Financial database processing analysis system with data protection function

Abstract

The invention relates to the technical field of financial database processing systems, and discloses a financial database processing and analyzing system with a data protection function, which comprises a system architecture design module, a data protection strategy module, a data processing and analyzing module and a security scale module. The invention relates to a financial database processing and analyzing system which improves performance and data processing capacity and ensures data safety and privacy. The system realizes high availability and stability through technologies of read-write separation, fault transfer, health examination and the like; data protection strategies such as dynamic desensitization, secondary authorization and encryption further guarantee data security; the data processing and analysis module is combined to enhance the data processing and analysis capability of financial services, and the security and scale module ensures the overall security of the system; compliance evaluation ensures that the system conforms to related laws and regulations; in short, the system provides an efficient, safe and compliant data processing analysis scheme for the financial industry.

Description

Technical Field

[0001] The invention relates to the technical field of financial database processing systems and discloses a financial database processing and analysis system with a data protection function. Background Art

[0002] As an information-intensive industry, the financial industry involves a large amount of sensitive personal and corporate information. According to relevant national laws and regulations, financial institutions must take effective measures to protect user data security and prevent data leakage, abuse or improper access. Once financial data is leaked, it may lead to financial risks and even affect the stability of the financial market. Through the data protection system, internal or external unauthorized access can be prevented, and fraud and operational risks can be reduced. Financial databases store users' personal information and transaction records. Building a system with data protection functions can effectively protect user privacy and avoid personal property losses and reputation damage. With the development of financial technology, financial institutions need to improve service quality and efficiency through innovative technologies. Data protection technology can enhance Customer trust, enhance the competitiveness and market position of institutions; financial business has extremely high requirements for the real-time and accuracy of data; data protection systems must not only ensure data security, but also ensure data availability and processing efficiency to support the daily operations and decision-making of financial institutions; with the development of technologies such as big data and cloud computing, the amount of data that financial institutions need to process and analyze is increasing. Building a system with data protection functions can help financial institutions make full use of these technologies for data analysis and tap into business value while ensuring security; network attacks and data leaks occur frequently, and financial institutions are facing increasingly severe external threats; data protection systems can enhance the ability of data to resist external attacks and protect key information from being illegally obtained; Therefore, building a financial database processing and analysis system with data protection function is of great significance for maintaining financial stability, protecting user interests and promoting financial technology innovation. Therefore, a financial database processing and analysis system with data protection function is proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a financial database processing and analysis system with data protection function, which adopts the present method to work, thereby solving the problem of low protection performance of traditional financial databases in the above background; To achieve the above-mentioned purpose, the present invention provides the following technical solution: it includes a system architecture design module, a data protection strategy module, a data processing and analysis module and a security compliance module; The system architecture design module includes database selection and database cluster, where: database selection is divided into database system and storage medium selection. When selecting a database system, priority should be given to systems that enjoy a high reputation in the industry and have rich security features, such as Oracle Database or Microsoft SQL Server Enterprise Edition; these systems have built-in advanced security features such as encryption and access control, providing a solid guarantee for data security; for those application scenarios that seek higher flexibility and scalability, open source databases such as PostgreSQL can be considered, and additional security extension packages can be integrated to meet specific security requirements; in terms of storage media, SSD (solid-state drive) is selected as the main storage medium to improve data read and write speed and system responsiveness; at the same time, consider using RAID (redundant array of independent disks) technology to increase data redundancy and prevent data loss due to single hard disk failure; The database cluster is divided into high-availability architecture and failover, wherein the high-availability architecture is a design that includes a cluster architecture of a master database server and multiple slave database servers; the master server is responsible for processing all write operations and most read operations, while the slave server provides read operation services by replicating the data of the master server, realizing read-write separation and improving system performance; the failover specifically means that when the master server fails, the system automatically switches the service to one of the slave servers to ensure service continuity; at the same time, all servers are regularly checked for health to promptly discover and eliminate potential problems; The data protection strategy module includes dynamic data desensitization, secondary authorization mechanism and data encryption; dynamic data desensitization is divided into desensitization strategy and desensitization engine. The desensitization strategy is to formulate a detailed desensitization strategy according to business needs and data sensitivity level; for highly sensitive data (such as customer ID number, bank card number, etc.), irreversible encryption or desensitization algorithm is used for processing; for sensitive data (such as customer name, address, etc.), reversible desensitization algorithm is used for processing so that it can be restored when needed; the desensitization engine is to develop or integrate a professional desensitization engine to realize automatic identification and desensitization processing of sensitive data; the desensitization engine supports multiple desensitization algorithms and custom desensitization rules to meet the needs of different scenarios; The secondary authorization mechanism includes permission management and secondary authorization. Permission management is to establish a strict permission management mechanism to manage the user's access rights in a refined manner. Different users are given different permissions according to their roles and responsibilities to ensure that each user can only access the data they need. Secondary authorization is to implement a secondary authorization mechanism for high-risk operations such as access to sensitive data and file export. Before performing these operations, users need to submit an application and go through the approval process, and can only perform the operation after approval. In the approval process, multiple levels of approvers can be set to ensure the fairness and rigor of the approval. Data encryption includes encryption algorithms and key management. The encryption algorithm specifically adopts strong encryption algorithms such as AES-256 to encrypt the stored and transmitted data. The selection of encryption algorithms should meet the national and industry security standards. Key management specifically establishes a key management system to manage the generation, storage, distribution and update of encryption keys throughout their life cycle. The key management system should support regular key replacement and backup and recovery functions to ensure the security and availability of keys.

[0004] As a preferred technical solution of the present invention, the data processing and analysis module is composed of data integration, data warehouse and real-time analysis; in terms of data integration, it mainly includes ETL tools and data cleaning modules; professional ETL tools (such as Talend, Informatica, etc.) are used to realize data integration of multiple data sources; ETL tools support automated processing of data extraction, conversion and loading processes to reduce the risk of manual intervention and errors; data cleaning work is performed during the data integration process, including removing duplicate data, correcting erroneous data, filling missing data, etc.; the cleaned data will be stored in the data warehouse for subsequent analysis.

[0005] As a preferred technical solution of the present invention, the data warehouse specifically includes a storage structure and index optimization; wherein the storage structure is specifically a reasonably designed data warehouse storage structure to store the cleaned data; the data warehouse should support advanced data structures such as multidimensional data models and data cubes to facilitate complex queries and data analysis; index optimization is specifically to optimize the indexes of key fields in the data warehouse to improve query performance; the selection of indexes should be customized according to the query mode and data distribution characteristics to ensure the best query performance.

[0006] As the preferred technical solution of the present invention, the real-time analysis is specifically real-time data processing and machine learning applications, wherein the real-time data processing specifically adopts real-time data stream processing systems such as Apache Kafka to realize the collection and processing of real-time data; Kafka supports high-throughput, low-latency data transmission and processing capabilities to meet the needs of real-time data analysis; the specific content of machine learning application is to combine machine learning technology to carry out data mining and trend forecasting; use machine learning algorithms to analyze and train models for large amounts of historical data to discover potential business rules and risk points and provide strong support for business decisions.

[0007] As a preferred technical solution of the present invention, the security compliance module specifically includes access control, auditing and monitoring, and regulatory compliance; the main contents of access control include RBAC model and session management; the RBAC model specifically implements a role-based access control (RBAC) model to manage user access rights; the RBAC model associates users with roles and associates roles with rights to achieve refined management of user access rights; the specific content of session management is to establish a session management mechanism to track and manage user login session information; sessions that have been inactive for a long time will be automatically logged out to prevent unauthorized access.

[0008] As the preferred technical solution of the present invention, the audit and monitoring specifically include diary recording and security monitoring; the specific content of the diary recording is to record all sensitive operations in detail and save them in log files for subsequent audit and traceability; the log file should contain key information such as operation time, operator, operation content, etc. to ensure the accuracy and effectiveness of the audit work; the specific content of the security monitoring is to deploy a security monitoring system to monitor the system's operating status and security event information in real time; the security monitoring system should support detection and alarm functions for abnormal behavior in order to promptly discover and deal with potential security threats.

[0009] As a preferred technical solution of the present invention, the regulatory compliance principle requires regular compliance assessments of the system to ensure that the system meets various standards such as national and industry laws and regulations, data protection policies, data processing procedures, and security measures; for any issues identified that do not meet regulatory standards, corrective measures must be taken promptly, and the rectification process and its results must be recorded in detail.

[0010] 1. Improve data security: By implementing dynamic data desensitization, secondary authorization mechanism and data encryption and other measures, we can ensure the security of sensitive data during storage, transmission and use, and effectively prevent data leakage and abuse.

[0011] 2. Optimize data processing efficiency: Use advanced ETL tools and data cleaning modules to achieve efficient data integration and cleaning, reduce manual intervention, and improve the accuracy and efficiency of data processing.

[0012] 3. Improve data analysis capabilities: By building a data warehouse and implementing real-time analysis, we support complex queries and data analysis, and combine machine learning technology to provide strong data support for business decisions.

[0013] 4. Strengthen system security: Ensure system security and compliance by implementing access control, auditing and monitoring, and regulatory compliance measures to effectively prevent unauthorized access and potential security threats.

[0014] 5. Improve user satisfaction: Through refined permission management and secondary authorization mechanism, ensure that users can easily access the required data, while ensuring data security and improving user satisfaction.

[0015] 6. Reduce operating costs: By automating and optimizing data processing processes, the risk of manual intervention and errors can be reduced, thereby reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an overall flow chart of a financial database processing and analysis system with data protection function of the present invention; Figure 2 A flow chart of system architecture design in a financial database processing and analysis system with data protection function of the present invention; Figure 3 A data protection strategy flow chart of a financial database processing and analysis system with data protection function according to the present invention; Figure 4 A flowchart of data processing and analysis in a financial database processing and analysis system with data protection function of the present invention; Figure 5 This is a safety compliance flow chart of a financial database processing and analysis system with data protection function according to the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of the invention.

[0018] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings. Example 1

[0019] comprehensive Figure 1-5 The present invention provides a technical solution: a financial database processing and analysis system with data protection function, including the following main contents: It includes system architecture design module, data protection strategy module, data processing and analysis module and security compliance module; The system architecture design module includes database selection and database cluster, where: database selection is divided into database system and storage medium selection. When selecting a database system, priority should be given to systems that enjoy a high reputation in the industry and have rich security features, such as Oracle Database or Microsoft SQL Server Enterprise Edition; these systems have built-in advanced security features such as encryption and access control, providing a solid guarantee for data security; for those application scenarios that seek higher flexibility and scalability, open source databases such as PostgreSQL can be considered, and additional security extension packages can be integrated to meet specific security requirements; in terms of storage media, SSD (solid-state drive) is selected as the main storage medium to improve data read and write speed and system responsiveness; at the same time, consider using RAID (redundant array of independent disks) technology to increase data redundancy and prevent data loss due to single hard disk failure; The database cluster is divided into high-availability architecture and failover, wherein the high-availability architecture is a design that includes a cluster architecture of a master database server and multiple slave database servers; the master server is responsible for processing all write operations and most read operations, while the slave server provides read operation services by replicating the data of the master server, realizing read-write separation and improving system performance; the failover specifically means that when the master server fails, the system automatically switches the service to one of the slave servers to ensure service continuity; at the same time, all servers are regularly checked for health to promptly discover and eliminate potential problems; The data protection strategy module includes dynamic data desensitization, secondary authorization mechanism and data encryption; dynamic data desensitization is divided into desensitization strategy and desensitization engine. The desensitization strategy is to formulate a detailed desensitization strategy according to business needs and data sensitivity level; for highly sensitive data (such as customer ID number, bank card number, etc.), irreversible encryption or desensitization algorithm is used for processing; for sensitive data (such as customer name, address, etc.), reversible desensitization algorithm is used for processing so that it can be restored when needed; the desensitization engine is to develop or integrate a professional desensitization engine to realize automatic identification and desensitization processing of sensitive data; the desensitization engine supports multiple desensitization algorithms and custom desensitization rules to meet the needs of different scenarios; The secondary authorization mechanism includes permission management and secondary authorization. Permission management is to establish a strict permission management mechanism to manage the user's access rights in a refined manner. Different users are given different permissions according to their roles and responsibilities to ensure that each user can only access the data they need. Secondary authorization is to implement a secondary authorization mechanism for high-risk operations such as access to sensitive data and file export. Before performing these operations, users need to submit an application and go through the approval process, and can only perform the operation after approval. In the approval process, multiple levels of approvers can be set to ensure the fairness and rigor of the approval. Data encryption includes encryption algorithms and key management. The encryption algorithm is to use strong encryption algorithms such as AES-256 to encrypt the stored and transmitted data. The selection of encryption algorithms should meet the requirements of national and industry security standards. Key management is to establish a key management system to manage the generation, storage, distribution and update of encryption keys throughout their life cycle. The key management system should support regular key replacement and backup and recovery functions to ensure the security and availability of keys. The data processing and analysis module consists of data integration, data warehouse and real-time analysis. In terms of data integration, it mainly includes ETL tools and data cleaning modules. Professional ETL tools (such as Talend, Informatica, etc.) are used to achieve data integration of multiple data sources. ETL tools support automated processing of data extraction, conversion and loading processes to reduce the risk of manual intervention and errors. Data cleaning work is performed during the data integration process, including removing duplicate data, correcting erroneous data, filling missing data, etc. The cleaned data will be stored in the data warehouse for subsequent analysis. The data warehouse specifically includes storage structure and index optimization; the storage structure specifically refers to a reasonably designed data warehouse storage structure to store cleaned data; the data warehouse should support advanced data structures such as multidimensional data models and data cubes to facilitate complex queries and data analysis; index optimization specifically refers to index optimization of key fields in the data warehouse to improve query performance; the selection of indexes should be customized according to query patterns and data distribution characteristics to ensure optimal query performance; The real-time analysis specifically refers to real-time data processing and machine learning applications, where the real-time data processing specifically uses real-time data stream processing systems such as Apache Kafka to realize the collection and processing of real-time data; Kafka supports high-throughput, low-latency data transmission and processing capabilities to meet the needs of real-time data analysis; the specific content of machine learning applications is to combine machine learning technology to carry out data mining and trend forecasting; use machine learning algorithms to analyze and train models for large amounts of historical data to discover potential business rules and risk points and provide strong support for business decisions. Example 2

[0020] The security compliance module specifically includes access control, audit and monitoring, and regulatory compliance; the main contents of access control include RBAC model and session management; the RBAC model specifically implements the role-based access control (RBAC) model to manage user access rights; the RBAC model associates users with roles and roles with rights to achieve refined management of user access rights; the specific contents of session management are to establish a session management mechanism to track and manage user login session information; sessions that have been inactive for a long time will be automatically logged out to prevent unauthorized access; The audit and monitoring specifically include diary records and security monitoring; the specific content of diary records is to record all sensitive operations in detail and save them in log files for subsequent audit and traceability; the log files should contain key information such as operation time, operator, operation content, etc. to ensure the accuracy and effectiveness of the audit work; the specific content of security monitoring is to deploy a security monitoring system to monitor the system's operating status and security event information in real time; the security monitoring system should support detection and alarm functions for abnormal behavior in order to promptly discover and deal with potential security threats. Example 3

[0021] The regulatory compliance principle requires that compliance assessments be performed on the system regularly to ensure that the system meets national and industry laws and regulations, data protection policies, data processing procedures, security measures and other standards; for any issues identified that do not meet regulatory standards, corrective measures must be taken promptly and the rectification process and its results must be recorded in detail.

[0022] In summary, the invention proposes a financial database processing and analysis system with data protection function: it aims to provide efficient and secure data processing and analysis capabilities to meet the strict requirements of the financial industry for data protection and system performance; the system ensures the high availability and stability of the system through technical means such as read-write separation, failover, and health check; at the same time, through data protection strategies such as dynamic data desensitization, secondary authorization mechanism and data encryption, it effectively guarantees the security and privacy of data; In the data processing and analysis module, the organic combination of data integration, data warehouse and real-time analysis provides powerful data processing and analysis capabilities for financial services. Data integration achieves efficient and accurate data integration through ETL tools and data cleaning modules. Data warehouse provides a solid foundation for storage structure and index optimization, supporting complex query and data analysis. Real-time analysis uses real-time data stream processing systems and machine learning technology to provide financial services with real-time data processing and trend prediction capabilities. The security compliance module ensures the overall security of the system; access control achieves refined management of user access rights through the RBAC model and session management; audit and monitoring ensure real-time monitoring of the system's operating status and security event information through log records and security monitoring; regulatory compliance ensures that the system meets national and industry legal and regulatory requirements through regular compliance assessments; The present invention relates to a financial database processing and analysis system, which improves performance and data processing capabilities and ensures data security and privacy; the system achieves high availability and stability through technologies such as read-write separation, failover and health check; data protection strategies such as dynamic desensitization, secondary authorization and encryption further ensure data security; the combination of data processing and analysis modules enhances the data processing and analysis capabilities of financial services, while the security and compliance module ensures the overall security of the system; compliance assessment ensures that the system complies with relevant laws and regulations; in short, the system provides an efficient, secure and compliant data processing and analysis solution for the financial industry.

[0023] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0024] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A financial database processing and analysis system with data protection function, characterized in that: It includes system architecture design module, data protection strategy module, data processing and analysis module and security compliance module; The system architecture design module includes database selection and database cluster, where: database selection is divided into database system and storage medium selection. When selecting a database system, priority should be given to systems that enjoy a high reputation in the industry and have rich security features, such as Oracle Database or Microsoft SQL Server Enterprise Edition; these systems have built-in advanced security features such as encryption and access control, providing a solid guarantee for data security; for those application scenarios that seek higher flexibility and scalability, open source databases such as PostgreSQL can be considered, and additional security extension packages can be integrated to meet specific security requirements; in terms of storage media, SSD (solid-state drive) is selected as the main storage medium to improve data read and write speed and system responsiveness; at the same time, consider using RAID (redundant array of independent disks) technology to increase data redundancy and prevent data loss due to single hard disk failure; The database cluster is divided into high-availability architecture and failover, wherein the high-availability architecture is a design that includes a cluster architecture of a master database server and multiple slave database servers; the master server is responsible for processing all write operations and most read operations, while the slave server provides read operation services by replicating the data of the master server, realizing read-write separation and improving system performance; the failover specifically means that when the master server fails, the system automatically switches the service to one of the slave servers to ensure service continuity; at the same time, all servers are regularly checked for health to promptly discover and eliminate potential problems; The data protection strategy module includes dynamic data desensitization, secondary authorization mechanism and data encryption; dynamic data desensitization is divided into desensitization strategy and desensitization engine. The desensitization strategy is to formulate a detailed desensitization strategy according to business needs and data sensitivity level; for highly sensitive data (such as customer ID number, bank card number, etc.), irreversible encryption or desensitization algorithm is used for processing; for sensitive data (such as customer name, address, etc.), reversible desensitization algorithm is used for processing so that it can be restored when needed; the desensitization engine is to develop or integrate a professional desensitization engine to realize automatic identification and desensitization processing of sensitive data; the desensitization engine supports multiple desensitization algorithms and custom desensitization rules to meet the needs of different scenarios; The secondary authorization mechanism includes permission management and secondary authorization. Permission management is to establish a strict permission management mechanism to manage the user's access rights in a refined manner. Different users are given different permissions according to their roles and responsibilities to ensure that each user can only access the data they need. Secondary authorization is to implement a secondary authorization mechanism for high-risk operations such as access to sensitive data and file export. Before performing these operations, users need to submit an application and go through the approval process, and can only perform the operation after approval. In the approval process, multiple levels of approvers can be set to ensure the fairness and rigor of the approval. Data encryption includes encryption algorithms and key management. The encryption algorithm specifically adopts strong encryption algorithms such as AES-256 to encrypt the stored and transmitted data. The selection of encryption algorithms should meet the national and industry security standards. Key management specifically establishes a key management system to manage the generation, storage, distribution and update of encryption keys throughout their life cycle. The key management system should support regular key replacement and backup and recovery functions to ensure the security and availability of keys.

2. The financial database processing and analysis system with data protection function according to claim 1, characterized in that: The data processing and analysis module consists of data integration, data warehouse and real-time analysis. In terms of data integration, it mainly includes ETL tools and data cleaning modules. Professional ETL tools (such as Talend, Informatica, etc.) are used to achieve data integration of multiple data sources. ETL tools support automated processing of data extraction, conversion and loading processes to reduce the risk of manual intervention and errors. Data cleaning is performed during the data integration process, including removing duplicate data, correcting erroneous data, filling in missing data, etc. The cleaned data will be stored in the data warehouse for subsequent analysis.

3. The financial database processing and analysis system with data protection function according to claim 2, characterized in that: The data warehouse specifically includes storage structure and index optimization; the storage structure specifically is a reasonably designed data warehouse storage structure to store cleaned data; the data warehouse should support advanced data structures such as multidimensional data models and data cubes to facilitate complex queries and data analysis; index optimization specifically is to optimize the index of key fields in the data warehouse to improve query performance; the selection of indexes should be customized according to the query mode and data distribution characteristics to ensure the best query performance.

4. The financial database processing and analysis system with data protection function according to claim 3 is characterized in that: The real-time analysis specifically refers to real-time data processing and machine learning applications, wherein the real-time data processing specifically refers to the use of real-time data stream processing systems such as Apache Kafka to realize the collection and processing of real-time data; Kafka supports high-throughput, low-latency data transmission and processing capabilities to meet the needs of real-time data analysis; The specific content of machine learning applications is to combine machine learning technology to carry out data mining and trend forecasting; use machine learning algorithms to analyze and train models on large amounts of historical data to discover potential business rules and risk points and provide strong support for business decisions.

5. The financial database processing and analysis system with data protection function according to claim 4 is characterized in that: The security compliance module specifically includes access control, auditing and monitoring, and regulatory compliance; the main contents of access control include RBAC model and session management; the RBAC model specifically implements a role-based access control (RBAC) model to manage user access rights; the RBAC model associates users with roles and associates roles with permissions to achieve refined management of user access rights; the specific content of session management is to establish a session management mechanism to track and manage user login session information; sessions that have been inactive for a long time will be automatically logged out to prevent unauthorized access.

6. The financial database processing and analysis system with data protection function according to claim 5, characterized in that: The audit and monitoring specifically include diary records and security monitoring; the specific content of diary records is to record all sensitive operations in detail and save them in log files for subsequent audit and traceability; the log files should contain key information such as operation time, operator, operation content, etc. to ensure the accuracy and effectiveness of the audit work; the specific content of security monitoring is to deploy a security monitoring system to monitor the system's operating status and security event information in real time; the security monitoring system should support detection and alarm functions for abnormal behavior in order to promptly discover and deal with potential security threats.

7. The financial database processing and analysis system with data protection function according to claim 6 is characterized by: The regulatory compliance principle requires that compliance assessments be performed on the system regularly to ensure that the system meets national and industry laws and regulations, data protection policies, data processing procedures, security measures and other standards; for any issues identified that do not meet regulatory standards, corrective measures must be taken promptly and the rectification process and its results must be recorded in detail.

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