Data compliance determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a data compliance determination method and device, electronic equipment and a storage medium, and relates to the technical field of data analysis. Obtaining multiple pieces of to-be-analyzed data, and extracting metadata corresponding to each piece of to-be-analyzed data; according to a label library and each piece of metadata, performing label information matching on each piece of to-be-analyzed data, and according to a label matching result corresponding to each piece of to-be-analyzed data, the metadata corresponding to each piece of to-be-analyzed data and a rule library, performing risk rule matching on each piece of to-be-analyzed data; and determining whether the to-be-analyzed data has compliance risks or not according to the label matching result and the risk rule matching result corresponding to the to-be-analyzed data. Therefore, the accuracy of risk rule matching can be improved, and on the basis, whether the to-be-analyzed data has the compliance risk is jointly determined by combining the label matching result and the risk rule matching result, so that the judgment accuracy of the compliance risk can be improved.

Description

Technical Field

[0001] This application relates to the field of data analysis technology. Specifically, it relates to a method, device, electronic device, and storage medium for determining data compliance. Background Art

[0002] With the acceleration of digital transformation and the development of stricter global data supervision, the importance of data compliance risks has become increasingly prominent in scenarios such as finance, healthcare, e-commerce, government affairs, and cross-border business, such as personal information protection, big data risk control, data security protection, etc. Currently, when judging data compliance risks, there are often problems with low judgment accuracy. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, device, electronic device, and storage medium for determining data compliance to improve the accuracy of data compliance risk judgment.

[0004] To achieve the above purpose, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, this application provides a method for determining data compliance, which is applied to an electronic device. A tag library and a rule library are stored in the electronic device. Multiple tag information is stored in the tag library, and the tag information represents the attribute information of risk data. Multiple risk rules are stored in the rule library. The method includes: Obtain multiple data to be analyzed, and extract the metadata corresponding to each of the data to be analyzed; According to the tag library and each piece of metadata, perform tag information matching on each of the data to be analyzed, and according to the tag matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library, perform risk rule matching on each of the data to be analyzed; Determine whether there is a compliance risk for each of the data to be analyzed according to the tag matching results and risk rule matching results corresponding to each of the data to be analyzed.

[0005] In an optional implementation, the step of performing tag information matching on each of the data to be analyzed according to the tag library and each piece of metadata, and performing risk rule matching on each of the data to be analyzed according to the tag matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library includes: For each piece of metadata corresponding to the data to be analyzed, determine whether there is target tag information corresponding to the metadata in the multiple tag information in the tag library; If there is target tag information corresponding to the metadata, perform risk rule matching on the data to be analyzed according to the target tag information and the multiple risk rules in the rule library; If there is no target label information corresponding to the metadata, risk rule matching is performed on the data to be analyzed according to the metadata of the data to be analyzed and multiple risk rules in the rule library.

[0006] In an alternative embodiment, the metadata includes field information, and the label information includes a field label, a similar field label corresponding to the field label, a sensitive label, and a category label; Determining whether there is target label information corresponding to the metadata in the multiple label information in the label library for the metadata corresponding to each data to be analyzed includes: For the metadata corresponding to each data to be analyzed, determining whether there is a target field label corresponding to the metadata among the multiple field labels; If there is a target field label corresponding to the metadata among the multiple field labels, the target similar field label, target sensitive label, and target category label corresponding to the metadata are determined according to the target field label; If there is no target field label corresponding to the metadata among the multiple field labels, determining whether there is a target similar field label corresponding to the metadata among the multiple similar field labels; If there is a target similar field label corresponding to the metadata among the multiple similar field labels, the target field label, target sensitive label, and target category label corresponding to the metadata are determined according to the target similar field label; If there is no target similar field label corresponding to the metadata among the multiple similar field labels, it is determined that there is no target label information corresponding to the metadata in the multiple label information.

[0007] In an alternative embodiment, determining whether there is a target field label corresponding to the metadata among the multiple field labels includes: Performing field value matching on the metadata and the multiple field labels; In the case of successful field value matching, the field label in the multiple field labels that matches the metadata is determined as the target field label. In the case of failed field value matching, regular expression matching is performed on the metadata and the multiple field labels; In the case of successful regular expression matching, the field label in the multiple field labels that matches the metadata is determined as the target field label. In the case of failed regular expression matching, similarity matching is performed on the metadata and the multiple field labels; In the case of successful similarity matching, determine the field tags among the multiple field tags that match the metadata as the target field tags. In the case of failed similarity matching, determine that there are no target field tags corresponding to the metadata among the multiple field tags.

[0008] In an alternative embodiment, the risk rule matching of the data to be analyzed according to the target label information and multiple risk rules in the rule library includes: Determine candidate risk rules corresponding to the target label information from among the multiple risk rules; Perform pattern matching on the target label information and the candidate risk rules; In the case of successful pattern matching, determine the candidate risk rule as the target risk rule. In the case of failed pattern matching, perform semantic matching on the target label information and the candidate risk rules; In the case of successful semantic matching, determine the candidate risk rule as the target risk rule. In the case of failed semantic matching, determine that there are no corresponding target risk rules for the data to be analyzed In an alternative embodiment, the risk rule matching of the data to be analyzed according to the metadata of the data to be analyzed and multiple risk rules in the rule library includes: Perform field matching on the metadata of the data to be analyzed and the multiple risk rules; In the case of successful field matching, determine the risk rules among the multiple risk rules that match the metadata as the target risk rules. In the case of failed field matching, determine that there are no corresponding target risk rules for the data to be analyzed.

[0009] In an alternative embodiment, determining whether there are compliance risks for each piece of data to be analyzed according to the label matching results and risk rule matching results corresponding to each piece of data to be analyzed includes: If there are corresponding target label information and target risk rules for the data to be analyzed, determine that the data to be analyzed has a compliance risk; If there is corresponding target label information for the data to be analyzed and there are no corresponding target risk rules, calculate a risk value according to the target label information corresponding to the data to be analyzed, and determine whether the data to be analyzed has a compliance risk according to the risk value; If there is no corresponding target label information for the data to be analyzed and there are corresponding target risk rules, send the data to be analyzed to a processing personnel, and determine whether the data to be analyzed has a compliance risk according to the confirmation operation of the processing personnel for the data to be analyzed; If there is no corresponding target label information and no corresponding target risk rule for the data to be analyzed, it is determined that there is no compliance risk for the data to be analyzed.

[0010] In an alternative embodiment, the calculating a risk value according to the target label information corresponding to the data to be analyzed and determining whether there is a compliance risk for the data to be analyzed according to the risk value includes: Calculating the risk value according to the target sensitive label corresponding to the data to be analyzed and a preset risk calculation formula; If the risk value exceeds a preset risk threshold, sending the data to be analyzed to a handler, and determining whether there is a compliance risk for the data to be analyzed according to the confirmation operation of the handler for the data to be analyzed; If the risk value does not exceed the preset risk threshold, it is determined that there is no compliance risk for the data to be analyzed.

[0011] In an alternative embodiment, the method further includes: Sending the data to be analyzed with a compliance risk to a handler, and performing data repair or data isolation on the data to be analyzed according to the processing operation of the handler for the data to be analyzed.

[0012] In an alternative embodiment, the rule library further includes a first correction rule corresponding to each risk rule; the performing data repair or data isolation on the data to be analyzed according to the processing operation of the handler for the data to be analyzed includes: Obtaining the processing operation of the handler for the data to be analyzed; If the processing operation is to perform data repair on the data to be analyzed, when there is a corresponding target risk rule for the data to be analyzed, performing data correction on the data to be analyzed according to the first correction rule corresponding to the target risk rule, and when there is no corresponding target risk rule for the data to be analyzed, performing data correction on the data to be analyzed according to the second correction rule sent by the handler; If the processing operation is to perform data isolation on the data to be analyzed, performing an isolation operation on the data to be analyzed according to the data type of the data to be analyzed.

[0013] In an alternative embodiment, after performing data correction on the data to be analyzed, the method further includes: Performing repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed; In the case where the repair verification of the data to be analyzed fails, restore the repaired data to be analyzed, and record the reason for the repair failure corresponding to the data to be analyzed.

[0014] In a second aspect, the present application provides a data compliance determination device, which is applied to an electronic device. The electronic device stores a tag library and a rule library. The tag library includes multiple tag information, and the tag information represents the attribute information of the data to be analyzed. The rule library includes multiple risk rules. The device includes: An acquisition module, configured to acquire multiple data to be analyzed, and extract the metadata corresponding to each of the data to be analyzed; A matching module, configured to perform tag information matching on each of the data to be analyzed according to the tag library and each of the metadata, and perform risk rule matching on each of the data to be analyzed according to the tag matching result corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library; A determination module, configured to determine whether there is a compliance risk for each of the data to be analyzed according to the tag matching result and the risk rule matching result corresponding to each of the data to be analyzed.

[0015] In a third aspect, the present application provides an electronic device, including a processor and a memory. The memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method according to any one of the foregoing embodiments.

[0016] In a fourth aspect, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the foregoing embodiments is implemented.

[0017] The data compliance determination method, device, electronic device, and storage medium provided by the embodiments of the present application enable the electronic device to perform tag information matching on each of the data to be analyzed according to multiple tag information in the tag library and the metadata corresponding to the data to be analyzed, and perform risk rule matching on each of the data to be analyzed according to the tag matching result, the metadata of each of the data to be analyzed, and multiple risk rules in the rule library, so as to determine whether there is a compliance risk for each of the data to be analyzed according to the tag matching result and the risk rule matching result corresponding to each of the data to be analyzed. In this way, by first performing tag matching on the data to be analyzed and then performing risk rule matching on the data to be analyzed through the tag matching result, the accuracy of matching risk rules can be improved. On this basis, by jointly determining whether there is a compliance risk for the data to be analyzed in combination with the tag matching result and the risk rule matching result, the accuracy of judging compliance risk can be improved.

[0018] To make the above objects, features, and advantages of the present application more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and describes them in detail as follows. Description of the Drawings

[0019] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0020] Figure 1 Shows a block diagram of an electronic device provided by an embodiment of the present application; Figure 2 Shows a flowchart of a method for determining data compliance provided by an embodiment of the present application; Figure 3 Shows another flowchart of a method for determining data compliance provided by an embodiment of the present application; Figure 4 Shows yet another flowchart of a method for determining data compliance provided by an embodiment of the present application; Figure 5 Shows a functional module diagram of a data compliance determination device provided by an embodiment of the present application.

[0021] Icons: 100 - Memory; 110 - Processor; 120 - Communication Module; 200 - Acquisition Module; 210 - Matching Module; 220 - Determination Module. Detailed Embodiments

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Usually, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0024] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0025] Please refer to Figure 1 , which is a block diagram of the electronic device provided by the embodiment of the present application. The electronic device includes a memory 100, a processor 110, and a communication module 120. Each of the memory 100, the processor 110, and the communication module 120 is electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines.

[0026] Among them, the memory 100 is used to store computer programs or data that can be executed by the processor. The memory 100 can be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0027] The processor 110 is used to read / write the data or computer program stored in the memory and execute the computer program to implement the data compliance determination method provided by the embodiment of the present application.

[0028] The communication module 120 is used to establish a communication connection between the electronic device and other communication terminals through a network and is used to receive and send data through the network.

[0029] It should be understood that Figure 1 The structure shown is only a schematic diagram of the structure of the electronic device, and the electronic device may further include more than Figure 1more or fewer components shown, or having a configuration different from that shown in Figure 1 shown. Figure 1 Each component shown in can be implemented by hardware, software, or a combination thereof.

[0030] Optionally, a tag library and a rule library are also stored in the electronic device. The tag library stores multiple pieces of tag information, which characterize the attribute information of risk data, and the rule library stores multiple risk rules.

[0031] Optionally, the tag information is the attribute information of data that may have compliance risks. For example, if an ID number may have risks such as personal information leakage, the tag information may include an ID number tag.

[0032] In this embodiment, both the tag library and the rule library can be interfaced with some external policy supervision platforms to synchronize the tag information and risk rules in real time, so as to ensure the accuracy of data compliance risk determination.

[0033] Next, taking the electronic device in the above Figure 1 as the execution subject, the data compliance determination method provided in the embodiments of the present application will be introduced exemplarily in combination with the process schematic diagram. Specifically, Figure 2 is a process schematic diagram of a data compliance determination method provided in the embodiments of the present application. Please refer to Figure 2 , and the method includes: Step S20, obtain multiple pieces of data to be analyzed, and extract the metadata corresponding to each piece of data to be analyzed.

[0034] Optionally, the electronic device can obtain the data to be analyzed through multiple channels, such as database connection, API (Application Programming Interface) call, FTP (File Transfer Protocol) upload, etc.

[0035] Optionally, the data to be analyzed can be multi-source heterogeneous data.

[0036] In this embodiment, the electronic device can obtain the metadata corresponding to the data to be analyzed in different ways for the data to be analyzed obtained through different acquisition methods.

[0037] In a possible implementation manner, for the data to be analyzed obtained through database connection, the metadata (field name, index, annotation, etc.) corresponding to the data to be analyzed can be obtained by directly reading the table Schema.

[0038] For the data to be analyzed obtained through FTP upload, the file type can be determined by the file extension (such as.csv,.sql,.json,.xml, etc.) and syntactic features (such as delimiters, keywords, format tags, etc.), so as to parse the metadata of the file, obtain field names, field types, etc. Then, methods such as pandas.infer_dtype are used to sample part of the data to infer the field types corresponding to the data to be analyzed, and translation or matching with pre-trained word vectors is used to infer the field definitions corresponding to the data to be analyzed, so as to obtain the metadata corresponding to the data to be analyzed.

[0039] For the data to be analyzed obtained through API calls, the data to be analyzed can be obtained by defining the interface address, request method, request format, etc., and the data to be analyzed is parsed according to different request formats to obtain metadata such as field names and field types.

[0040] Optionally, for the convenience of subsequent processing, the electronic device can convert the metadata into key-value pairs after obtaining the metadata.

[0041] In a possible implementation manner, the association analysis ability between metadata can also be enhanced through knowledge graph technology.

[0042] Step S21: Match the label information for each data to be analyzed according to the label library and each piece of metadata, and perform risk rule matching on each data to be analyzed according to the label matching results corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library.

[0043] Optionally, the label matching result may include whether the data to be analyzed matches the corresponding target label information and the specific target label information that is matched.

[0044] In this embodiment, the electronic device can perform risk rule matching on the data to be analyzed in combination with the label matching result corresponding to the data to be analyzed, so the accuracy of risk rule matching can be improved.

[0045] Step S22: Determine whether there are compliance risks for each data to be analyzed according to the label matching results and risk rule matching results corresponding to each data to be analyzed.

[0046] Optionally, the risk rule matching result may include whether the data to be analyzed matches the corresponding target risk rule and the specific target risk rule that is matched.

[0047] It can be understood that, compared with the method in the related art of simply matching the data to be analyzed with risk rules to determine whether there is a compliance risk in the data to be analyzed, the data compliance method provided by the embodiments of the present application can perform label matching on the data to be analyzed and then perform risk rule matching on the data to be analyzed according to the label matching result, so as to determine whether there is a compliance risk in the data to be analyzed by combining the label matching result and the risk rule matching result. This not only ensures the accuracy of risk rule matching, but also improves the accuracy of compliance risk judgment by combining the label matching result and the risk rule matching result to jointly determine whether there is a compliance risk in the data to be analyzed.

[0048] For the data compliance determination method provided by the embodiments of the present application, the electronic device can perform label information matching on each data to be analyzed according to multiple label information in the label library and the metadata corresponding to the data to be analyzed, and perform risk rule matching on each data to be analyzed according to the label matching result, the metadata of each data to be analyzed, and multiple risk rules in the rule library, so as to determine whether there is a compliance risk in each data to be analyzed according to the label matching result and the risk rule matching result corresponding to each data to be analyzed. In this way, first perform label matching on the data to be analyzed, and then perform risk rule matching on the data to be analyzed through the label matching result, which can improve the accuracy of matching risk rules. On this basis, combining the label matching result and the risk rule matching result to jointly determine whether there is a compliance risk in the data to be analyzed can improve the accuracy of compliance risk judgment.

[0049] Optionally, next, a possible implementation manner is provided for how to perform label information matching on each data to be analyzed according to the label library and each metadata, and perform risk rule matching on each data to be analyzed according to the label matching result corresponding to each data to be analyzed, the metadata corresponding to each data to be analyzed, and the rule library.

[0050] Specifically, on the basis of Figure 2 for the data compliance determination method provided by the embodiments of the present application, another process schematic diagram is shown in Figure 3 The above step S21 can be implemented through the following steps: Figure 3 Step S21-1, for the metadata corresponding to each data to be analyzed, determine whether there is target label information corresponding to the metadata among the multiple label information in the label library.

[0051] Optionally, the electronic device can determine whether there is target label information corresponding to the metadata among the multiple label information in the label library by matching the metadata with the label information.

[0052] ​Step S21-2, if there is target label information corresponding to the metadata, perform risk rule matching on the data to be analyzed according to the target label information and multiple risk rules in the rule library.

[0053] It can be understood that each risk rule in the rule library is generated according to the label information. Therefore, risk rule matching can be performed in the rule library according to the target label information corresponding to the data to be analyzed.

[0054] Step S21-3, if there is no target label information corresponding to the metadata, perform risk rule matching on the data to be analyzed according to the metadata of the data to be analyzed and multiple risk rules in the rule library.

[0055] It can be understood that if there is no target label in the metadata, risk rule matching can be directly performed in the rule library according to the metadata corresponding to the data to be analyzed.

[0056] Optionally, there can be multiple pieces of such label information.

[0057] In this embodiment, for the convenience of management, the label library can be a hierarchical structure, and the label information can include multi-level labels.

[0058] Optionally, the label information can include field labels, similar field labels corresponding to the field labels, sensitive labels, and category labels.

[0059] Optionally, the sensitive labels can include non-sensitive, low-sensitivity, medium-sensitivity, and extremely sensitive. Among them, each sensitive label can be represented by a number. For example, non-sensitive is represented as 1, low-sensitivity is represented as 2, medium-sensitivity is represented as 3, and extremely sensitive is represented as 4.

[0060] In a possible implementation manner, the label information can include first-level labels, second-level labels, third-level labels, fourth-level labels, fifth-level labels, and sixth-level labels. Among them, the first-level labels are large-category labels, the second-level labels are refined labels of the first-level labels, the third-level labels are refined labels of the second-level labels, the fifth-level labels are refined labels of the third-level labels, the sixth-level labels are peer labels of the fifth-level labels and correspond one-to-one with the fifth-level labels, the fourth-level labels are sensitive labels, and the fourth-level labels can correspond one-to-one with the third-level labels.

[0061] Optionally, the fifth-level labels can be specific field labels, the sixth-level labels can be similar field labels corresponding to the field labels, and the first-level to third-level labels are all category labels.

[0062] In one example, if the first-level label is personal information, the refined second-level labels under this first-level label may include personal financial information. The refined third-level labels under the personal financial information in this second-level label may include bank account information. The fourth-level label corresponding to this third-level label, that is, the sensitive label, may be of a high sensitivity level. The refined fifth-level label under the bank account information in this third-level label, that is, the specific field label, may be the main account number. The sixth-level label corresponding to this field label, that is, the similar field label, may be bank account number, bank card number, debit card number, credit card number, etc., and there may be multiple sixth-level labels.

[0063] It can be understood that the specific field label may be the field attribute information with a standard name, such as identity information, while the similar field is other names corresponding to this field attribute information with a standard name. For example, some enterprises, for the convenience of processing, directly call identity information identity ID, etc.

[0064] In this embodiment, the category labels include the first-level label, the second-level label, and the third-level label.

[0065] Based on this, next, a possible implementation manner is provided for determining whether there is a target label information corresponding to the metadata in the multiple label information in the label library for the metadata corresponding to each data to be analyzed respectively.

[0066] Specifically, the electronic device can respectively determine whether there is a target field label corresponding to the metadata in the multiple field labels for the metadata corresponding to each data to be analyzed.

[0067] In this embodiment, if there is a target field label corresponding to the metadata in the multiple field labels, then determine the target similar field label, the target sensitive label, and the target category label corresponding to the metadata according to the target field label. If there is no target field label corresponding to the metadata in the multiple field labels, then determine whether there is a target similar field label corresponding to the metadata in the multiple similar field labels; if there is a target similar field label corresponding to the metadata in the multiple similar field labels, then determine the target field label, the target sensitive label, and the target category label corresponding to the metadata according to the target similar field label. If there is no target similar field label corresponding to the metadata in the multiple similar field labels, then determine that there is no target label information corresponding to the metadata in the multiple label information.

[0068] Optionally, when performing label matching, the metadata can be first matched with the specific field label, and in the case of failure to match the specific field label, the metadata is then matched with the similar field label.

[0069] It can be understood that since there is a corresponding relationship from the first-level label to the sixth-level label, after determining the target field label (the fifth-level label) corresponding to the metadata, other label information corresponding to this metadata can be directly obtained.

[0070] Similarly, if the metadata does not directly match the five - level tag but matches the target similar field (six - level tag) corresponding to the metadata, other tag information corresponding to the metadata can also be directly obtained according to this six - level tag.

[0071] In this embodiment, if both the five - level tag and the six - level tag match fails, it can be determined that the metadata has no corresponding target tag information.

[0072] Optionally, in order to improve the success rate of tag matching and further improve the accuracy of data compliance risk determination, multiple matching methods can be used to match the metadata with the field tags and similar field tags.

[0073] Next, taking the field tags as an example, introduce how to determine whether there is a target field tag corresponding to the metadata among multiple field tags.

[0074] Specifically, the electronic device can first perform field - value matching on the metadata and multiple field tags. When the field - value matching is successful, the field tag that matches the metadata among the multiple field tags is determined as the target field tag. When the field - value matching fails, perform regular - expression matching on the metadata and multiple field tags. When the regular - expression matching is successful, the field tag that matches the metadata among the multiple field tags is determined as the target field tag. When the regular - expression matching fails, perform similarity matching on the metadata and multiple field tags. When the similarity matching is successful, the field tag that matches the metadata among the multiple field tags is determined as the target field tag. When the similarity matching fails, it is determined that there is no target field tag corresponding to the metadata among the multiple field tags.

[0075] Optionally, field - value matching refers to calculating the field value of the metadata and the field values of each field tag, and determining whether the field value of the metadata is equal to the field value of the field tag. If they are equal, it means that the field - value matching is successful, and the field tag with the same field value as the metadata field value is determined as the target field tag.

[0076] Optionally, if there is no field tag with the same field value as the metadata field value, it can be determined that the field - value matching fails. At this time, the regular expression of the metadata and the regular expressions of each field tag can be calculated, and it is determined whether the regular expression of the metadata matches the regular expressions of each field tag. The field tag that matches the regular expression of the metadata is determined as the target field tag.

[0077] Optionally, if there is no field tag that matches the regular expression of the metadata, it can be determined that the regular - expression matching fails. At this time, the cosine similarity between the metadata and each field tag can be calculated.

[0078] In a possible implementation, when calculating the cosine similarity, the Sentence-BERT model can be used to process the metadata and field labels first, so as to generate a 768-dimensional vector corresponding to the metadata and a 768-dimensional vector corresponding to the field labels. Then, the cosine similarity between the metadata and each field label is calculated through a preset similarity calculation formula.

[0079] Optionally, the similarity calculation formula can be S=(A·B) / (||A||*||B||), where S represents the cosine similarity, A represents the 768-dimensional vector corresponding to the metadata, and B represents the 768-dimensional vector corresponding to the field label.

[0080] Optionally, the electronic device can determine the field labels whose cosine similarity with the metadata exceeds the first preset similarity threshold. If the number of field labels whose cosine similarity exceeds the first preset similarity threshold is multiple, the target field labels corresponding to the metadata are multiple.

[0081] Optionally, if there are no field labels whose cosine similarity with the metadata exceeds the first preset similarity threshold, it can be determined that there are no corresponding target field labels for the metadata.

[0082] In a possible implementation, the first preset similarity threshold can be 0.7. It can be understood that the process of determining whether there are target similar field labels corresponding to the metadata among multiple similar field labels is the same as the above process, and will not be elaborated here.

[0083] In this embodiment, if there is target label information, the risk rules can be matched for the data to be analyzed according to the target label information.

[0084] Optionally, in order to improve the success rate of risk rule matching and further improve the accuracy of data compliance risk determination, multiple matching methods can be used for risk rule matching.

[0085] In this embodiment, the electronic device can first determine the candidate risk rules corresponding to the target label information from multiple risk rules, and perform pattern matching on the target label information and the candidate risk rules. If the pattern matching is successful, the candidate risk rules are determined as the target risk rules. If the pattern matching fails, semantic matching is performed on the target label information and the candidate risk rules. If the semantic matching is successful, the candidate risk rules are determined as the target risk rules. If the semantic matching fails, it is determined that there are no corresponding target risk rules for the data to be analyzed.

[0086] Optionally, since each risk rule in the rule library is generated according to the label information, the candidate risk rules can be directly determined from multiple risk rules according to the target label information corresponding to the data to be analyzed.

[0087] In a possible implementation manner, the corresponding candidate risk rules can be directly determined according to the refined five-level tags. Taking the refinement of the target tag information to the five-level tag as the personal ID number as an example, the candidate risk rules at this time are the risk rules corresponding to the personal ID number.

[0088] Optionally, pattern matching refers to matching through regular expressions or data templates. In this embodiment, the data template can be a data format.

[0089] Optionally, the electronic device can calculate the regular expression corresponding to the target tag information and the regular expression corresponding to the candidate risk rule, and determine whether the candidate rule is the target risk rule by comparing whether the two regular expressions match. Alternatively, the electronic device can compare the data template corresponding to the target tag information and the data template corresponding to the candidate risk rule to determine whether the candidate risk rule is the target risk rule.

[0090] Optionally, if the candidate risk rule does not match the regular expression of the target tag information or does not match the data template corresponding to the target tag information, semantic matching needs to be performed on the target tag information and the candidate risk rule.

[0091] Optionally, the electronic device can extract the text description corresponding to the target tag information, generate the semantic vector corresponding to the target tag information according to the text description, and extract the semantic features corresponding to the candidate risk rule, including text descriptions, etc., generate the semantic vector corresponding to the candidate risk rule through the Sentence-BERT model, and then calculate the cosine similarity between the semantic vector corresponding to the target tag information and the semantic vector corresponding to the candidate risk rule, so as to determine whether the candidate risk rule is the target risk rule according to the cosine similarity.

[0092] Optionally, the calculation method of the cosine similarity is the same as the above method, and will not be elaborated here.

[0093] Optionally, when the cosine similarity between the candidate risk rule and the target tag information exceeds the second preset similarity threshold, the electronic device can determine the candidate risk rule as the target risk rule. It can be understood that if the cosine similarity between the candidate risk rule and the target tag information does not exceed the second preset similarity threshold, it can be determined that there is no target risk rule corresponding to the target tag information. In a possible implementation manner, the second preset similarity threshold can be 0.8.

[0094] Optionally, if there is no target label information, the metadata of the data to be analyzed can be directly field-matched with multiple risk rules. In the case of successful field matching, the risk rules that match the metadata among the multiple risk rules are determined as the target risk rules. In the case of failed field matching, it is determined that there is no corresponding target risk rule for the data to be analyzed.

[0095] In this embodiment, to improve the matching efficiency, all rules in the rule library are imported into the rule engine, and the rule engine can automatically traverse the rules during matching and call different algorithms for risk rule matching. Next, a possible implementation method is provided for determining whether there is a compliance risk for each data to be analyzed based on the corresponding label matching result and risk rule matching result of each data to be analyzed.

[0096] Specifically, if the data to be analyzed has corresponding target label information and target risk rules, the electronic device can determine that the data to be analyzed has a compliance risk. If the data to be analyzed does not have corresponding target label information and does not have corresponding target risk rules, it is determined that the data to be analyzed does not have a compliance risk. If the data to be analyzed only matches any one of the target label information and the target risk rules, further judgment is required for this data.

[0097] In a possible implementation manner, if the data to be analyzed has corresponding target label information and does not have corresponding target risk rules, a risk value is calculated based on the corresponding target label information of the data to be analyzed, and it is determined whether the data to be analyzed has a compliance risk according to the risk value.

[0098] In another possible implementation manner, if the data to be analyzed does not have corresponding target label information and has corresponding target risk rules, the data to be analyzed is sent to the processing personnel, and it is determined whether the data to be analyzed has a compliance risk according to the confirmation operation of the processing personnel for the data to be analyzed.

[0099] In this embodiment, the electronic device needs to calculate the risk value for the data to be analyzed that matches the target label information, and this risk value can represent the probability that the data to be analyzed has a compliance risk.

[0100] Optionally, when the data to be analyzed does not have target label information but has target risk rules, the risk value cannot be calculated, so the to-be-analyzed rule can be directly sent to the processing personnel for risk judgment.

[0101] Next, a possible implementation method is provided for calculating the risk value according to the corresponding target label information of the data to be analyzed and determining whether the data to be analyzed has a compliance risk according to the risk value.

[0102] In this embodiment, the electronic device can calculate a risk value according to the target sensitive label corresponding to the data to be analyzed and a preset risk calculation formula. If the risk value exceeds the preset risk threshold, the electronic device can send the data to be analyzed to the processor, and determine whether there is a compliance risk for the data to be analyzed according to the confirmation operation of the processor for the data to be analyzed. If the risk value does not exceed the preset risk threshold, it is determined that the data to be analyzed does not have a compliance risk.

[0103] In a possible implementation manner, the risk calculation formula can be expressed as: RiskScore = *MatchRate + *Sensitivity + *HistoryViolation Wherein, RiskScore represents the risk value, MatchRate represents the cosine similarity between the candidate risk rule and the target label information, Sensitivity represents the sensitive parameter corresponding to the sensitive label in the target label information, and HistoryViolation represents the ratio of the total number of violations of the data source corresponding to the data to be analyzed to the total number of violations of all data sources in history.

[0104] Optionally, the sensitive parameter can be set according to the actual application scenario and the specific sensitive label. For example, the sensitive parameter corresponding to non-sensitive is 25%, the sensitive parameter corresponding to low sensitivity is 50%, the sensitive parameter corresponding to medium sensitivity is 75%, and the sensitive parameter corresponding to extremely sensitive is 100%.

[0105] Optionally, 、 、 respectively represent constant values and can be set according to the actual application scenario. In a possible implementation manner, 、 、 。

[0106] Optionally, the preset risk threshold can be set according to the actual application scenario, for example, it is 0.6.

[0107] Optionally, considering that in the related art, data with compliance risks is often discarded or globally isolated, resulting in a decrease in data utilization rate. Therefore, in order to improve the data utilization rate, the electronic device can also repair the data with compliance risks and isolate the data that cannot be repaired.

[0108] Specifically, for the data to be analyzed with compliance risks, the electronic device can also send it to the processing personnel, and the processing personnel determine whether the data can be repaired. Then, the electronic device can perform data repair or data isolation on the data to be analyzed according to the processing operations of the processing personnel for the data to be analyzed.

[0109] In this embodiment, for the data to be analyzed that requires the processing personnel to determine whether there are compliance risks, when the processing personnel determine that the data to be analyzed has compliance risks, they can determine whether the data to be analyzed can be repaired, and thus issue a processing instruction for the data to be analyzed; for the data to be analyzed that the electronic device directly determines has compliance risks, the data to be analyzed can be sent to the processing personnel to determine whether it can be repaired.

[0110] In this embodiment, when the processing personnel determine that the data to be analyzed can be repaired, they can send a repair instruction for the data to be analyzed to the electronic device. Then, the electronic device can execute the data repair process for the data to be analyzed. If the processing personnel determine that the data to be analyzed cannot be repaired, they can issue an isolation instruction for the data to be analyzed to the electronic device, and the electronic device performs data isolation on the data to be analyzed.

[0111] It can be understood that since the data repair process can be executed by the electronic device itself without manual data repair by the staff, it can also improve the data utilization rate while reducing the manual processing cost and improving the data processing efficiency. In a possible implementation manner, in order to facilitate data repair, the rule library can also include a first correction rule corresponding to each risk rule.

[0112] It can be understood that each risk rule corresponds to a first correction rule. For example, if the ID number should be desensitized in a certain format under compliance conditions, the risk rule can be the format when the ID number is not desensitized or the desensitization is incorrect. The first correction rule corresponding to this risk rule is the format of the ID number after normal desensitization. In this case, the electronic device can process the ID number with compliance risks based on this first correction rule to make it compliant.

[0113] In this embodiment, the electronic device can obtain the processing operations of the processing personnel for the data to be analyzed.

[0114] In a possible implementation manner, if the processing operation is to perform data repair on the data to be analyzed, when the data to be analyzed has a corresponding target risk rule, the data to be analyzed is corrected according to the first correction rule corresponding to the target risk rule, and when the data to be analyzed does not have a corresponding target risk rule, the data to be analyzed is corrected according to the second correction rule sent by the processing personnel.

[0115] In another possible implementation, if the processing operation is to perform data isolation on the data to be analyzed, the data to be analyzed is isolated according to its data type.

[0116] Optionally, if the data to be analyzed that can be data-repaired matches the corresponding target risk rule, the electronic device can directly call the first correction rule corresponding to the target risk rule to correct the data to be analyzed.

[0117] If the data to be analyzed that can be data-repaired does not match the corresponding target risk rule, the electronic device can send the data to be analyzed to the relevant processing personnel, and the processing personnel can define the second correction rule for the data to be analyzed by themselves.

[0118] It can be understood that the electronic device can perform word segmentation and part-of-speech tagging on the second correction rule through technologies such as NLP. On the basis of understanding the user's intention, it can judge the type of user requirements, that is, the specific correction content, and then generate executable code logic according to the data source type and data connection information of the data to be analyzed, so as to realize data repair for the data to be analyzed by executing the code logic.

[0119] Optionally, the data type of the data to be analyzed refers to the data format, such as including structured data, file-type data, API-type data, and so on.

[0120] In this embodiment, when the electronic device cannot perform data repair on the data to be analyzed, it can isolate the data according to its data type.

[0121] In a possible implementation, if the data to be analyzed is structured data, such as the data in a structured database table, the electronic device can use the REVOKE statement of SQL syntax to revoke all users' SELECT, INSERT, UPDATE, and DELETE permissions for the specified table; if the data to be analyzed is file-type data, the electronic device can encrypt the file using the AES algorithm to prevent users from performing addition, deletion, modification, and query on the file; if the data to be analyzed is API-type data, the electronic device can automatically establish a black and white list mechanism through the dynamic routing engine deployed at the API gateway layer, and update the API paths prohibited from access in real time to prevent users from obtaining non-compliant API data.

[0122] In this embodiment, the electronic device can trigger an alarm after successfully isolating the data to notify the relevant person in charge to perform manual processing on the isolated data to be analyzed.

[0123] Optionally, considering that there may still be cases of repair errors or repair failures after data repair, the electronic device can also, after performing data correction on the data to be analyzed, perform repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed. In the case where the repair verification of the data to be analyzed fails, the repaired data to be analyzed is restored, and the reason for the repair failure corresponding to the data to be analyzed is recorded.

[0124] Optionally, if the data to be analyzed is repaired by the first correction rule, the electronic device can perform repair verification on the data to be analyzed through the first correction rule. If the data to be analyzed is repaired by the second correction rule, the electronic device can perform repair verification on the data to be analyzed through the second correction rule.

[0125] It can be understood that if the repair is successful, it can be determined that the repaired data to be analyzed is compliant data. On the contrary, if the repair fails, the data to be analyzed may not be repairable. Therefore, the electronic device can restore it to the data before repair and record the reason for its repair failure.

[0126] Optionally, the electronic device can generate a data copy of the data to be analyzed for recording before repairing the data to be analyzed, so as to restore it in the case of repair failure.

[0127] Optionally, in the case where the repair of the data to be analyzed fails, the electronic device can isolate the data to be analyzed, or can trigger an alarm to notify the processing personnel to manually repair the data to be analyzed. Optionally, in order to ensure data security, the electronic device can adopt multi-party secure computing (MPC) technology during the data repair process to achieve the availability and invisibility of the data to be analyzed.

[0128] Optionally, considering that in the related art, there is also a problem that the process of determining data compliance lacks records and it is difficult to meet the full-chain audit requirements of regulatory agencies, the electronic device can also generate its corresponding audit record and repair log for the data to be analyzed.

[0129] In this embodiment, the audit record can include relevant records of all steps in the entire process such as metadata extraction, label matching, rule matching, risk calculation, compliance determination, data repair, data isolation, alarm, and manual processing of the data to be analyzed.

[0130] Optionally, a multi-dimensional statistical report corresponding to the data to be analyzed can also be generated in combination with the audit record to show the overall compliance situation of the data, such as the compliance rate trend of all data to be analyzed, high-frequency violation types, repair success rate, etc. Among them, the compliance rate trend can be statistically analyzed and displayed according to dates, such as daily, weekly, and monthly. The high-frequency violation types can be displayed as percentages, and the repair success rate can be statistically analyzed according to data sources.

[0131] Next, in combination with Figure 4 an exemplary introduction to the data compliance determination method provided by the embodiments of the present application will be given.

[0132] Specifically, Figure 4 Another flowchart of the data compliance determination method provided by the embodiments of the present application is shown in Figure 4 .

[0133] The electronic device can first obtain the data to be analyzed, perform related processing such as metadata extraction on the data to be analyzed, and then match the label information of the data to be analyzed through the label library, so as to output the data to be analyzed that has matched the target label information and the data to be analyzed that has not matched the target label information, and perform risk rule matching on these two types of data to be analyzed respectively.

[0134] For the data to be analyzed that has matched the target label information, it can be determined whether the data to be analyzed has matched the target risk rule through the target label information.

[0135] If the target risk rule is matched, the data to be analyzed can be sent to the processing personnel to determine whether it can be intelligently repaired. If it can be intelligently repaired, the intelligent repair plan is executed. If it cannot be intelligently repaired, the data to be analyzed can be isolated, and relevant personnel can be notified to execute the manual processing process, such as manual repair.

[0136] If the target risk rule is not matched, the risk value of the data to be analyzed can be calculated, and it can be determined whether the risk value of the data to be analyzed exceeds the preset risk threshold. If it does not exceed the preset risk threshold, it can be determined that there is no relevant compliance risk for the data to be analyzed, and the overall audit record and report corresponding to the data to be analyzed are generated. If it exceeds the preset risk threshold, the manual review process can be entered, and the processing personnel can perform compliance determination on it, and execute the subsequent processing process according to the compliance determination result.

[0137] For the data to be analyzed that has not matched the target label information, risk rule matching can be directly performed.

[0138] If the target risk rule is matched, the manual review process can be entered, and the processing personnel can perform compliance determination on it, and execute the subsequent processing process according to the compliance determination result. If the target risk rule is not matched, it can be determined that there is no relevant compliance risk for the data to be analyzed, and the overall audit record and report corresponding to the data to be analyzed are generated.

[0139] In the manual review process, the processing personnel can determine whether there is a compliance risk for the data to be analyzed. If it is determined that there is no compliance risk, the audit record and report corresponding to the data to be analyzed can be generated. If it is determined that there is a compliance risk, it can be further determined whether the data to be analyzed can be intelligently repaired.

[0140] If intelligent repair can be performed, repair instructions can be given, and the electronic device creates a data copy of the data to be analyzed for storage, corrects the data to be analyzed through correction rules (the first correction rule or the second correction rule), and verifies the correction result. If the correction is successful, a repair log of the data to be analyzed can be generated, and then an overall audit record and report corresponding to the data to be analyzed can be generated. If the correction fails, the data to be analyzed with the failed correction can be deleted, the data to be analyzed can be restored according to the data copy, and it can be sent to relevant personnel to execute the manual processing process, such as manual repair.

[0141] If intelligent repair cannot be performed, isolation instructions can be given, and the electronic device isolates the data to be analyzed and notifies relevant personnel to execute the manual processing process, such as manual repair, etc.

[0142] After the manual processing process ends, an overall audit record and report also need to be generated for the data to be analyzed.

[0143] To execute the corresponding steps in the above embodiments and various possible ways, an implementation manner of a data compliance determination device is given below. Optionally, the data compliance determination device can adopt the device structure of the above Figure 1 shown electronic device. Further, please refer to Figure 5 , Figure 5 which is a functional module diagram of a data compliance determination device provided by an embodiment of the present application. It should be noted that the basic principle and the technical effects generated by the data compliance determination device provided in this embodiment are the same as those in the above embodiments. For a brief description, for parts not mentioned in this embodiment, reference can be made to the corresponding content in the above embodiments. The data compliance determination device includes: an acquisition module 200, a matching module 210, and a determination module 220.

[0144] The acquisition module 200 is used to acquire multiple pieces of data to be analyzed and extract the metadata corresponding to each piece of data to be analyzed.

[0145] It can be understood that the acquisition module 200 can also be used to execute the above step S20.

[0146] The matching module 210 is used to perform label information matching on each piece of data to be analyzed according to the label library and each piece of metadata, and perform risk rule matching on each piece of data to be analyzed according to the label matching result corresponding to each piece of data to be analyzed, the metadata corresponding to each piece of data to be analyzed, and the rule library.

[0147] It can be understood that the matching module 210 can also be used to execute the above step S21.

[0148] The determination module 220 is configured to determine whether there are compliance risks for each piece of data to be analyzed based on the label matching results and risk rule matching results corresponding to the data to be analyzed.

[0149] It can be understood that the determination module 220 can also be used to execute the above step S22.

[0150] Optionally, the matching module 210 is further configured to respectively determine whether there is a target label information corresponding to the metadata in the multiple label information in the label library for the metadata corresponding to each piece of data to be analyzed; if there is a target label information corresponding to the metadata, perform a risk rule matching on the data to be analyzed according to the target label information and the multiple risk rules in the rule library; if there is no target label information corresponding to the metadata, perform a risk rule matching on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule library.

[0151] It can be understood that the matching module 210 can also be used to execute the above step S21-1 to step S21-2.

[0152] Optionally, the matching module 210 is further configured to respectively determine whether there is a target field label corresponding to the metadata in the multiple field labels for the metadata corresponding to each piece of data to be analyzed; if there is a target field label corresponding to the metadata in the multiple field labels, determine the target similar field label, target sensitive label, and target category label corresponding to the metadata according to the target field label; if there is no target field label corresponding to the metadata in the multiple field labels, determine whether there is a target similar field label corresponding to the metadata in the multiple similar field labels; if there is a target similar field label corresponding to the metadata in the multiple similar field labels, determine the target field label, target sensitive label, and target category label corresponding to the metadata according to the target similar field label; if there is no target similar field label corresponding to the metadata in the multiple similar field labels, determine that there is no target label information corresponding to the metadata in the multiple label information.

[0153] Optionally, the matching module 210 is further configured to perform a field value matching on the metadata and the multiple field labels; in the case of a successful field value matching, determine the field label in the multiple field labels that matches the metadata as the target field label, in the case of a failed field value matching, perform a regular expression matching on the metadata and the multiple field labels; in the case of a successful regular expression matching, determine the field label in the multiple field labels that matches the metadata as the target field label, in the case of a failed regular expression matching, perform a similarity matching on the metadata and the multiple field labels; in the case of a successful similarity matching, determine the field label in the multiple field labels that matches the metadata as the target field label, in the case of a failed similarity matching, determine that there is no target field label corresponding to the metadata in the multiple field labels.

[0154] Optionally, the matching module 210 is further configured to determine a candidate risk rule corresponding to the target label information from multiple risk rules; perform pattern matching on the target label information and the candidate risk rule; in the case of successful pattern matching, determine the candidate risk rule as the target risk rule, and in the case of failed pattern matching, perform semantic matching on the target label information and the candidate risk rule; in the case of successful semantic matching, determine the candidate risk rule as the target risk rule, and in the case of failed semantic matching, determine that there is no corresponding target risk rule for the data to be analyzed.

[0155] Optionally, the matching module 210 is further configured to perform field matching on the metadata of the data to be analyzed and multiple risk rules; in the case of successful field matching, determine the risk rule that matches the metadata among the multiple risk rules as the target risk rule, and in the case of failed field matching, determine that there is no corresponding target risk rule for the data to be analyzed.

[0156] Optionally, the determining module 220 is further configured to determine that the data to be analyzed has a compliance risk if there is a corresponding target label information and target risk rule for the data to be analyzed; if there is a corresponding target label information for the data to be analyzed but no corresponding target risk rule, calculate a risk value based on the target label information corresponding to the data to be analyzed, and determine whether the data to be analyzed has a compliance risk according to the risk value; if there is no corresponding target label information for the data to be analyzed but there is a corresponding target risk rule, send the data to be analyzed to a processor, and determine whether the data to be analyzed has a compliance risk according to the confirmation operation of the processor for the data to be analyzed; if there is no corresponding target label information for the data to be analyzed and no corresponding target risk rule, determine that the data to be analyzed has no compliance risk.

[0157] Optionally, the determining module 220 is further configured to calculate a risk value according to the target sensitive label corresponding to the data to be analyzed and a preset risk calculation formula; if the risk value exceeds a preset risk threshold, send the data to be analyzed to a processor, and determine whether the data to be analyzed has a compliance risk according to the confirmation operation of the processor for the data to be analyzed; if the risk value does not exceed the preset risk threshold, determine that the data to be analyzed has no compliance risk.

[0158] Optionally, the determining module 220 is further configured to send the data to be analyzed with a compliance risk to a processor, and perform data repair or data isolation on the data to be analyzed according to the processing operation of the processor for the data to be analyzed.

[0159] Optionally, the determining module 220 is further configured to obtain a processing operation of a processing person for the data to be analyzed; if the processing operation is to perform data repair on the data to be analyzed, then in the case that there is a corresponding target risk rule for the data to be analyzed, perform data correction on the data to be analyzed according to the first correction rule corresponding to the target risk rule, and in the case that there is no corresponding target risk rule for the data to be analyzed, perform data correction on the data to be analyzed according to the second correction rule sent by the processing person; if the processing operation is to perform data isolation on the data to be analyzed, then perform an isolation operation on the data to be analyzed according to the data type of the data to be analyzed.

[0160] Optionally, the determining module 220 is further configured to perform repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed; in the case that the repair verification of the data to be analyzed fails, perform data restoration on the repaired data to be analyzed, and record the reason for the repair failure corresponding to the data to be analyzed.

[0161] The data compliance determination device provided by the embodiments of the present application obtains a plurality of data to be analyzed through an obtaining module, and extracts metadata corresponding to each data to be analyzed; through a matching module, performs label information matching on each data to be analyzed according to a tag library and each metadata, and performs risk rule matching on each data to be analyzed according to the tag matching result corresponding to each data to be analyzed, each metadata corresponding to each data to be analyzed, and a rule library; through a determining module, determines whether there is a compliance risk for each data to be analyzed according to the tag matching result and the risk rule matching result corresponding to each data to be analyzed. In this way, first perform label matching on the data to be analyzed, and then perform risk rule matching on the data to be analyzed through the label matching result, which can improve the accuracy of matching risk rules. On this basis, jointly determine whether there is a compliance risk for the data to be analyzed by combining the label matching result and the risk rule matching result, which can improve the accuracy of judging compliance risks.

[0162] Optionally, the above modules may be stored in the Figure 1 memory shown in the form of software or firmware and solidified in the operating system (OS) of the electronic device, and can be executed by the Figure 1 processor. At the same time, the data, program code, etc. required to execute the above modules can be stored in the memory.

[0163] The embodiments of the present application further provide a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the data compliance determination method provided by the embodiments of the present application.

[0164] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0165] In addition, each functional module in various embodiments of the present application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0166] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0167] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining data compliance, characterized in that Applied to an electronic device, in which a tag library and a rule library are stored. Multiple tag information are stored in the tag library, and the tag information represents the attribute information of risk data. Multiple risk rules are stored in the rule library. The method includes: Obtain multiple data to be analyzed, and extract the metadata corresponding to each of the data to be analyzed; According to the tag library and each piece of metadata, perform tag information matching on each of the data to be analyzed, and according to the tag matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library, perform risk rule matching on each of the data to be analyzed; Determine whether there is a compliance risk for each of the data to be analyzed according to the tag matching results and risk rule matching results corresponding to each of the data to be analyzed.

2. The method according to claim 1, wherein The performing tag information matching on each of the data to be analyzed according to the tag library and each piece of metadata, and performing risk rule matching on each of the data to be analyzed according to the tag matching results corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library, includes: For each piece of metadata corresponding to the data to be analyzed, determine whether there is target tag information corresponding to the metadata among the multiple tag information in the tag library; If there is target tag information corresponding to the metadata, perform risk rule matching on the data to be analyzed according to the target tag information and the multiple risk rules in the rule library; If there is no target tag information corresponding to the metadata, perform risk rule matching on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule library.

3. The method according to claim 2, wherein The metadata includes field information, and the tag information includes field tags, similar field tags corresponding to the field tags, sensitive tags, and category tags; The determining whether there is target tag information corresponding to the metadata among the multiple tag information in the tag library for each piece of metadata corresponding to the data to be analyzed includes: For each piece of metadata corresponding to the data to be analyzed, determine whether there is a target field tag corresponding to the metadata among the multiple field tags; If there is a target field tag corresponding to the metadata among the multiple field tags, determine the target similar field tag, target sensitive tag, and target category tag corresponding to the metadata according to the target field tag; If there is no target field tag corresponding to the metadata among the multiple field tags, determine whether there is a target similar field tag corresponding to the metadata among the multiple similar field tags; If there is a target similar field tag corresponding to the metadata among the multiple similar field tags, determine the target field tag, target sensitive tag, and target category tag corresponding to the metadata according to the target similar field tag; If there is no target similar field tag corresponding to the metadata among the multiple similar field tags, determine that there is no target tag information corresponding to the metadata among the multiple tag information.

4. The method according to claim 3, wherein Determining whether there is a target field label corresponding to the metadata among the multiple field labels includes: Performing field value matching on the metadata and the multiple field labels; When the field value matching is successful, determining the field label that matches the metadata among the multiple field labels as the target field label. When the field value matching fails, performing regular expression matching on the metadata and the multiple field labels; When the regular expression matching is successful, determining the field label that matches the metadata among the multiple field labels as the target field label. When the regular expression matching fails, performing similarity matching on the metadata and the multiple field labels; When the similarity matching is successful, determining the field label that matches the metadata among the multiple field labels as the target field label. When the similarity matching fails, determining that there is no target field label corresponding to the metadata among the multiple field labels.

5. The method according to claim 2, wherein Performing risk rule matching on the data to be analyzed according to the target label information and the multiple risk rules in the rule library includes: Determining candidate risk rules corresponding to the target label information from the multiple risk rules; Performing pattern matching on the target label information and the candidate risk rules; When the pattern matching is successful, determining the candidate risk rule as the target risk rule. When the pattern matching fails, performing semantic matching on the target label information and the candidate risk rules; When the semantic matching is successful, determining the candidate risk rule as the target risk rule. When the semantic matching fails, determining that there is no corresponding target risk rule for the data to be analyzed.

6. The method according to claim 2, wherein Performing risk rule matching on the data to be analyzed according to the metadata of the data to be analyzed and the multiple risk rules in the rule library includes: Performing field matching on the metadata of the data to be analyzed and the multiple risk rules; When the field matching is successful, determining the risk rule that matches the metadata among the multiple risk rules as the target risk rule. When the field matching fails, determining that there is no corresponding target risk rule for the data to be analyzed.

7. The method according to claim 1, wherein Determining whether there is a compliance risk for each data to be analyzed according to the label matching result and the risk rule matching result corresponding to each data to be analyzed includes: If the data to be analyzed has a corresponding target label information and a target risk rule, determining that the data to be analyzed has a compliance risk; If the data to be analyzed has a corresponding target label information and does not have a corresponding target risk rule, calculating a risk value according to the target label information corresponding to the data to be analyzed, and determining whether the data to be analyzed has a compliance risk according to the risk value; If the data to be analyzed does not have a corresponding target label information and has a corresponding target risk rule, sending the data to be analyzed to a handler, and determining whether the data to be analyzed has a compliance risk according to the confirmation operation of the handler for the data to be analyzed. If there is no corresponding target label information and no corresponding target risk rule for the data to be analyzed, it is determined that there is no compliance risk for the data to be analyzed.

8. The method according to claim 7, wherein Calculating a risk value according to the target label information corresponding to the data to be analyzed, and determining whether there is a compliance risk for the data to be analyzed according to the risk value, includes: Calculating the risk value according to the target sensitive label corresponding to the data to be analyzed and a preset risk calculation formula; If the risk value exceeds a preset risk threshold, sending the data to be analyzed to a handler, and determining whether there is a compliance risk for the data to be analyzed according to the confirmation operation of the handler for the data to be analyzed; If the risk value does not exceed the preset risk threshold, it is determined that there is no compliance risk for the data to be analyzed.

9. The method according to claim 1, characterized in that The method further includes: Sending the data to be analyzed with a compliance risk to a handler, and performing data repair or data isolation on the data to be analyzed according to the processing operation of the handler for the data to be analyzed.

10. The method according to claim 9, characterized in that The rule library further includes a first correction rule corresponding to each risk rule; performing data repair or data isolation on the data to be analyzed according to the processing operation of the handler for the data to be analyzed, includes: Obtaining the processing operation of the handler for the data to be analyzed; If the processing operation is to perform data repair on the data to be analyzed, when there is a corresponding target risk rule for the data to be analyzed, performing data correction on the data to be analyzed according to the first correction rule corresponding to the target risk rule, and when there is no corresponding target risk rule for the data to be analyzed, performing data correction on the data to be analyzed according to the second correction rule sent by the handler; If the processing operation is to perform data isolation on the data to be analyzed, performing an isolation operation on the data to be analyzed according to the data type of the data to be analyzed.

11. The method according to claim 10, wherein After performing data correction on the data to be analyzed, the method further includes: Performing repair verification on the data to be analyzed according to the first correction rule or the second correction rule corresponding to the data to be analyzed; In the case where the repair verification of the data to be analyzed fails, restoring the repaired data to be analyzed, and recording the reason for the repair failure corresponding to the data to be analyzed.

12. A data compliance determination device, characterized in that, Applied to an electronic device, the electronic device stores a label library and a rule library, the label library includes a plurality of label information, the label information represents the attribute information of the data to be analyzed, the rule library includes a plurality of risk rules, the device includes: An acquisition module, configured to acquire a plurality of data to be analyzed, and extract metadata corresponding to each of the data to be analyzed; A matching module, configured to perform label information matching on each of the data to be analyzed according to the label library and each of the metadata, and perform risk rule matching on each of the data to be analyzed according to the label matching result corresponding to each of the data to be analyzed, the metadata corresponding to each of the data to be analyzed, and the rule library; A determination module, configured to determine whether there is a compliance risk for each piece of data to be analyzed according to the tag matching result and the risk rule matching result corresponding to each piece of data to be analyzed.

13. An electronic device, characterized in that, It includes a processor and a memory. The memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the method according to any one of claims 1-11.

14. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1-11.

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