Pre-data desensitization display method based on data format by utilizing browser

Abstract

The invention discloses a preposed data desensitization display method based on a data format by utilizing a browser. The preposed data desensitization display method comprises the following steps: generating a query abnormal coefficient according to account login information and user operation information acquired by utilizing the browser; querying according to the query information to obtain to-be-displayed enterprise data; generating a desensitization rule according to the query abnormal coefficient and the to-be-displayed enterprise data; desensitizing the to-be-displayed enterprise data according to the desensitization rule to obtain display data; and displaying the display data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method for desensitizing and displaying pre-placed data based on data format using a browser. Background Art

[0002] With the continuous development of informatization, the requirements for network security are getting higher and higher, and the requirements for confidentiality of user data are also getting higher and higher. During the application process, most business data, such as name, mobile phone number, ID card and other information, are required to be displayed in an anonymized manner.

[0003] Prior art (an invention patent with announcement number CN112000727B) discloses a method for dynamically configuring business data desensitization display, by configuring business interface desensitization rules and user desensitization permissions in the system, and the desensitization rules are set for secondary processing corresponding to the user desensitization permissions, and the user logs in normally to query the business data, and the interface obtains the business data set and successively judges the attributes of the data set itself, the desensitization permissions of the current user, and the desensitization rules, and finally corresponds and processes the desensitization rules with the business data set if necessary, and outputs the data returned by the aforementioned operation. In the present invention, when authorized users access business data, desensitization is not performed, and when unauthorized users access, desensitization operations are performed according to the configuration, and then when the business interface desensitization content requirements change, configuration can be performed for these changes, reducing the amount of program development caused by changes in business data desensitization objects, accelerating the response speed to business needs, and also reducing the cost loss of the enterprise.

[0004] The above-mentioned data desensitization method sets different desensitization methods by setting different role permissions for different user roles and user levels. However, in daily work, there are account borrowing or account theft, which may lead to information leakage. For example, when borrowing an account, the owner of the borrowed account only wants the person who borrows the account to view part of the account permissions. Since the data desensitization rules are set according to the user's level, all information of the corresponding account can be viewed by the borrower. This will easily cause information leakage during the borrowing process, which in turn leads to low security of enterprise data management. Therefore, a pre-data desensitization method based on the data format using a browser is needed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for desensitizing and displaying pre-placed data based on data format using a browser.

[0006] According to one aspect of the present invention, a method for desensitizing and displaying pre-placed data based on data format using a browser is provided, comprising:

[0007] Generate a query anomaly coefficient based on the account login information and user operation information obtained through the browser;

[0008] Perform a query based on the query information to obtain the enterprise data to be displayed;

[0009] Generate desensitization rules based on the query anomaly coefficient and the enterprise data to be displayed;

[0010] Desensitize the enterprise data to be displayed according to the desensitization rules and obtain the display data;

[0011] Display the display data.

[0012] Optionally, a query abnormality coefficient is generated according to the account login information and user operation information obtained by using the browser, including:

[0013] Extract the current login address and account input information from the account login information;

[0014] Generate a first preceding anomaly coefficient QZ1 according to the current login address;

[0015] Generate a second pre-existing anomaly coefficient QZ2 according to the account input information;

[0016] Extracting query text information and character operation information from user operation information;

[0017] Generate a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information;

[0018] The query anomaly coefficient is calculated according to the first pre-anomaly coefficient, the second pre-anomaly coefficient and the third pre-anomaly coefficient, wherein the calculation formula of the query anomaly coefficient CY is:

[0019] CY=α1×QZ1+α2×QZ2+α3×QZ3, where α1, α2 and α3 are weight coefficients.

[0020] Optionally, generating a first preceding anomaly coefficient according to the current login address includes:

[0021] Get the login count ZDC of the current login address and the total login count XDC of all login addresses;

[0022] According to the number of logins ZDC and the total number of logins XDC, the first preceding anomaly coefficient is calculated, wherein the calculation formula of the first preceding anomaly coefficient is:

[0023] QZ1=exp(-β1×XDC / ZDC), where β1 is the adjustment parameter.

[0024] Optionally, generating a second pre-existing abnormality coefficient QZ2 according to the account input information includes:

[0025] Extracting the interval time between each input character and adjacent input characters in the account input information, and dividing adjacent input characters whose interval time is less than an interval threshold into the same continuous character group; dividing each character in the account input information into a plurality of continuous character groups in turn, and integrating the plurality of continuous character groups into a first character group;

[0026] Obtain the same number TG of historical normal character groups that are the same as the first character group and the total number ZG of historical normal character groups;

[0027] According to the same number TG and the total number of historical normal character groups ZG, the second preceding abnormal coefficient QZ2 is calculated, wherein the calculation formula of the second preceding abnormal coefficient is:

[0028] QZ2=exp(-β2×TG / ZG), where β2 is the adjustment parameter.

[0029] Optionally, generating a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information includes:

[0030] Perform word segmentation operation on the query text information to obtain several query word segments;

[0031] Extracting input character information corresponding to each query word in the character operation information;

[0032] Obtaining the interval time between each input character and an adjacent input character in the input character information, and dividing the adjacent input characters whose interval time is less than the interval threshold into the same continuous character group; dividing each character in the account input information into a plurality of continuous character groups in turn; and integrating the plurality of continuous character groups into a second character group;

[0033] According to the obtained several historical query segmentations and their corresponding historical character information, the historical query segmentations identical to the query segmentations are obtained and marked as the first-level comparison segmentations of the query segmentations; the first-level comparison segmentations whose historical character information is identical to the input character information of the corresponding query segmentations are marked as second-level comparison segmentations; and the number of the first-level comparison segmentations is marked as YBCi, and the number of the second-level comparison segmentations is marked as EBCi; wherein i is the number of the query segmentations;

[0034] Obtain the historical character information corresponding to each secondary comparison segmentation word, and generate the historical character group corresponding to the secondary comparison segmentation word according to the historical character information; obtain the number of secondary comparison segmentations in the historical character group that are the same as the second character group and mark them as TBCi;

[0035] The third preceding anomaly coefficient QZ3 corresponding to the query text information and character operation information is calculated by the formula QZ3 = ∑[γ1×exp(-β3×EBCi / YBCi)+γ2×exp(-β4×TBCi / EBCi)] / N; wherein β3 and β4 are adjustment parameters; γ1 and γ2 are weight coefficients; i = 1, 2,…, N; N is the total number of query words.

[0036] Optionally, a desensitization rule is generated according to the query abnormality coefficient and the enterprise data to be displayed, including:

[0037] Step 1: Determine whether the query anomaly coefficient is lower than the anomaly coefficient threshold according to the query anomaly coefficient; if yes, set the desensitization rule as the default desensitization rule; if no, proceed to step 2;

[0038] Step 2: Get the current license query information and determine whether the current license query information is empty. If so, use the default license information as the current license query information and proceed to step 3; otherwise, proceed directly to step 3;

[0039] Step 3: Extract the license level and license classification in the current license query information, and input the enterprise data to be displayed into the data classification model to obtain its corresponding data level and data classification, wherein the data classification model is obtained through artificial intelligence model training;

[0040] Step 4: Determine whether the data level is less than the license level and whether the data classification belongs to the license classification; if so, set the desensitization rule as the default desensitization rule; if not, set the level desensitization rule corresponding to the data level as the desensitization rule.

[0041] Optionally, the data classification model is obtained by training an artificial intelligence model, including:

[0042] Obtaining certain enterprise data and corresponding data classifications and data levels;

[0043] Integrate a number of enterprise data, data classifications and data levels into a number of training data and test data;

[0044] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data to generate a data classification model whose input is enterprise data and outputs the corresponding data classification and data level, wherein the artificial intelligence model includes BP neural network and RBF neural network.

[0045] Optionally, desensitizing the enterprise data to be displayed according to the desensitization rule, and obtaining the display data, including:

[0046] Step 1: Determine whether the desensitization rule is the default desensitization rule; if yes, proceed to step 2; if no, proceed to step 3;

[0047] Step 2: Extract the queryable level and queryable category in the account login information; obtain the data level and data category corresponding to the enterprise data to be displayed; determine whether the data level is less than the queryable level and whether the data category belongs to the queryable category; if yes, do not perform data desensitization on the enterprise data to be displayed; if no, proceed to step 3;

[0048] Step 3: According to the data level corresponding to the enterprise data to be displayed, query the corresponding desensitization method in the desensitization method query table, desensitize the enterprise data to be displayed according to the desensitization method, and obtain the display data.

[0049] Optionally, the steps for constructing the desensitization method query table are as follows:

[0050] Obtain the average safety score AP and the average efficiency score GP of each desensitization method from several experts;

[0051] The comprehensive score ZP of the corresponding desensitization method is calculated based on the average safety score AP and the average efficiency score GP. The calculation formula for the comprehensive score of each desensitization method is:

[0052] ZP=a1×AP+a2×GP, where a1 and a2 are weight coefficients; and a1=b×exp(ZSD-SD), a2=1-a1; ZSD is the maximum data level, SD is the data level; b is the adjustment coefficient, and 0 <b<1;

[0053] Obtain the comprehensive score values ​​of several desensitization methods corresponding to each data level, obtain each data level and the desensitization method with the largest comprehensive score in turn, establish a mapping relationship between the desensitization method with the largest comprehensive score and the corresponding data level, and construct a desensitization method query table.

[0054] According to another aspect of the present invention, there is provided a device for desensitizing and displaying pre-placed data based on data format using a browser, comprising:

[0055] A first generating module, used to generate a query abnormality coefficient according to the account login information and user operation information obtained by using a browser;

[0056] A query module, used to query based on query information and obtain enterprise data to be displayed;

[0057] The second generation module is used to generate desensitization rules according to the query abnormality coefficient and the enterprise data to be displayed;

[0058] A desensitization module is used to desensitize the enterprise data to be displayed according to the desensitization rules and obtain the display data;

[0059] The display module is used to display the display data.

[0060] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0061] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0062] Therefore, the present application obtains account login information and user operation information; generates a query anomaly coefficient based on the account login information and user operation information; obtains query information, performs query based on the query information, and obtains enterprise data to be displayed; generates desensitization rules based on the query anomaly coefficient; desensitizes the enterprise data to be displayed based on the desensitization rules to obtain display data; displays the display data; determines whether the account has an abnormality based on the account login information and user operation information of the user before querying the information in the past. If an abnormality occurs, it means that the account may be used by others. At this time, it is necessary to perform corresponding desensitization operations on the data to be displayed, that is, desensitize the enterprise data to be displayed based on the abnormal results; desensitize the enterprise data accordingly before displaying; and improves the security of the enterprise data during the query process. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0064] Figure 1 It is a flowchart of a method for desensitizing and displaying pre-placed data based on data format using a browser provided by an exemplary embodiment of the present invention;

[0065] Figure 2 It is a structural schematic diagram of a pre-data desensitization display device based on data format using a browser provided by an exemplary embodiment of the present invention;

[0066] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0067] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0068] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0069] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0070] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0071] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0072] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0073] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0074] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0075] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0076] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0077] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0078] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0079] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0080] Exemplary Methods

[0081] Figure 1 1 is a flow chart of a method for displaying pre-set data based on data format using a browser provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the method 100 for displaying front-end data desensitized based on the data format by using a browser includes the following steps:

[0082] Step 101, generating a query abnormality coefficient according to the account login information and user operation information obtained by using a browser;

[0083] Step 102, querying according to the query information to obtain the enterprise data to be displayed;

[0084] Step 103, generating a desensitization rule according to the query abnormality coefficient and the enterprise data to be displayed;

[0085] Step 104, desensitizing the enterprise data to be displayed according to the desensitization rule, and obtaining the display data;

[0086] Step 105: display the display data.

[0087] Specifically, this application proposes a pre-data desensitization method based on the data format using a browser to solve the technical problem that the existing data desensitization method has poor security during the query process of enterprise data. Specifically, it includes the following steps:

[0088] Step 1: Obtain account login information and user operation information using a browser; generate a query abnormality coefficient based on the account login information and the user operation information;

[0089] Step 2: Obtain query information, perform query based on the query information, and obtain the enterprise data to be displayed;

[0090] Step 3: Generate desensitization rules based on the query anomaly coefficient;

[0091] Step 4: Desensitize the enterprise data to be displayed according to the desensitization rules to obtain the display data;

[0092] Step 5: Display the data.

[0093] Preferably, generating the query abnormality coefficient according to the account login information and the user operation information includes:

[0094] Extract the current login address and account input information from the account login information; generate a first pre-exception coefficient QZ1 according to the current login address; generate a second pre-exception coefficient QZ2 according to the account input information;

[0095] Extracting query text information and character operation information from the user operation information; generating a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information;

[0096] The query anomaly coefficient CY is generated by the formula CY=α1×QZ1+α2×QZ2+α3×QZ3; wherein α1, α2 and α3 are weight coefficients.

[0097] Preferably, generating the first preceding abnormality coefficient according to the current login address includes:

[0098] Get the total number of historical logins ZDC and the number of logins XDC with the same historical login address as the current login address;

[0099] The first pre-anomaly coefficient QZ1 is calculated by the formula QZ1=exp(-β1×XDC / ZDC); wherein β1 is an adjustment parameter.

[0100] Preferably, generating the second pre-existing abnormality coefficient according to the account input information includes:

[0101] Extracting the interval time between each input character and adjacent input characters in the account input information, and dividing adjacent input characters whose interval time is less than an interval threshold into the same continuous character group; dividing each character in the account input information into a number of continuous character groups in turn; and integrating the several continuous character groups into a character group;

[0102] Acquire several historical normal character groups; acquire the number TG of historical normal character groups identical to the character group; and the total number ZG of historical normal character groups;

[0103] The second pre-anomaly coefficient QZ2 corresponding to the account input information is calculated by the formula QZ2=exp(-β2×TG / ZG); wherein β2 is an adjustment parameter.

[0104] Preferably, generating the third preceding anomaly coefficient according to the query text information and the character operation information includes:

[0105] Performing a word segmentation operation on the query text information to obtain a number of query words; extracting input character information corresponding to each query word in the character operation information;

[0106] Obtaining the interval time between each input character and adjacent input characters in the input character information, and dividing adjacent input characters whose interval time is less than an interval threshold into the same continuous character group; dividing each character in the account input information into a number of continuous character groups in turn; and integrating the several continuous character groups into a character group;

[0107] Acquire several historical query segmentations and their corresponding historical character information; acquire the historical query segmentations identical to the query segmentation and mark them as the primary comparison segmentations of the query segmentation; mark the primary comparison segmentations whose historical character information in the primary comparison segmentation is identical to the input character information of the corresponding query segmentation as secondary comparison segmentations; and mark the number of the primary comparison segmentations as YBCi and the number of the secondary comparison segmentations as EBCi; wherein i is the number of the query segmentation;

[0108] Obtaining historical character information corresponding to each secondary comparison segmentation word, generating a historical character group corresponding to the secondary comparison segmentation word according to the historical character information; obtaining the number of secondary comparison segmentations in the historical character group that are identical to the character group and marking them as TBCi;

[0109] The third preceding anomaly coefficient QZ3 corresponding to the query text information and character operation information is calculated by the formula QZ3=∑[γ1×exp(-β3×EBCi / YBCi)+γ2×exp(-β4×TBCi / EBCi)] / N; wherein β3 and β4 are adjustment parameters; γ1 and γ2 are weight coefficients; i=1, 2,…, N; and N is the total number of query word segmentations.

[0110] Preferably, generating the desensitization rule according to the query abnormality coefficient includes the following steps:

[0111] S31: Obtain the query anomaly coefficient, and determine whether the query anomaly coefficient is lower than the anomaly coefficient threshold; if yes, set the desensitization rule as the default desensitization rule; if no, proceed to S32;

[0112] S32: Obtain current license query information, and determine whether the current license query information is "empty". If so, obtain default license information, use the default license information as current license query information, and enter S33; otherwise, enter S33;

[0113] S33: extracting the license level and license classification in the current license query information; obtaining the enterprise data to be displayed, and inputting the enterprise data to be displayed into a data classification model to obtain its corresponding data level and data classification, wherein the data classification model is obtained through training of an artificial intelligence model;

[0114] S34: Determine whether the data level is less than the license level, and whether the data classification belongs to the license classification; if yes, set the desensitization rule to the default desensitization rule; if not, set the desensitization rule to the level desensitization rule corresponding to the data level.

[0115] Preferably, the data classification model is obtained by training an artificial intelligence model, including:

[0116] Acquire a number of enterprise data, and corresponding data classifications and data levels; integrate the number of enterprise data, data classifications and data levels into a number of training data and test data;

[0117] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data; ultimately, a data classification model is obtained whose input is enterprise data and whose output is the corresponding data classification and data level, wherein the artificial intelligence model includes a BP neural network and a RBF neural network.

[0118] Preferably, desensitizing the enterprise data to be displayed according to the desensitization rules comprises the following steps:

[0119] S41: Obtain a desensitization rule and determine whether the desensitization rule is a default desensitization rule; if yes, proceed to S42; if no, proceed to S43;

[0120] S42: Obtain account information, extract the queryable level and queryable category in the account information; obtain the data level and data category corresponding to the enterprise data to be displayed; determine whether the data level is less than the queryable level, and whether the data category belongs to the queryable category; if yes, do not perform data desensitization on the enterprise data to be displayed; if no, proceed to S43;

[0121] S43: Obtain the data level corresponding to the enterprise data to be displayed, query the corresponding data desensitization method in the desensitization method query table according to the data level, and perform data desensitization on the enterprise data to be displayed according to the desensitization method.

[0122] Preferably, the desensitization method query table is constructed in the following manner:

[0123] Obtain the average value AP of the security scores and the average value GP of the efficiency scores of each desensitization method by several experts; calculate the comprehensive score value ZP corresponding to the desensitization method through the formula ZP = a1×AP + a2×GP; where, a1 and a2 are weight coefficients; and a1 = b×exp(ZSD - SD), a2 = 1 - a1; ZSD is the maximum data level, SD is the data level; b is an adjustment coefficient, and 0 < b < 1;

[0124] Obtain the comprehensive scores of several desensitization methods corresponding to each data level, and sequentially obtain the desensitization method with the largest comprehensive score value for each data level and its corresponding data level; establish a mapping relationship between the desensitization method with the largest comprehensive score value and the corresponding data level; thus construct the desensitization method query table.

[0125] In an embodiment of the present invention, the specific implementation steps are as follows:

[0126] Step 1: Use the browser to obtain the account login information and user operation information; generate a query anomaly coefficient according to the account login information and user operation information.

[0127] The account login information in Step 1 includes the current login address and the account input information; the current login address is the device address of the device corresponding to the account for this login, which can be an IP address; the account input information is the input information when entering the account and password corresponding to the account, including the input characters and the time interval between adjacent input characters. For example, when entering "xm", the corresponding input characters are "x" and "m", and the corresponding time interval is the time interval between "x" and "m"; the user operation information is the operation information after the account login is completed, including the query text information and character operation information; the query text information is the text queried after the account login; the character operation information is the time interval between each input character and adjacent input characters in the input character information corresponding to the query text; for example, for "Zhang San's salary", the corresponding several input characters may be "zhangsangongzi", and the time intervals are the input intervals between each letter.

[0128] The query anomaly coefficient is whether the account login information and user operation information during this query are the same as the query method of the account owner corresponding to the account. The more different they are, the larger the query anomaly coefficient; the larger the query anomaly coefficient, the greater the possibility that the account has been borrowed or stolen by others.

[0129] Step 2: Obtain query information, perform query based on the query information, and obtain the enterprise data to be displayed;

[0130] The query information in step 2 is the user's input information, and an SQL statement can be used to query the enterprise database according to the SQL language to obtain the enterprise data to be displayed. The enterprise data to be displayed is the query result corresponding to the query information.

[0131] Step 3: Generate desensitization rules based on the query anomaly coefficient;

[0132] The desensitization rules in step 3 can be understood as whether it is necessary to desensitize the enterprise data to be displayed, and how to desensitize the enterprise data to be displayed;

[0133] Step 4: Desensitize the enterprise data to be displayed according to the desensitization rules to obtain the display data;

[0134] Step 5: Display the data.

[0135] This embodiment obtains account login information and user operation information by using a browser; generates a query anomaly coefficient based on the account login information and user operation information; obtains query information, performs a query based on the query information, and obtains the enterprise data to be displayed; generates a desensitization rule based on the query anomaly coefficient; desensitizes the enterprise data to be displayed based on the desensitization rule to obtain the display data; displays the display data; determines whether the account is abnormal based on the account login information and user operation information of the user before the query information, and if an abnormality occurs, it means that the account may be used by others, and at this time, it is necessary to perform corresponding desensitization operations on the data to be displayed, that is, desensitize the enterprise data to be displayed based on the abnormal results; desensitize the enterprise data accordingly before displaying; and improves the security of the enterprise data during the query process.

[0136] Generating the query abnormality coefficient according to the account login information and the user operation information includes: extracting the current login address and the account input information in the account login information; generating a first pre-abnormality coefficient QZ1 according to the current login address; generating a second pre-abnormality coefficient QZ2 according to the account input information;

[0137] Extracting query text information and character operation information from the user operation information; generating a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information;

[0138] The query anomaly coefficient CY is generated by the formula CY=α1×QZ1+α2×QZ2+α3×QZ3; wherein α1, α2 and α3 are weight coefficients; the weight coefficients can be set according to expert experience.

[0139] This embodiment uses the above formula to perform weighted summation of the first pre-anomaly coefficient, the second pre-anomaly coefficient and the third pre-anomaly coefficient to obtain a comprehensive query anomaly coefficient for this account login and query; this is convenient for expressing the abnormal situation of this account login and query. The larger the query anomaly coefficient, the greater the possibility that the corresponding account is abnormal.

[0140] Generate a first pre-existing abnormality coefficient according to the current login address, including: obtaining the total number of historical logins ZDC and the number of logins XDC when several historical login addresses are the same as the current login address; it can be understood that obtaining the historical login address and the current login address is permitted; the historical login address is the login address when the account is accessed normally in the historical record, and the total number of historical logins is the number of normal accesses of the account in the historical record;

[0141] The first pre-anomaly coefficient QZ1 is calculated by the formula QZ1=exp(-β1×XDC / ZDC); wherein β1 is an adjustment parameter; it is used to adjust the distribution of the first pre-anomaly coefficient corresponding to different login addresses; the specific value is set based on experience.

[0142] This embodiment calculates the pre-anomaly coefficient through the above formula. When the current login address does not appear in the historical login address, that is, the number of logins in which the historical login address is the same as the current login address is zero; then the first pre-anomaly coefficient at this time is the largest, that is, QZ1=1; when the current login address appears in the historical login address, and the more times it appears, the smaller the corresponding pre-anomaly coefficient; it is easy to understand that if the historical login address appears more times, the trustworthiness of the corresponding device is higher, the security risk of information leakage is smaller, and the corresponding pre-anomaly coefficient is smaller.

[0143] The second pre-anomaly coefficient is generated according to the account input information, including: extracting the interval time between each input character and the adjacent input character in the account input information, and dividing the adjacent input characters whose interval time is less than the interval threshold into the same continuous character group; dividing each character in the account input information into a plurality of continuous character groups in turn; integrating the plurality of continuous character groups into a character group; the account number in this embodiment is "zhangsan123456", and its corresponding three continuous character groups are {zhang}, {san} and {123456} respectively; the character group is {zhang, san, 123456}; due to different input habits of different people, the character group may be different, such as the character group may also be {zhangsan, 123, 456}, etc.;

[0144] Obtain several historical normal character groups; the historical normal character group is a character group obtained according to several normal account input information corresponding to the account owner, which can be understood as the input habit of the account owner when logging into the account normally; obtain the number TG of historical normal character groups that are the same as the character group; the total number ZG of historical normal character groups;

[0145] The second pre-anomaly coefficient QZ2 corresponding to the account input information is calculated by the formula QZ2=exp(-β2×TG / ZG); wherein β2 is an adjustment parameter used to adjust the distribution of the second pre-anomaly coefficients corresponding to different login addresses; the specific value is set based on experience.

[0146] This embodiment calculates the second preceding anomaly coefficient through the above formula; when the current character group does not appear in the historical normal character group, that is, the number of historical normal character groups that are the same as the character group TG=0; then its corresponding second preceding anomaly coefficient QZ2=1; if the current character group appears more times in the historical normal character group, its corresponding preceding anomaly coefficient will be smaller. It is easy to understand that the more the current account input information matches the input habits of the account owner, the smaller the corresponding second preceding anomaly coefficient will be.

[0147] Generating a third pre-existing abnormality coefficient according to the query text information and the character operation information includes: performing a segmentation operation on the query text information to obtain a plurality of query segmentations; extracting input character information corresponding to each query segmentation in the character operation information; the query text information in this embodiment is "Zhang San's salary"; after the segmentation operation, two query segmentations are obtained, namely "Zhang San" and "salary", and the corresponding input character information in the payment operation information is respectively "zhangsan" and "gongzi";

[0148] Obtaining the interval time between each input character and the adjacent input characters in the input character information, and dividing the adjacent input characters whose interval time is less than the interval threshold into the same continuous character group; dividing each character in the account input information into a number of continuous character groups in turn; integrating the number of continuous character groups into a character group; in this embodiment, corresponding character groups are generated according to the above input character information and the corresponding interval time, such as {zhang, san} and {gong, zi};

[0149] Acquire several historical query participles and their corresponding historical character information; acquire the historical query participles identical to the query participles and mark them as the first-level comparison participles of the query participles; mark the first-level comparison participles whose historical character information is identical to the input character information of the corresponding query participles as second-level comparison participles; and mark the number of first-level comparison participles as YBCi, and the number of second-level comparison participles as EBCi; wherein i is the number of the query participle; the first-level comparison participle corresponding to the query participle "Zhang San" in the present embodiment is the historical query participle "Zhang San" in the historical query participles; the second-level comparison participles are the historical query participles whose historical character information is "zhangsan" in each first-level comparison participle; due to different input habits of different people, the characters in the historical character information corresponding to the same query participle "Zhang San" may be "zhangsan", "zhangs", "zs", etc.;

[0150] Obtain the historical character information corresponding to each secondary comparison segmentation word, and generate the historical character group corresponding to the secondary comparison segmentation word according to the historical character information; obtain the number of secondary comparison segmentations in the historical character group that are the same as the character group and mark it as TBCi; that is, the number of historical character groups {zhang, san};

[0151] The third pre-anomaly coefficient QZ3 corresponding to the query text information and the character operation information is calculated by the formula QZ3=∑[γ1×exp(-β3×EBCi / YBCi)+γ2×exp(-β4×TBCi / EBCi)] / N; wherein β3 and β4 are adjustment parameters; the specific values ​​of β3 and β4 are set according to experience; similar to the functions of β1 and β2, both are numbers greater than zero; for example, when β1>1, the larger its value, the easier it is to distinguish between the first pre-anomaly coefficients corresponding to different or the same number of logins; setting appropriate adjustment parameters will make different pre-anomaly coefficients more reasonable; γ1 and γ2 are weight coefficients, and the specific values ​​of γ1 and γ2 are also set according to experience; i=1, 2,…, N; N is the total number of query word segments.

[0152] In this embodiment, the third preceding anomaly coefficient is calculated by the above formula; when the proportion of the secondary comparison segmentation corresponding to the query segmentation is larger, it means that the input habit of the corresponding query segmentation is more similar to the input habit of the account owner, the possibility that the account owner uses it is greater, and the corresponding third preceding anomaly coefficient is smaller; when the number of historical character groups corresponding to the character group of the secondary comparison segmentation corresponding to the query segmentation is larger and the proportion is larger, it means that the input habit of the corresponding query segmentation is more similar to the input habit of the account owner, the possibility that the account owner uses it is greater, and the corresponding third preceding anomaly coefficient is smaller; at the same time, the more similar the query segmentation in the query text information is to the input habit of the account owner, the greater the possibility that the account owner uses it this time, and the corresponding third preceding anomaly coefficient is smaller.

[0153] See also Figure 2 , generating the desensitization rule according to the query anomaly coefficient, including the following steps:

[0154] S31: Obtain the query anomaly coefficient, and determine whether the query anomaly coefficient is lower than the anomaly coefficient threshold; if yes, set the desensitization rule as the default desensitization rule; if no, proceed to S32;

[0155] The abnormal coefficient threshold in S31 is the threshold of the abnormal coefficient set in advance for each account; when the account level is higher, the corresponding abnormal coefficient threshold is lower, and when the number of abnormalities in the account is lower, the corresponding abnormal coefficient threshold is lower, and it is easier to find account abnormalities; specifically, the abnormal coefficient threshold corresponding to the account can be obtained in the following way; the queryable level corresponding to each queryable category in the account information is obtained, marked as KCj, j is the number of the queryable category, and the queryable level is the level of the corresponding queryable enterprise data in each queryable category; the queryable level is divided into the type of data classification of the queryable enterprise data set according to the identity of each account owner; obtain the number of abnormalities YC and the total number of uses ZSC of the account; the number of abnormalities is the number of times the query abnormality coefficient is lower than the abnormality coefficient threshold; the abnormal number threshold YZ of the corresponding account is calculated by the formula YZ=k1×exp(-β5×max(KCj))+k2×exp(-β6×YC / ZSC); wherein β5 and β6 are adjustment parameters, and their corresponding functions are the same as β1; k1 and k2 are proportional coefficients, and their specific values ​​are set according to experience.

[0156] S32: Obtain current license query information, and determine whether the current license query information is "empty". If so, obtain default license information, use the default license information as current license query information, and enter S33; otherwise, enter S33;

[0157] S32 The current permission query information is "empty", which can be understood as the current account owner has not set the current permission query information; at this time, the previously set default permission information is used as the current permission query information.

[0158] S33: extracting the license level and license classification in the current license query information; obtaining the enterprise data to be displayed, and inputting the enterprise data to be displayed into a data classification model to obtain its corresponding data level and data classification, wherein the data classification model is obtained through training of an artificial intelligence model;

[0159] The license classification is the corresponding enterprise data classification that can be queried when the account owner hopes that the account will be used this time. The license level is the data level corresponding to the enterprise data that can be queried corresponding to each license classification. Both are set by the account owner themselves, and can be set uniformly or individually.

[0160] S34: Determine whether the data level is less than the permitted level and whether the data classification belongs to the permitted classification; if yes, set the desensitization rule to the default desensitization rule; if no, set the desensitization rule to the level desensitization rule corresponding to the data level;

[0161] When the enterprise data to be displayed is in the license classification and is lower than the corresponding license level, it means that the corresponding enterprise data to be displayed is enterprise data that can be queried by the corresponding inquirer; at this time, data desensitization is not required; otherwise, it means that the corresponding enterprise data to be displayed is information that you do not want the inquirer to query, and data desensitization is required.

[0162] The data classification model is obtained through artificial intelligence model training, including: obtaining a number of enterprise data, and the corresponding data classification and data level; the data classification in this embodiment includes employee information, customer information, enterprise information and financial information, etc.; the data level includes level one, level two and level three; the enterprise data corresponding to level one is ordinary data, the enterprise data corresponding to level two is important data, and the enterprise data corresponding to level three is sensitive data; integrating a number of enterprise data, data classification and data level into a number of training data and test data;

[0163] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data; ultimately, a data classification model is obtained whose input is enterprise data and whose output is the corresponding data classification and data level, wherein the artificial intelligence model includes a BP neural network and a RBF neural network.

[0164] Desensitize the enterprise data to be displayed according to the desensitization rules, including the following steps:

[0165] S41: Obtain a desensitization rule and determine whether the desensitization rule is a default desensitization rule; if yes, proceed to S42; if no, proceed to S43;

[0166] S42: Obtain account information, and extract the queryable level and queryable classification from the account information; obtain the data level and data classification corresponding to the enterprise data to be displayed; determine whether the data level is less than the queryable level and whether the data classification belongs to the queryable classification; if yes, do not perform data desensitization on the enterprise data to be displayed; if no, proceed to S43;

[0167] S43: Obtain the data level corresponding to the enterprise data to be displayed, query the corresponding data desensitization method in the data desensitization method query table according to the data level, and perform data desensitization on the enterprise data to be displayed according to the data desensitization method.

[0168] The data desensitization method query table is constructed in the following way: Obtain the average value AP of the security scores and the average value GP of the efficiency scores of each data desensitization method by several experts; calculate the comprehensive score value ZP of the corresponding data desensitization method through the formula ZP = a1×AP + a2×GP; where, a1 and a2 are weight coefficients; and a1 = b×exp(ZSD - SD), a2 = 1 - a1; ZSD is the maximum data level, SD is the data level; b is an adjustment coefficient, and 0 < b < 1, and b = 0.9 in this embodiment;

[0169] Obtain the comprehensive scores of several data desensitization methods corresponding to each data level, and sequentially obtain each data level and the data desensitization method with the largest comprehensive score value; establish a mapping relationship between the data desensitization method with the largest comprehensive score value and the corresponding data level; thus construct the data desensitization method query table.

[0170] In this embodiment, the data desensitization method query table is obtained through the above method, and the comprehensive score coefficients corresponding to each different data level in each data classification are calculated through the above formula. When the data level is higher, the corresponding anti-leakage requirement is higher, and a data desensitization method with a higher security score should be adopted; in this embodiment, by setting the weight coefficient a1 of the security score, when the data level is higher, the weight coefficient a1 of the security score is larger; for enterprise data with a lower data level, its corresponding anti-leakage requirement is not very high, and at this time, consider its efficiency; ensure that the enterprise data can be quickly desensitized; according to the above method, the data desensitization methods corresponding to different data levels can be scored to obtain a more suitable data desensitization method for different data levels; and construct the data desensitization method query table; making the construction of the data desensitization method query table more reasonable.

[0171] Some data in the above formula are calculated by removing the dimension and taking their numerical values. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the real situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0172] Therefore, the present application obtains account login information and user operation information; generates a query anomaly coefficient based on the account login information and user operation information; obtains query information, performs query based on the query information, and obtains enterprise data to be displayed; generates desensitization rules based on the query anomaly coefficient; desensitizes the enterprise data to be displayed based on the desensitization rules to obtain display data; displays the display data; determines whether the account has an abnormality based on the account login information and user operation information of the user before querying the information in the past. If an abnormality occurs, it means that the account may be used by others. At this time, it is necessary to perform corresponding desensitization operations on the data to be displayed, that is, desensitize the enterprise data to be displayed based on the abnormal results; desensitize the enterprise data accordingly before displaying; and improves the security of the enterprise data during the query process.

[0173] Exemplary Devices

[0174] Figure 2 1 is a schematic diagram of a structure of a pre-data desensitization display device based on a data format using a browser provided by an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0175] A first generating module 210, for generating a query abnormality coefficient according to the account login information and user operation information obtained by using a browser;

[0176] A query module 220, used to query according to the query information and obtain the enterprise data to be displayed;

[0177] The second generating module 230 is used to generate a desensitization rule according to the query abnormality coefficient and the enterprise data to be displayed;

[0178] Desensitization module 240, used to desensitize the enterprise data to be displayed according to the desensitization rules, and obtain display data;

[0179] The display module 250 is used to display the display data.

[0180] Optionally, the first generating module 210 includes:

[0181] The first extraction submodule is used to extract the current login address and account input information from the account login information;

[0182] A first generating submodule, used for generating a first preceding abnormal coefficient QZ1 according to the current login address;

[0183] The second generating submodule is used to generate a second pre-anomaly coefficient QZ2 according to the account input information;

[0184] The second extraction module is used to extract the query text information and character operation information from the user operation information;

[0185] A third generating submodule, used for generating a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information;

[0186] The calculation submodule is used to calculate the query abnormality coefficient according to the first pre-abnormality coefficient, the second pre-abnormality coefficient and the third pre-abnormality coefficient, wherein the calculation formula of the query abnormality coefficient CY is:

[0187] CY=α1×QZ1+α2×QZ2+α3×QZ3, where α1, α2 and α3 are weight coefficients.

[0188] Optionally, the first generation submodule includes:

[0189] A first acquisition unit is used to acquire the login count ZDC of the current login address and the total login count XDC of all login addresses;

[0190] The first calculation unit is used to calculate a first pre-exception coefficient according to the number of logins ZDC and the total number of logins XDC, wherein the calculation formula of the first pre-exception coefficient is:

[0191] QZ1=exp(-β1×XDC / ZDC), where β1 is the adjustment parameter.

[0192] Optionally, the second generation submodule includes:

[0193] A first extraction unit is used to extract the interval time between each input character and adjacent input characters in the account input information, and divide the adjacent input characters whose interval time is less than the interval threshold into the same continuous character group; divide each character in the account input information into a plurality of continuous character groups in turn, and integrate the plurality of continuous character groups into a first character group;

[0194] A second acquisition unit is used to acquire the same number TG of historical normal character groups that are the same as the first character group and the total number ZG of historical normal character groups;

[0195] The second calculation unit is used to calculate the second preceding abnormal coefficient QZ2 according to the same number TG and the total number of historical normal character groups ZG, wherein the calculation formula of the second preceding abnormal coefficient is:

[0196] QZ2=exp(-β2×TG / ZG), where β2 is the adjustment parameter.

[0197] Optionally, the third generation submodule includes:

[0198] A word segmentation unit is used to perform word segmentation operation on the query text information to obtain a number of query words;

[0199] A second extraction unit is used to extract input character information corresponding to each query word in the character operation information;

[0200] An integration unit is used to obtain the interval time between each input character and the adjacent input characters in the input character information, and divide the adjacent input characters whose interval time is less than the interval threshold into the same continuous character group; divide each character in the account input information into a plurality of continuous character groups in turn; and integrate the plurality of continuous character groups into a second character group;

[0201] The first acquisition unit is used to acquire, based on the acquired historical query segmentations and the corresponding historical character information, the historical query segmentations identical to the query segmentations and mark them as primary comparison segmentations of the query segmentations; mark the primary comparison segmentations whose historical character information in the primary comparison segmentations is identical to the input character information of the corresponding query segmentations as secondary comparison segmentations; and mark the number of the primary comparison segmentations as YBCi and the number of the secondary comparison segmentations as EBCi; wherein i is the number of the query segmentations;

[0202] The second acquisition unit is used to acquire the historical character information corresponding to each secondary comparison segmentation, generate the historical character group corresponding to the secondary comparison segmentation according to the historical character information; acquire the number of secondary comparison segmentations in the historical character group that are the same as the second character group and mark them as TBCi;

[0203] The third calculation unit is used to calculate the third preceding anomaly coefficient QZ3 corresponding to the query text information and the character operation information through the formula QZ3=∑[γ1×exp(-β3×EBCi / YBCi)+γ2×exp(-β4×TBCi / EBCi)] / N; wherein β3 and β4 are adjustment parameters; γ1 and γ2 are weight coefficients; i=1, 2,…, N; N is the total number of query word segmentations.

[0204] Optionally, the second generating module 230 includes:

[0205] Step 1: Determine whether the query anomaly coefficient is lower than the anomaly coefficient threshold according to the query anomaly coefficient; if yes, set the desensitization rule as the default desensitization rule; if no, proceed to step 2;

[0206] Step 2: Get the current license query information and determine whether the current license query information is empty. If so, use the default license information as the current license query information and proceed to step 3; otherwise, proceed directly to step 3;

[0207] Step 3: Extract the license level and license classification in the current license query information, and input the enterprise data to be displayed into the data classification model to obtain its corresponding data level and data classification, wherein the data classification model is obtained through artificial intelligence model training;

[0208] Step 4: Determine whether the data level is less than the license level and whether the data classification belongs to the license classification; if so, set the desensitization rule as the default desensitization rule; if not, set the level desensitization rule corresponding to the data level as the desensitization rule.

[0209] Optionally, the data classification model is obtained by training an artificial intelligence model, including:

[0210] Obtaining certain enterprise data and corresponding data classifications and data levels;

[0211] Integrate a number of enterprise data, data classifications and data levels into a number of training data and test data;

[0212] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data to generate a data classification model whose input is enterprise data and outputs the corresponding data classification and data level, wherein the artificial intelligence model includes BP neural network and RBF neural network.

[0213] Optionally, the desensitization module 240 includes:

[0214] Step 1: Determine whether the desensitization rule is the default desensitization rule; if yes, proceed to step 2; if no, proceed to step 3;

[0215] Step 2: Extract the queryable level and queryable category in the account login information; obtain the data level and data category corresponding to the enterprise data to be displayed; determine whether the data level is less than the queryable level and whether the data category belongs to the queryable category; if yes, do not perform data desensitization on the enterprise data to be displayed; if no, proceed to step 3;

[0216] Step 3: According to the data level corresponding to the enterprise data to be displayed, query the corresponding desensitization method in the desensitization method query table, desensitize the enterprise data to be displayed according to the desensitization method, and obtain the display data.

[0217] Optionally, the steps for constructing the desensitization method query table are as follows:

[0218] Obtain the average safety score AP and the average efficiency score GP of each desensitization method from several experts;

[0219] The comprehensive score ZP of the corresponding desensitization method is calculated based on the average safety score AP and the average efficiency score GP. The calculation formula for the comprehensive score of each desensitization method is:

[0220] ZP=a1×AP+a2×GP, where a1 and a2 are weight coefficients; and a1=b×exp(ZSD-SD), a2=1-a1; ZSD is the maximum data level, SD is the data level; b is the adjustment coefficient, and 0 <b<1;

[0221] Obtain the comprehensive score values ​​of several desensitization methods corresponding to each data level, obtain each data level and the desensitization method with the largest comprehensive score in turn, establish a mapping relationship between the desensitization method with the largest comprehensive score and the corresponding data level, and construct a desensitization method query table.

[0222] Exemplary Electronic Devices

[0223] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0224] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0225] The memory 32 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0226] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0227] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0228] Of course, to simplify, Figure 3Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0229] Exemplary computer program products and computer-readable storage media

[0230] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0231] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0232] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0233] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0234] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0235] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0236] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0237] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0238] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0239] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for desensitizing and displaying pre-placed data based on data format using a browser, characterized in that: include: Generate a query anomaly coefficient based on the account login information and user operation information obtained through the browser; Perform a query based on the query information to obtain the enterprise data to be displayed; Generate a desensitization rule according to the query abnormality coefficient and the enterprise data to be displayed; Desensitize the enterprise data to be displayed according to the desensitization rule to obtain display data; Display the display data.

2. The method according to claim 1, characterized in that Generate query anomaly coefficients based on account login information and user operation information obtained through the browser, including: Extracting the current login address and account input information from the account login information; Generate a first pre-existing anomaly coefficient QZ1 according to the current login address; Generate a second pre-existing anomaly coefficient QZ2 according to the account input information; Extracting query text information and character operation information from the user operation information; Generate a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information; The query abnormality coefficient is calculated according to the first pre-anomaly coefficient, the second pre-anomaly coefficient and the third pre-anomaly coefficient, wherein the calculation formula of the query abnormality coefficient CY is: CY=α1×QZ1+α2×QZ2+α3×QZ3, where α1, α2 and α3 are weight coefficients.

3. The method according to claim 2, characterized in that Generating a first preceding abnormality coefficient according to the current login address includes: Obtain the login count ZDC of the current login address and the total login count XDC of all login addresses; The first pre-exception coefficient is calculated according to the number of logins ZDC and the total number of logins XDC, wherein the calculation formula of the first pre-exception coefficient is: QZ1=exp(-β1×XDC / ZDC), where β1 is the adjustment parameter.

4. The method according to claim 2, characterized in that: Generating a second pre-existing abnormality coefficient QZ2 according to the account input information includes: Extracting the interval time between each input character and adjacent input characters in the account input information, and dividing adjacent input characters whose interval time is less than an interval threshold into the same continuous character group; dividing each character in the account input information into a plurality of continuous character groups in turn, and integrating the plurality of continuous character groups into a first character group; Obtain the same number TG of historical normal character groups that are the same as the first character group and the total number ZG of historical normal character groups; According to the same number TG and the total number of historical normal character groups ZG, the second preceding abnormal coefficient QZ2 is calculated, wherein the calculation formula of the second preceding abnormal coefficient is: QZ2=exp(-β2×TG / ZG), where β2 is the adjustment parameter.

5. The method according to claim 2, characterized in that: Generating a third preceding anomaly coefficient QZ3 according to the query text information and the character operation information includes: Performing a word segmentation operation on the query text information to obtain a number of query word segments; Extracting input character information corresponding to each query word in the character operation information; Obtaining the interval time between each input character and an adjacent input character in the input character information, and dividing the adjacent input characters whose interval time is less than an interval threshold into the same continuous character group; dividing each character in the account input information into a plurality of continuous character groups in turn; and integrating the plurality of continuous character groups into a second character group; According to the obtained several historical query participles and their corresponding historical character information, the historical query participles identical to the query participles are obtained and marked as the primary comparison participles of the query participles; the primary comparison participles whose historical character information in the primary comparison participles is identical to the input character information of the corresponding query participles are marked as secondary comparison participles; and the number of the primary comparison participles is marked as YBCi, and the number of the secondary comparison participles is marked as EBCi; wherein i is the number of the query participles; Obtaining historical character information corresponding to each secondary comparison segmentation word, generating a historical character group corresponding to the secondary comparison segmentation word according to the historical character information; obtaining the number of secondary comparison segmentations in the historical character group that are identical to the second character group and marking them as TBCi; The third preceding anomaly coefficient QZ3 corresponding to the query text information and character operation information is calculated by the formula QZ3=∑[γ1×exp(-β3×EBCi / YBCi)+γ2×exp(-β4×TBCi / EBCi)] / N; wherein β3 and β4 are adjustment parameters; γ1 and γ2 are weight coefficients; i=1, 2,…, N; and N is the total number of query word segmentations.

6. The method according to claim 1, characterized in that Generate a desensitization rule according to the query abnormality coefficient and the enterprise data to be displayed, including: Step 1: judging whether the query anomaly coefficient is lower than the anomaly coefficient threshold according to the query anomaly coefficient; if yes, setting the desensitization rule as the default desensitization rule; if no, proceeding to step 2; Step 2: Get the current license query information, and determine whether the current license query information is empty. If so, use the default license information as the current license query information and proceed to step 3; otherwise, proceed directly to step 3; Step 3: extracting the license level and license classification in the current license query information, and inputting the enterprise data to be displayed into a data classification model to obtain its corresponding data level and data classification, wherein the data classification model is obtained by training an artificial intelligence model; Step 4: Determine whether the data level is less than the license level and whether the data classification belongs to the license classification; if yes, set the desensitization rule as the default desensitization rule; if not, set the level desensitization rule corresponding to the data level as the desensitization rule.

7. The method according to claim 6, characterized in that The data classification model is obtained through artificial intelligence model training, including: Obtaining certain enterprise data and corresponding data classifications and data levels; Integrate a number of enterprise data, data classifications and data levels into a number of training data and test data; The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data to generate a data classification model whose input is enterprise data and outputs the corresponding data classification and data level, wherein the artificial intelligence model includes BP neural network and RBF neural network.

8. The method according to claim 1, characterized in that Desensitizing the enterprise data to be displayed according to the desensitization rule to obtain display data, including: Step 1: Determine whether the desensitization rule is the default desensitization rule; if yes, proceed to step 2; if no, proceed to step 3; Step 2: extract the queryable level and queryable category in the account login information; obtain the data level and data category corresponding to the enterprise data to be displayed; determine whether the data level is less than the queryable level and whether the data category belongs to the queryable category; if yes, do not perform data desensitization on the enterprise data to be displayed; if no, proceed to step 3; Step 3: According to the data level corresponding to the enterprise data to be displayed, query the corresponding desensitization method in the desensitization method query table, desensitize the enterprise data to be displayed according to the desensitization method, and obtain the display data.

9. The method according to claim 8, characterized in that The steps for constructing the desensitization method query table are as follows: Obtain the average safety score AP and the average efficiency score GP of each desensitization method from several experts; The comprehensive score ZP of the corresponding desensitization method is calculated according to the average safety score AP and the average efficiency score GP, wherein the calculation formula of the comprehensive score of each desensitization method is: ZP=a1×AP+a2×GP, where a1 and a2 are weight coefficients; and a1=b×exp(ZSD-SD), a2=1-a1; ZSD is the maximum data level, SD is the data level; b is the adjustment coefficient, and 0 <b<1; Obtain the comprehensive score values ​​of several desensitization methods corresponding to each data level, obtain each data level and the desensitization method with the largest comprehensive score value in turn, establish a mapping relationship between the desensitization method with the largest comprehensive score value and the corresponding data level, and construct the desensitization method query table.

10. A device for desensitizing and displaying pre-placed data based on data format using a browser, characterized in that: include: A first generating module, used to generate a query abnormality coefficient according to the account login information and user operation information obtained by using a browser; A query module, used to query based on query information and obtain enterprise data to be displayed; A second generating module is used to generate a desensitization rule according to the query abnormality coefficient and the enterprise data to be displayed; A desensitizing module, used for desensitizing the enterprise data to be displayed according to the desensitizing rule to obtain display data; The display module is used to display the display data.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 9.

12. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 9.

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