive field quantity reference value is less than or equal to the preset quantity reference value, or the sensitive field distribution coefficient of each text paragraph is less than or equal to the preset distribution coefficient.

4. The database desensitization method based on data analysis according to claim 3 is characterized in that: The task analysis method is determined according to the request task status. The task analysis method includes performing regional desensitization on each sensitive field in the target paragraph according to the desensitization parameters, or determining to use regional desensitization method or conventional desensitization method to desensitize the sensitive fields according to the feature status of the request task.

5. The database desensitization method based on data analysis according to claim 4 is characterized in that: In the first preset request task state, a text paragraph whose sensitive field distribution coefficient is greater than a preset distribution coefficient is recorded as a target paragraph, and regional desensitization is performed on each sensitive field in the target paragraph according to the desensitization parameter, and conventional desensitization is performed on each sensitive field in the text paragraph other than the target paragraph; The desensitization parameter is determined according to the sensitive field distribution coefficient of each target paragraph, and the value of the desensitization parameter is negatively correlated with the sensitive field distribution coefficient of each target paragraph.

6. The database desensitization method based on data analysis according to claim 5 is characterized in that: Increase and adjust each desensitization parameter according to the preset safety value of the requested task; The increase value of the desensitization parameter is negatively correlated with the preset safety value of the requested task.

7. The database desensitization method based on data analysis according to claim 6 is characterized in that: In the second preset request task state, the characteristic state is determined according to the proportion of a type of field in the request text corresponding to the request task, and the desensitization method is determined according to the characteristic state of the request task. The desensitization method includes desensitizing sensitive fields by adopting a regional desensitization method, or desensitizing sensitive fields by adopting a conventional desensitization method; The characteristic state includes a first-class characteristic state and a second-class characteristic state; A characteristic state is that the proportion of a category of fields is greater than the preset proportion of a category of fields; The second-category feature status is that the proportion of the first-category fields is less than or equal to the preset proportion of the first-category fields.

8. The database desensitization method based on data analysis according to claim 7 is characterized in that: When the request text is in the first-category feature state, the conventional desensitization method is used to desensitize the sensitive fields. When the request text is in the second-category feature state, the regional desensitization method is used to desensitize the sensitive fields. The desensitization parameter of the regional desensitization method is positively correlated with the proportion of a type of fields.

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