Database sensitive information identification method, abnormal state handling method, and related devices

Abstract

This application relates to the fields of financial technology and information technology, and in particular to a method for identifying sensitive information in a database, a method for handling abnormal states, and related apparatus. The database sensitive information identification method of this application requires first obtaining a sensitive information identification request; based on the sensitive information identification request, determining the target financial database; performing a full scan of the target financial database to obtain baseline financial data; identifying financial business data uploaded in the next adjacent time interval as target financial data; performing difference detection between the target financial data and the baseline financial data to determine incremental financial data; and identifying sensitive information in the incremental financial data to obtain the identification result data. Therefore, it can effectively scan for sensitive information without interfering with normal business processes. Furthermore, the abnormal state handling method of this application can effectively respond to anomalies that occur during the sensitive information identification process.

Description

Technical Field

[0001] This application relates to the fields of financial technology and information technology, and in particular to a method for identifying sensitive information in a database, a method for handling abnormal states, and related devices. Background Technology

[0002] In the financial sector, the sensitive information stored in databases is of paramount importance, typically including highly sensitive data such as personally identifiable information and financial records. The security of this information directly impacts customer privacy and the reputation of financial institutions. To ensure the security of this sensitive information, financial institutions usually employ techniques such as full scans, incremental scans, and scheduled scans to regularly check the sensitive information in their databases.

[0003] However, these technologies share a common limitation: they struggle to simultaneously meet the requirements of high timeliness, accuracy, and minimal impact on business operations. Therefore, how to conduct effective sensitive information scanning without disrupting normal business processes is a problem that the industry needs to consider. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a database sensitive information identification method, an abnormal state handling method, and related apparatus. The database sensitive information identification method can effectively scan for sensitive information without interfering with normal business processes, and the abnormal state handling method can effectively respond to abnormalities that occur during the sensitive information identification process.

[0005] The database sensitive information identification method according to the first aspect of this application includes:

[0006] Request to obtain sensitive information identification;

[0007] Based on the sensitive information identification request, a target financial database is determined; wherein, the target financial database stores multiple financial business data.

[0008] A full scan of the target financial database is performed to obtain benchmark financial data; wherein the upload time of the benchmark financial data corresponds to a benchmark time interval.

[0009] The financial business data uploaded to the target financial database in the next time interval adjacent to the reference time interval is identified as the target financial data.

[0010] Based on the difference detection between the target financial data and the benchmark financial data, incremental financial data is determined.

[0011] Sensitive information is identified in the incremental financial data to obtain the identification result data.

[0012] According to some embodiments of this application, the financial business data includes text business data, audio business data, and image business data. The step of uploading the financial business data from the next adjacent time interval to the target financial database, and defining it as target financial data, includes:

[0013] Query the text business data, audio business data, and image business data uploaded to the target financial database within the target time interval;

[0014] The audio service data is subjected to audio recognition to obtain the corresponding audio-to-text data;

[0015] Image recognition is performed on the image service data to obtain the corresponding image-to-text data;

[0016] The financial business data is obtained by integrating the text business data, the audio-to-text data, and the image-to-text data.

[0017] According to some embodiments of this application, the financial business data further includes video business data, and before integrating the text business data, the audio-to-text data, and the image-to-text data to obtain the financial business data, the method further includes:

[0018] Extract audio tracks and video frame sequences from the video service data;

[0019] The audio tracks are subjected to audio recognition to obtain the track-specific speech-text data;

[0020] Image recognition is performed on the video frame sequence to obtain video frame character data;

[0021] The split-track speech text data is added to the audio-to-text converted data to obtain the updated audio-to-text converted data;

[0022] The video frame character data is added to the image-converted text data to obtain the updated image-converted text data;

[0023] The process of integrating the text business data, the audio-to-text data, and the image-to-text data to obtain the financial business data includes:

[0024] The financial business data is obtained by integrating the text business data, the updated audio-to-text data, and the updated image-to-text data.

[0025] According to some embodiments of this application, the target financial database includes multiple business data groups, each of which corresponds to a business priority.

[0026] The process of identifying sensitive information in the incremental financial data to obtain the identification result data includes:

[0027] Based on the order of service priorities from high to low, the data group identification order for each service data group is determined;

[0028] According to the data group identification order, sensitive information is identified for the incremental financial data in each of the business data groups to obtain the identification result data.

[0029] According to some embodiments of this application, after the step of identifying sensitive information in the incremental financial data and obtaining the identification result data, the method further includes:

[0030] The benchmark financial data is updated based on the incremental financial data to obtain the updated benchmark financial data;

[0031] The next time interval adjacent to the reference time interval is determined as the updated reference time interval;

[0032] In response to a new sensitive information identification request, new identification result data is determined for the new sensitive information identification request based on the updated benchmark financial data and the updated benchmark time interval.

[0033] According to some embodiments of this application, the step of determining incremental financial data based on difference detection between the target financial data and the benchmark financial data includes:

[0034] Extract multiple target field records from the target financial data, and extract multiple benchmark field records from the benchmark financial data;

[0035] The target field records and the baseline field records of the same type are compared to obtain the difference comparison records;

[0036] Based on the difference comparison records, determine the add, delete, and modify operation information that matches the benchmark financial data;

[0037] The incremental financial data is generated based on the information from the add, delete, and modify operations.

[0038] According to some embodiments of this application, the step of determining incremental financial data based on difference detection between the target financial data and the benchmark financial data includes:

[0039] Using the benchmark financial data as the key, a corresponding benchmark hash table is generated;

[0040] Extract multiple target field records from the target financial data, and use each target field record as a key to query the baseline hash table;

[0041] The incremental financial data is generated based on the target field records for which no matching item can be found in the baseline hash table.

[0042] An abnormal state handling method according to a second aspect embodiment of this application includes:

[0043] In response to the execution of the database sensitive information identification program, abnormal object information is detected in real time; wherein, the database sensitive information identification program is used to execute the database sensitive information identification method described in the first aspect embodiment;

[0044] In response to the detection of the abnormal object information, the abnormal state type is determined based on the abnormal object information;

[0045] Iterate through multiple preset alternative response procedures to determine the state response procedure that matches the abnormal state type;

[0046] The status response procedure is executed in response to an abnormal state that occurs in the database sensitive information identification procedure.

[0047] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the database sensitive information identification method as described in any one of the embodiments of the first aspect of this application.

[0048] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that is executed by a processor to implement the database sensitive information identification method as described in any one of the embodiments of the first aspect of this application.

[0049] The database sensitive information identification method, abnormal state handling method, and related apparatus according to the embodiments of this application have at least the following beneficial effects:

[0050] The database sensitive information identification method of this application requires first obtaining a sensitive information identification request; based on the sensitive information identification request, determining the target financial database; wherein the target financial database stores multiple financial business data; performing a full scan of the target financial database to obtain benchmark financial data; wherein the upload time of the benchmark financial data corresponds to a benchmark time interval; identifying the financial business data uploaded to the target financial database in the next adjacent time interval as the target financial data; determining incremental financial data based on the difference detection between the target financial data and the benchmark financial data; and performing sensitive information identification on the incremental financial data to obtain the identification result data. Therefore, the database sensitive information identification method can perform effective sensitive information scanning without interfering with normal business processes.

[0051] Furthermore, in response to the execution of the database sensitive information identification program, abnormal object information is detected in real time. The database sensitive information identification program is used to execute the database sensitive information identification method of the first aspect embodiment. In response to the detection of abnormal object information, an abnormal state type is determined based on the abnormal object information. Multiple preset alternative response programs are traversed to determine a state response program matching the abnormal state type. The state response program is executed to respond to the abnormal state occurring in the database sensitive information identification program. Based on this, the abnormal state handling method can effectively respond to abnormalities when they occur during the sensitive information identification process.

[0052] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0053] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0054] Figure 1 A flowchart illustrating the database sensitive information identification method provided in this application embodiment;

[0055] Figure 2 Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0056] Figure 3 Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0057] Figure 4 Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0058] Figure 5Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0059] Figure 6 Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0060] Figure 7 Another schematic diagram of the database sensitive information identification method provided in the embodiments of this application;

[0061] Figure 8 A flowchart illustrating the abnormal state handling method provided in the embodiments of this application;

[0062] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0063] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0064] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0065] In the description of this application, it should be understood that the orientation descriptions, such as up, down, left, right, front, and back, are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0066] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0067] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setting," "installation," and "connection" should be interpreted broadly. Those skilled in the art can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution. Furthermore, the identification of specific steps in the following text does not imply a limitation on the order of steps or execution logic. The execution order and logic between each step should be understood and inferred from the content described in the embodiments.

[0068] In the financial sector, the sensitive information stored in databases is of paramount importance, typically including highly sensitive data such as personally identifiable information and financial records. The security of this information directly impacts customer privacy and the reputation of financial institutions. To ensure the security of this sensitive information, financial institutions usually employ techniques such as full scans, incremental scans, and scheduled scans to regularly check the sensitive information in their databases.

[0069] A full scan is a technique that performs a one-time, comprehensive check on the entire database. It ensures that all data is reviewed, but this method consumes a lot of computing resources and time when processing large amounts of data, which may affect the normal operation of the database. Furthermore, because it is a one-time operation, it cannot capture dynamic changes in the database in real time.

[0070] Incremental scanning scans the database for newly added or modified data. This method is more efficient than full scanning because it only focuses on the changed parts, thus saving resources. However, incremental scanning may fail to detect sensitive information in older, unupdated data, posing a risk of omission.

[0071] Scheduled scanning involves scanning the database at fixed time intervals. While this method ensures regular checks, it also fails to achieve real-time monitoring, resulting in poor timeliness and the potential to miss sensitive information changes that occur between two scans.

[0072] Understandably, a full scan requires a comprehensive examination of the entire database to identify all potentially sensitive information. Incremental scans, on the other hand, require checking data that has been added or changed since the last scan to improve scanning efficiency. Scheduled scans, meanwhile, need to automatically execute scan tasks at preset time intervals to ensure continuous monitoring of sensitive information.

[0073] However, these technologies face some common challenges in implementation. First, ensuring both high timeliness and accuracy of scan results is technically difficult. Second, the scanning process may disrupt the daily operations of financial institutions; how to conduct effective scanning of sensitive information without interfering with normal business processes is a problem the industry needs to consider.

[0074] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a database sensitive information identification method, an abnormal state handling method, and related apparatus. The database sensitive information identification method can effectively scan for sensitive information without interfering with normal business processes, and the abnormal state handling method can effectively respond to abnormalities that occur during the sensitive information identification process.

[0075] The following explanation is based on the accompanying drawings.

[0076] Reference Figure 1 The database sensitive information identification method provided in the embodiments of this application may include, but is not limited to, the following steps S101 to S106.

[0077] Step S101: Obtain a sensitive information identification request;

[0078] Step S102: Based on the sensitive information identification request, determine the target financial database; wherein, the target financial database stores multiple financial business data;

[0079] Step S103: Perform a full scan of the target financial database to obtain benchmark financial data; wherein, the upload time of the benchmark financial data corresponds to the benchmark time interval.

[0080] Step S104: The financial business data uploaded to the target financial database from the next time interval adjacent to the base time interval is identified as the target financial data.

[0081] Step S105: Based on the difference detection between the target financial data and the benchmark financial data, determine the incremental financial data;

[0082] Step S106: Sensitive information is identified in the incremental financial data to obtain the identification result data.

[0083] The database sensitive information identification method of this application requires first obtaining a sensitive information identification request; based on the sensitive information identification request, determining the target financial database; wherein the target financial database stores multiple financial business data; performing a full scan of the target financial database to obtain benchmark financial data; wherein the upload time of the benchmark financial data corresponds to a benchmark time interval; identifying the financial business data uploaded to the target financial database in the next adjacent time interval as the target financial data; determining incremental financial data based on the difference detection between the target financial data and the benchmark financial data; and performing sensitive information identification on the incremental financial data to obtain the identification result data. Therefore, the database sensitive information identification method can perform effective sensitive information scanning without interfering with normal business processes.

[0084] In some embodiments, step S101 involves obtaining a sensitive information identification request. It should be noted that obtaining the sensitive information identification request aims to determine the purpose and scope of the entire identification process. It should be understood that a sensitive information identification request may be initiated for various reasons, such as compliance checks, security audits, data breach prevention, or responding to specific security incidents.

[0085] In some embodiments, the sensitive information identification request may include specific parameters and conditions, such as the type of sensitive information to be identified, the target financial database or dataset, and the time range for identification. This sensitive information identification request may be submitted manually or triggered automatically, such as at a specific time interval or after a specific event.

[0086] In some embodiments, step S102 involves determining a target financial database based on a sensitive information identification request; wherein the target financial database stores multiple financial business data. It should be noted that after obtaining the sensitive information identification request, the content of the request needs to be analyzed in detail to determine its specific requirements, which may include, but are not limited to, the business scope, data type, and time range of the sensitive information identification. It should be pointed out that these specific requirements will directly affect the selection of the target financial database. For example, if the request is for resource flow data within a specific time period, then the target financial database should contain financial business data within that time period.

[0087] In some embodiments, multiple candidate financial databases can be screened, and the most suitable financial database can be determined as the target financial database based on the specific content corresponding to the sensitive information identification request. This screening process may involve database access permission checks, data availability assessments, and data correlation analysis.

[0088] In some embodiments, step S103 involves performing a full scan of the target financial database to obtain benchmark financial data; the upload time of the benchmark financial data corresponds to a benchmark time interval. It should be noted that this application embodiment performs a full scan on a predetermined target financial database to read and analyze all data in the target financial database. It should be pointed out that the benchmark financial data is the result of the full scan, containing all financial business data uploaded to the target financial database within the benchmark time interval. The benchmark time interval is a specific time range used to define the data scope of the full scan, ensuring the timeliness and relevance of the data. The determination of this benchmark time interval can be based on various factors, such as compliance requirements, audit cycles, or specific business needs. It is important to clarify that obtaining benchmark financial data is crucial for subsequent sensitive information identification. This is because benchmark financial data provides a reference benchmark for comparison with data collected subsequently from the target financial database, providing a comprehensive and accurate data foundation for subsequent difference detection and sensitive information identification, laying a solid foundation for ensuring data security and compliance.

[0089] In some embodiments, step S104 identifies financial business data uploaded to the target financial database in the next adjacent time interval as target financial data. It should be noted that target financial data refers to financial business data uploaded to the target financial database within the time interval immediately following the base time interval. Because this target financial data is generated after the base time interval, it is considered changed or newly added data and is a key area for identifying sensitive information.

[0090] In some more specific embodiments, the determination of target financial data can be based on the timestamps of its upload, which must fall within the immediately following time interval of the base time interval. Such time definition ensures the relevance and timeliness of the target financial data, because only data within this time period is likely to contain new or changed sensitive information.

[0091] Reference Figure 2 According to some embodiments provided in this application, financial business data may include text business data, audio business data, and image business data. Step S104 determines the financial business data uploaded to the target financial database in the next adjacent time interval as the target financial data, which may include, but is not limited to:

[0092] Step S201: Query the text business data, audio business data, and image business data uploaded to the target financial database within the target time interval;

[0093] Step S202: Perform audio recognition on the audio service data to obtain the corresponding audio-to-text data;

[0094] Step S203: Perform image recognition on the image service data to obtain the corresponding image-to-text data;

[0095] Step S204: Integrate the text business data, audio-to-text data, and image-to-text data to obtain financial business data.

[0096] In the embodiments of this application, the method for identifying financial business data is not limited to traditional text data, but extends to audio and image data to achieve more comprehensive detection of sensitive information.

[0097] In some embodiments, step S201 involves querying text business data, audio business data, and image business data uploaded to the target financial database within a target time interval. It should be noted that this embodiment queries all financial business data uploaded to the target financial database within the target time interval, including text business data, audio business data, and image business data. This step ensures that various modalities potentially containing sensitive information are included in the detection scope.

[0098] In step S202 of some embodiments, audio recognition is performed on the audio service data to obtain corresponding audio-to-text data. It should be noted that the embodiments of this application perform audio recognition on the audio service data, using speech recognition technology to convert the speech content in the audio into text form, thus obtaining audio-to-text data. This makes audio data that was originally impossible to directly analyze into readable and analyzable.

[0099] In step S203 of some embodiments, image recognition is performed on the image service data to obtain corresponding image-to-text data. It should be noted that, for image service data, this application embodiment uses image recognition technology to extract text information from the image and convert it into analyzable text data, thus obtaining image-to-text data. This makes image data that was originally impossible to directly analyze readable and analyzable. In some embodiments, optical character recognition (OCR) technology or other image processing algorithms can be used to extract the text information contained in the image.

[0100] In step S204 of some embodiments, text business data, audio-to-text data, and image-to-text data are integrated to obtain financial business data. It should be noted that after obtaining the audio-to-text data and image-to-text data, they are integrated with the text business data to form a unified financial business dataset. This integrated dataset provides a foundation for subsequent sensitive information identification, enabling the embodiments of this application to apply a unified text analysis method to detect sensitive information. In this way, sensitive information in the data can be searched, identified, and labeled from this integrated dataset in subsequent steps. The entire process improves the comprehensiveness and accuracy of sensitive information detection and strengthens data security.

[0101] Reference Figure 3 According to some embodiments provided in this application, the financial business data also includes video business data. Before integrating the text business data, audio-to-text data, and image-to-text data in step S204 to obtain the financial business data, the process may further include:

[0102] Step S301: Extract audio tracks and video frame sequences from video service data;

[0103] Step S302: Perform audio recognition on the audio tracks to obtain the audio text data of each track;

[0104] Step S303: Perform image recognition on the video frame sequence to obtain video frame character data;

[0105] Step S304: Add the track-by-track speech text data to the audio-to-text data to obtain the updated audio-to-text data;

[0106] Step S305: Add the video frame character data to the image-to-text data to obtain the updated image-to-text data;

[0107] In step S204, the text business data, audio-to-text data, and image-to-text data are integrated to obtain financial business data, which may include, but is not limited to:

[0108] Step S306: Integrate the text business data, the updated audio-to-text data, and the updated image-to-text data to obtain financial business data.

[0109] In the embodiments provided in this application, the method for identifying financial business data is further extended to video business data to achieve more comprehensive sensitive information detection. This process adds several key steps to the original text, audio, and image data recognition to ensure that sensitive information in video content can also be effectively identified.

[0110] In some embodiments, step S301 involves extracting audio tracks and video frame sequences from the video service data. It should be noted that this embodiment extracts audio tracks and video frame sequences from the video service data. This step involves splitting the video content into two independent parts: audio and images, so that they can be processed separately.

[0111] In step S302 of some embodiments, audio recognition is performed on the audio tracks to obtain track-specific speech-text data. It should be noted that the embodiments of this application perform audio recognition on the extracted audio tracks to utilize speech recognition technology to convert the speech content in the audio into text data, thus obtaining track-specific speech-text data. This allows the speech information in the video to be converted into an analyzable text format.

[0112] In step S303 of some embodiments, image recognition is performed on the video frame sequence to obtain video frame character data. It should be noted that the image recognition of the video frame sequence in this embodiment can use OCR technology or other image recognition methods to extract text or important information from each frame to obtain video frame character data. This step converts the static image content in the video into text, increasing the readability of the data.

[0113] In step S304 of some embodiments, the segmented speech text data is added to the audio-to-text conversion data to obtain updated audio-to-text conversion data. It should be noted that in this embodiment, the segmented speech text data is added to the original audio-to-text conversion data to obtain updated audio-to-text conversion data. Integrating the video content recognition results into the existing audio dataset lays the foundation for subsequent unified processing.

[0114] In step S305 of some embodiments, video frame character data is added to the image-to-text data to obtain updated image-to-text data. It should be noted that video frame character data is also added to the image-to-text data to obtain updated image-to-text data. Integrating the video content recognition results into the existing image dataset lays the foundation for subsequent unified processing.

[0115] In step S306 of some embodiments, the text business data, the updated audio-to-text data, and the updated image-to-text data are integrated to obtain financial business data. It should be noted that the embodiments of this application integrate the text business data, the updated audio-to-text data, and the updated image-to-text data to obtain comprehensive financial business data. This integration process includes text data from various modal conversions; whether it is original text, text converted from audio or images, or text extracted from video content, all are gathered together to form a comprehensive dataset.

[0116] The embodiments of this application shown in steps S301 to S306 enable comprehensive sensitive information identification of financial business data across multiple modalities, including video. This not only improves the comprehensiveness and accuracy of data identification but also strengthens the monitoring and management capabilities of multimedia data, helping to better comply with data protection regulations, enhance data security, and protect customer privacy.

[0117] In some embodiments, step S105 involves performing difference detection between the target financial data and the benchmark financial data to determine the incremental financial data. It should be noted that detecting the difference between the target financial data and the benchmark financial data identifies incremental financial data that has changed since the benchmark time interval. This step determines which data requires further sensitive information analysis.

[0118] In some implementations, when performing difference detection, it is first necessary to ensure that the benchmark financial data and the target financial data are comparable. This means that both must be based on the same data structure and format in order to accurately identify the differences between them. Next, difference detection algorithms can be applied to compare the direct differences between the benchmark and target financial data, identifying the discrepancies between the two datasets. These difference detection algorithms can include techniques such as hash comparison, timestamp comparison, and data version control.

[0119] It should be clarified that the results of discrepancy detection between the target financial data and the benchmark financial data will generate incremental financial data. This incremental financial data can include all records that exist in the target financial data but not in the benchmark financial data, as well as records whose content is inconsistent between the two datasets. Incremental financial data may represent newly created records, modified existing records, or even records that were deleted and then re-added.

[0120] In addition, after the difference detection is completed, these incremental financial data, which contain all changes since the base time, are more likely to contain new or changed sensitive information and require detailed identification.

[0121] Reference Figure 4 According to some embodiments provided in this application, step S105 performs difference detection based on the target financial data and the benchmark financial data to determine the incremental financial data, which may include, but is not limited to:

[0122] Step S401: Extract multiple target field records from the target financial data and extract multiple benchmark field records from the benchmark financial data;

[0123] Step S402: Compare the target field records and the baseline field records of the same type to obtain the difference comparison records;

[0124] Step S403: Based on the difference comparison records, determine the add, delete, and modify operation information that matches the benchmark financial data;

[0125] Step S404: Generate incremental financial data based on the add, delete, and modify operation information.

[0126] In the embodiments provided in this application, the difference detection process is a key step in identifying incremental financial data. This process ensures that only data that has changed since the baseline time interval is labeled and processed, thereby improving efficiency and reducing unnecessary resource consumption.

[0127] In some embodiments, step S401 involves extracting multiple target field records from the target financial data and multiple benchmark field records from the benchmark financial data. It should be noted that in this application embodiment, multiple target field records are extracted from the target financial data. Here, the target financial data represents data uploaded to the target financial database in the subsequent time interval adjacent to the benchmark time interval, and the target field records are the field records that constitute the target financial data. Additionally, this application embodiment also extracts corresponding benchmark field records from the benchmark financial data. These records are obtained by performing a full scan of the financial business data uploaded within the benchmark time interval, resulting in the field records of the target financial database.

[0128] In step S402 of some embodiments, target field records and benchmark field records of the same type are compared to obtain a difference comparison record. It should be noted that in this embodiment, target field records and benchmark field records of the same type are compared. This comparison process allows for detailed field matching between the target field records and benchmark field records to determine which data items in the target financial database changed between two scans. It should be pointed out that the difference comparison record is used to list the differences between the target field records and the benchmark field records.

[0129] In some embodiments, step S403 involves determining addition, deletion, and modification operation information matching the benchmark financial data based on the difference comparison records. It should be noted that the embodiments of this application determine the addition, deletion, and modification operation information matching the benchmark financial data by analyzing the difference comparison field records. These embodiments can identify which field records are newly added and which field records have been deleted or modified. This operation information is crucial for understanding the nature of the data changes.

[0130] In some embodiments, step S404 generates incremental financial data based on add, delete, and modify operation information. It should be noted that this application embodiment generates incremental financial data based on add, delete, and modify operation information. This means that this application embodiment integrates the changed field records to form a dataset, which only contains data that has changed since a base time. This incremental financial data can then be used for various purposes, such as sensitive information identification, data synchronization, or audit trails.

[0131] The entire process is an automated and efficient data change detection workflow that optimizes resource utilization by accurately identifying changes and ensures that only relevant data is further processed. This approach not only improves the accuracy of data processing but also provides a mechanism for financial applications to quickly respond to data changes and maintain data integrity and security.

[0132] Reference Figure 5 According to some embodiments provided in this application, step S105 performs difference detection based on the target financial data and the benchmark financial data to determine the incremental financial data, which may include, but is not limited to:

[0133] Step S501: Using the benchmark financial data as the key, generate the corresponding benchmark hash table;

[0134] Step S502: Extract multiple target field records from the target financial data, and use each target field record as a key to query the benchmark hash table;

[0135] Step S503: Generate incremental financial data based on the target field records for which no matching item can be found in the baseline hash table.

[0136] In the embodiments provided in this application, the difference detection process is a precise and efficient step used to determine changes that have occurred since the creation of baseline financial data, thereby identifying incremental financial data. This process uses a hash table as the primary data structure to accelerate lookup and comparison operations.

[0137] In some embodiments, step S501 involves generating a corresponding benchmark hash table using benchmark financial data as the key. It should be noted that this embodiment uses benchmark financial data as the key to generate the corresponding benchmark hash table. This step requires converting each field record in the benchmark financial data into a hash value using a hash function and storing it in the hash table. The hash table provides fast data access capabilities, making subsequent query and comparison operations more efficient.

[0138] In step S502 of some embodiments, multiple target field records are extracted from the target financial data, and each target field record is used as a key to query a benchmark hash table. It should be noted that in this embodiment, multiple target field records are extracted from the target financial data. Each target field record represents data that may have changed since a benchmark time. This embodiment uses these target field records as keys to query an already generated benchmark hash table to check if they exist in the benchmark data.

[0139] In step S503 of some embodiments, incremental financial data is generated based on target field records for which no matching entries can be found in the baseline hash table. It should be noted that this embodiment generates incremental financial data based on target field records for which no matching entries can be found in the baseline hash table. This means that if a target field record does not have a corresponding entry in the baseline hash table, it will be considered a newly added or modified record because the original baseline data does not contain this record. These unmatched target field records constitute the incremental financial dataset, representing the changes that have occurred in the database since the last scan.

[0140] The embodiments of this application shown in steps S501 to S503 can quickly identify addition, modification, or deletion operations in a target financial database without repeatedly performing a full scan of the entire database. This not only improves data processing efficiency but also reduces resource consumption during sensitive information identification, making the identification process faster and more accurate.

[0141] In some embodiments, step S106 involves identifying sensitive information in the incremental financial data to obtain identification result data. It should be noted that identifying sensitive information in the incremental financial data aims to accurately discover and mark all sensitive information within the incremental financial data, thereby obtaining the identification result data.

[0142] In some embodiments, incremental financial data can be analyzed using algorithms and rule sets. These algorithms and rule sets can be based on pattern matching, regular expressions, machine learning models, or a series of predefined sensitive information types to scan each record in the dataset and identify fields or data fragments that may contain sensitive information. During this identification process, embodiments of this application need to ensure high accuracy and low false positive rate as much as possible to avoid incorrectly marking non-sensitive information as sensitive information, while also ensuring that no truly sensitive information is overlooked.

[0143] It should be emphasized that, in this application embodiment, when identifying sensitive information in the target financial database, the sensitive information is directly identified in the financial business data uploaded in the next time interval adjacent to the base time interval. This allows for the accurate identification of incremental financial data by recognizing data changes, providing a basis for the discovery and protection of sensitive information, thereby achieving a relatively accurate result in sensitive information identification.

[0144] In some more specific embodiments, the process of identifying sensitive information in incremental financial data can be implemented using various technical means to ensure the accuracy and effectiveness of the identification results. The following are some examples of possible implementation methods:

[0145] Pattern matching: Uses predefined patterns or templates to identify sensitive information in data. For example, card numbers often follow a specific format (such as 16 digits), and card information can be quickly identified by matching this pattern.

[0146] Regular expressions: Use regular expressions to define complex search patterns, which can be used to identify various types of sensitive information, such as phone numbers and email addresses.

[0147] Machine learning models: Training machine learning models to identify sensitive information. By using a large amount of labeled sensitive and non-sensitive data as a training set, the model can learn the characteristics of sensitive information and use them to identify new, unknown data.

[0148] Natural Language Processing (NLP): Using NLP techniques to analyze textual information in a dataset and identify natural language expressions that may contain sensitive information.

[0149] Rule set: A set of rules defined based on specific financial business logic or regulatory requirements to identify sensitive information. For example, certain financial resource transfers may be flagged as sensitive if the amount exceeds a certain threshold.

[0150] Data classification: Divide the data into different categories, such as personal data, resource flow data, etc., and then apply specific sensitive information identification strategies for each category.

[0151] Anomaly detection: By analyzing the statistical characteristics of data, we can identify data points that do not conform to the usual patterns, which may be potentially sensitive information.

[0152] Reference Figure 6 According to some embodiments provided in this application, the target financial database includes multiple business data groups, each business data group corresponding to a business priority. Step S106 identifies sensitive information in the incremental financial data to obtain identification result data, which may include, but is not limited to:

[0153] Step S601: Determine the data group identification order for each business data group based on the order of business priority from high to low.

[0154] Step S602: According to the data group identification order, sensitive information is identified for incremental financial data in each business data group to obtain identification result data.

[0155] In the embodiments provided in this application, the target financial database contains multiple business data groups, each associated with a specific business priority. This reflects that in some embodiments, different financial business data differ in importance and urgency, thus requiring different processing strategies.

[0156] In some embodiments, step S601 determines the data group identification order for each business data group based on a descending order of business priority. It should be noted that this application embodiment determines the identification order of each business data group based on a descending order of business priority. This step determines which data groups should be processed first. For example, data groups that may involve sensitive customer information or high-risk transactions may be given higher priority to ensure that this data is reviewed promptly and carefully.

[0157] In step S602 of some embodiments, sensitive information is identified for incremental financial data in each business data group according to the data group identification order, resulting in identification result data. It should be noted that this embodiment identifies sensitive information for incremental financial data in each business data group according to a determined data group identification order. This means that this embodiment processes the new or changed data in each business data group sequentially according to priority, and for each business data group, it applies appropriate identification algorithms or technologies to detect and mark sensitive information. It should be pointed out that the identification of sensitive information for incremental financial data in each business data group will generate identification result data. This identification result data can be used to meet various needs, such as risk assessment, compliance reporting, security monitoring, or customer service improvement. Through this method, this embodiment can manage and protect sensitive data more accurately and efficiently.

[0158] As illustrated in steps S601 to S602, this embodiment of the present application, through this hierarchical and priority-driven processing method, ensures that high-priority business data is processed first, thereby enabling rapid response to potential risks or compliance issues. Simultaneously, this method improves the efficiency of the identification process because it allows the embodiment to concentrate computing resources on the most important business data groups. Furthermore, this priority-based identification order helps balance the performance and responsiveness of the embodiment. By prioritizing the processing of the most important data, the embodiment can maximize its effectiveness with limited computing resources while ensuring that all business data groups ultimately receive appropriate sensitive information identification.

[0159] In some more specific embodiments, within the target financial database, various business data groups can be prioritized based on the usage environment and importance of the financial business data. These business data groups may include production business data groups, disaster recovery business data groups, warm backup business data groups, local backup business data groups, and remote backup business data groups, etc. Each type of business data group can be assigned different priorities based on its impact on business continuity and data security.

[0160] First, this embodiment of the application performs a one-time full scan, that is, a comprehensive scan of all business data groups in the target financial database to determine the benchmark financial data contained in each business data group. The benchmark financial data provides a reference for subsequent incremental scans.

[0161] Subsequently, this embodiment of the application performs real-time scanning according to the priority of each business data group, focusing on the parts that have changed since the last full scan, including newly added, deleted, or modified data. This incremental scanning can be implemented based on database synchronization technology, enabling timely capture and response to data changes.

[0162] If no changes are found in a high-priority business data group during the inspection, this embodiment will automatically switch to the next higher-priority business data group to continue the scanning task. This sequential inspection method not only improves scanning efficiency but also ensures that all business data groups receive appropriate attention and review.

[0163] Furthermore, this implementation method has the advantage of dynamically adjusting scanning resources based on business needs and the importance of business data groups. For example, more frequent and detailed scanning may be needed in production business data groups, while the frequency and depth of scanning may be reduced in other types of business data groups.

[0164] By employing this priority-driven scanning approach, the embodiments of this application can ensure the security and compliance of critical data while optimizing resource utilization. This approach is particularly suitable for environments with extremely high requirements for data security and business continuity, such as the financial services industry. It achieves comprehensive monitoring of sensitive information by intelligently allocating scanning tasks and resources, while maintaining minimal disruption to business operations.

[0165] Reference Figure 7 According to some embodiments provided in this application, after identifying sensitive information in incremental financial data in step S106 and obtaining the identification result data, the method may further include:

[0166] Step S701: Update the benchmark financial data based on the incremental financial data to obtain the updated benchmark financial data.

[0167] Step S702: Determine the next time interval adjacent to the reference time interval as the updated reference time interval;

[0168] Step S703: In response to receiving a new sensitive information identification request, new identification result data is determined for the new sensitive information identification request based on the updated benchmark financial data and the updated benchmark time interval.

[0169] In the embodiments provided in this application, the process of identifying sensitive information in incremental financial data is not a one-time task, but a continuous and dynamically updated cyclical process. After completing the identification of sensitive information, the system will further update the benchmark financial data and benchmark time interval to ensure the accuracy and timeliness of future identification work.

[0170] In some embodiments, step S701 involves updating the benchmark financial data based on the incremental financial data to obtain updated benchmark financial data. It should be noted that in this embodiment, the original benchmark financial data is updated based on the identified incremental financial data. This means that once this embodiment identifies newly added, deleted, or modified data items, these changes are integrated into the original benchmark financial data to generate updated benchmark financial data. This step ensures that the benchmark financial data stored in the target financial database is continuously updated, always reflecting the latest state of the target financial database.

[0171] In some embodiments, step S702 involves determining the next adjacent time interval as the updated reference time interval. It should be noted that in this embodiment, the reference time interval is updated to the next adjacent time interval. This is to prepare for the next sensitive information identification cycle. By updating the reference time interval, this embodiment ensures that in the next identification process, only changes occurring within the new reference time interval are considered.

[0172] In step S703 of some embodiments, in response to receiving a new sensitive information identification request, new identification result data is determined for the new sensitive information identification request based on updated benchmark financial data and updated benchmark time interval. It should be noted that when this application embodiment receives a new sensitive information identification request, it determines the new identification result data based on updated benchmark financial data and updated benchmark time interval. This means that each identification request is based on the latest benchmark data and time interval, thereby ensuring the accuracy and relevance of the identification results.

[0173] Through the embodiments of this application shown in steps S701 to S703, this continuous update and identification process enables continuous monitoring of sensitive information in the financial database and timely response to data changes. This approach not only improves data security and compliance but also enhances the responsiveness of the embodiments of this application to data changes, ensuring the continuity and effectiveness of data monitoring. Furthermore, this dynamic update mechanism reduces repetitive work and improves the efficiency of computing resource utilization.

[0174] Reference Figure 8 The abnormal state handling method provided in the embodiments of this application may include, but is not limited to:

[0175] Step S801: In response to the execution of the database sensitive information identification program, abnormal object information is detected in real time; wherein, the database sensitive information identification program is used to execute the database sensitive information identification method of any embodiment of this application;

[0176] Step S802: In response to detecting abnormal object information, determine the abnormal state type based on the abnormal object information;

[0177] Step S803: Traverse the multiple preset alternative response procedures to determine the status response procedure that matches the abnormal status type;

[0178] Step S804: Execute the status response procedure to respond to any abnormal status that occurs in the database sensitive information identification procedure.

[0179] In this embodiment, abnormal object information is detected in real time during the execution of the database sensitive information identification program. This process involves continuous monitoring of the program execution process to quickly identify any abnormal situations that do not meet expectations. These abnormal situations may include potential security threats or system failures. Once abnormal object information is detected, this embodiment determines the corresponding abnormal state type during program execution based on this abnormal object information. This is achieved by analyzing the nature and potential impact of the abnormality, thereby ensuring that the response measures taken are targeted and effective.

[0180] Furthermore, in this embodiment, multiple preset alternative response procedures are traversed. This step is to find the most suitable response procedure for the current abnormal state type. These response procedures can range from simple logging to complex recovery operations as described in this embodiment.

[0181] Furthermore, the selected status response procedure is executed to effectively address abnormal states occurring during the database sensitive information identification process. Based on this, the abnormal state handling method of this application embodiment can effectively respond to abnormalities that occur during the sensitive information identification process, thereby maintaining the stability and data integrity of this application embodiment. In the financial field, the application of this abnormal state handling method is particularly important because the security of financial data is directly related to the security of financial resources.

[0182] In some more specific embodiments, the abnormal state type may include several main types, such as network error, database connection error, and data processing error.

[0183] At the implementation level, embodiments of this application can prevent program crashes due to errors through exception handling mechanisms in the code. For example, this can be achieved using try-catch blocks. The try block contains code that might throw exceptions, while the catch block contains handling logic for specific exception types.

[0184] This application's embodiments construct a comprehensive error handling framework through measures such as error classification, anomaly capture, logging, notification sending, and manual intervention. This framework aims to improve system stability and reliability, ensuring that even in the event of errors, the impact on users and the system can be minimized.

[0185] Reference Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0186] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0187] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to perform the database sensitive information identification method of the embodiments of this application, or to perform the abnormal state handling method of the embodiments of this application.

[0188] The input / output interface 903 is used to implement information input and output;

[0189] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0190] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0191] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0192] This application also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, causing the computer device to perform the database sensitive information identification method described above.

[0193] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “including,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0194] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, which can include, but is not limited to, any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0195] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0196] In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0199] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and may include, but is not limited to, several instructions used to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0200] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0201] The above is a detailed description of the embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.

Claims

1. A method for identifying sensitive information in a database, characterized in that, include: Request to obtain sensitive information identification; Based on the sensitive information identification request, a target financial database is determined; wherein, the target financial database stores multiple business data groups, each business data group includes multiple financial business data, and each business data group corresponds to a business priority. A full scan of the target financial database is performed to obtain benchmark financial data; wherein the upload time of the benchmark financial data corresponds to a benchmark time interval. The financial business data uploaded to the target financial database in the next time interval adjacent to the reference time interval is identified as the target financial data. Based on the difference detection between the target financial data and the benchmark financial data, incremental financial data is determined. Based on the order of service priorities from high to low, the data group identification order for each service data group is determined; According to the data group identification order, sensitive information is identified for the incremental financial data in each business data group to obtain identification result data; wherein, the database sensitive information identification method is executed by the database sensitive information identification program; In response to the execution of the database sensitive information identification program, abnormal object information is detected in real time; In response to the detection of abnormal object information, the abnormal state type is determined based on the abnormal object information; Iterate through multiple preset alternative response procedures to determine the state response procedure that matches the abnormal state type; The status response procedure is executed in response to an abnormal state that occurs in the database sensitive information identification procedure.

2. The method according to claim 1, characterized in that, The financial business data includes text business data, audio business data, and image business data. The financial business data uploaded to the target financial database from the next adjacent time interval of the reference time interval is defined as the target financial data, including: Query the text business data, audio business data, and image business data uploaded to the target financial database within the target time interval; The audio service data is subjected to audio recognition to obtain the corresponding audio-to-text data; Image recognition is performed on the image service data to obtain the corresponding image-to-text data; The financial business data is obtained by integrating the text business data, the audio-to-text data, and the image-to-text data.

3. The method according to claim 2, characterized in that, The financial business data also includes video business data. Before integrating the text business data, the audio-to-text data, and the image-to-text data to obtain the financial business data, the process further includes: Extract audio tracks and video frame sequences from the video service data; The audio tracks are subjected to audio recognition to obtain the track-specific speech-text data; Image recognition is performed on the video frame sequence to obtain video frame character data; The split-track speech text data is added to the audio-to-text converted data to obtain the updated audio-to-text converted data; The video frame character data is added to the image-converted text data to obtain the updated image-converted text data; The process of integrating the text business data, the audio-to-text data, and the image-to-text data to obtain the financial business data includes: The financial business data is obtained by integrating the text business data, the updated audio-to-text data, and the updated image-to-text data.

4. The method according to claim 1, characterized in that, After obtaining the identification result data, the following is also included: The benchmark financial data is updated based on the incremental financial data to obtain the updated benchmark financial data; The next time interval adjacent to the reference time interval is determined as the updated reference time interval; In response to a new sensitive information identification request, new identification result data is determined for the new sensitive information identification request based on the updated benchmark financial data and the updated benchmark time interval.

5. The method according to any one of claims 1 to 4, characterized in that, The step of determining incremental financial data by performing difference detection based on the target financial data and the benchmark financial data includes: Extract multiple target field records from the target financial data, and extract multiple benchmark field records from the benchmark financial data; The target field records and the baseline field records of the same type are compared to obtain the difference comparison records; Based on the difference comparison records, determine the add, delete, and modify operation information that matches the benchmark financial data; Based on the information from the add, delete, and modify operations, the incremental financial data is generated.

6. The method according to any one of claims 1 to 4, characterized in that, The step of determining incremental financial data by performing difference detection based on the target financial data and the benchmark financial data includes: Using the benchmark financial data as the key, a corresponding benchmark hash table is generated; Extract multiple target field records from the target financial data, and use each target field record as a key to query the baseline hash table; The incremental financial data is generated based on the target field records for which no matching item can be found in the baseline hash table.

7. An electronic device, characterized in that, include: The system includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the database sensitive information identification method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The storage medium stores a program, which is executed by a processor to implement the database sensitive information identification method as described in any one of claims 1 to 6.

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