User real-name information protection method and system oriented to competition reservation software

Abstract

The invention relates to the technical field of information security protection, and discloses a user real-name information protection method and system for competition appointment software. The method comprises the following steps: firstly, obtaining real-name information such as a user name, an identity card number and a contact way, and preprocessing to obtain desensitization preparation data; extracting sensitive features and determining distribution information, and combining a preset privacy constraint to generate an initial protection strategy; prediction feature distribution information and prediction use data are obtained through trend prediction, compensation optimization is carried out on the initial strategy according to the prediction feature distribution information and the prediction use data, and an optimization protection strategy is generated; after the strategy is applied, the protection effect is simulated and adjusted through reverse deduction to obtain a final protection strategy, and the final protection strategy is output to competition appointment software to be executed. According to the method, through multi-link processing and dynamic optimization, the real-name information security of the user is effectively protected, the data use requirements of each link of competition appointment are adapted, and the protection precision and flexibility are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security protection, in particular to a user real-name information protection method and system for event reservation software. BACKGROUND

[0002] With the vigorous development of sports event industry, various types of event reservation software have emerged, greatly facilitating the process of user registration and participation in events. In order to ensure the authenticity of event participants, maintain the order of events and meet the relevant management regulations, such software usually requires users to provide real-name information, including name, ID number, contact information and other key content. However, these user real-name information is highly sensitive data, once leaked, misused or tampered with, not only will it cause serious invasion of the user's personal privacy, but also may cause security problems such as fraud and identity theft, thereby affecting the user's trust in the event reservation software.

[0003] At present, there are some conventional means for user information protection, such as simple encryption storage of data or partial hiding of key information when displayed. However, in the context of event reservation, these methods have obvious limitations. Data interaction involving multiple links in the process of event organization, such as registration information review, ticket management, on-site verification, etc., simple protection measures are difficult to balance information security and efficient coordination of each link; the existing methods are mostly static protection strategies, which cannot dynamically adjust the protection strength according to the use frequency, sensitivity of user information and potential security threats, resulting in poor protection effect when facing new network attacks or complex data use scenarios.

[0004] The user scale of event reservation software often fluctuates greatly with the popularity of events. When the user volume surges, the data processing pressure increases, and if the information protection strategy is not reasonably designed, it may affect the efficiency of the software, and even cause system failure. At the same time, there are differences in laws and regulations for personal information protection in different regions, and if the event reservation software involves cross-regional operation, the existing information protection methods may not meet the compliance requirements of different regions, increasing the legal risk of software operation. Therefore, how to build a user real-name information protection method that takes into account security, flexibility, efficiency and compliance in the context of event reservation has become a problem to be solved. SUMMARY

[0005] The purpose of the present application is to provide a user real-name information protection method and system for event reservation software to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides a user real-name information protection method for event reservation software, which comprises: obtaining user real-name information, the user real-name information including user name, ID number and contact information; preprocessing the user real-name information to obtain desensitization preparation data; extracting sensitive features of the desensitization preparation data to determine sensitive feature distribution information; generating an initial protection strategy based on preset privacy constraint information and in combination with the sensitive feature distribution information; performing trend prediction on the sensitive feature distribution information and usage record data to obtain predicted feature distribution information and predicted usage data; performing compensation optimization on the initial protection strategy based on the predicted feature distribution information and the predicted usage data to generate an optimized protection strategy; applying the optimized protection strategy to protection processing of the user real-name information; performing reverse deduction according to the optimized protection strategy to simulate protection effect; adjusting the optimized protection strategy based on the simulated protection effect to obtain a final protection strategy; outputting the final protection strategy to a competition reservation software for execution.

[0007] Preferably, the preprocessing of the user real-name information to obtain desensitization preparation data comprises: identifying sensitive fields in the user real-name information; annotating the sensitive fields according to preset desensitization rules; classifying the annotated user real-name information into desensitizable data and non-desensitizable data; generating the desensitization preparation data in combination with the classification result.

[0008] Preferably, the extraction of sensitive features of the desensitization preparation data to determine sensitive feature distribution information comprises: constructing a user information model; inputting the desensitization preparation data into the user information model to output a sensitive feature cloud map; analyzing the sensitive feature distribution information based on the preset privacy constraint information and the sensitive feature cloud map.

[0009] Preferably, the generation of an initial protection strategy based on preset privacy constraint information and in combination with the sensitive feature distribution information comprises: associating and calibrating a decision space according to the sensitive feature distribution information and usage record data to obtain a first calibrated decision space; identifying a decision feature trigger interval in the first calibrated decision space to obtain a first decision feature trigger space; generating an initial protection decision based on the first decision feature trigger space; calculating the fitness value of the initial protection decision; determining whether the fitness value meets a preset fitness constraint condition; If yes, adding the initial protection decision to the initial protection strategy.

[0010] Preferably, the trend prediction on the sensitive feature distribution information and the usage record data obtains predicted feature distribution information and predicted usage data, including: loading historical feature distribution data; combining the historical feature distribution data and the current usage record data, predicting future feature change trend; outputting the predicted feature distribution information and the predicted usage data.

[0011] Preferably, the compensation optimization on the initial protection strategy based on the predicted feature distribution information and the predicted usage data generates an optimized protection strategy, including: correlating and calibrating a decision space according to the predicted feature distribution information and the predicted usage data, obtaining a second calibrated decision space; identifying a decision feature trigger interval in the second calibrated decision space, obtaining a second decision feature trigger space; iterative decision making based on a preset fitness constraint condition and the second decision feature trigger space, generating a compensation protection strategy; optimizing the initial protection strategy in combination with the compensation protection strategy, obtaining the optimized protection strategy.

[0012] Preferably, the application of the optimized protection strategy to the user real-name information protection processing includes: analyzing the protection rules in the optimized protection strategy; performing desensitization or encryption operation on the user real-name information according to the protection rules; generating protected user data.

[0013] Preferably, the reverse deduction according to the optimized protection strategy to simulate the protection effect includes: loading simulation usage scenario data; applying the optimized protection strategy to the simulation usage scenario data, outputting simulation protection results; analyzing information leakage risk in the simulation protection results.

[0014] Preferably, the adjustment of the optimized protection strategy based on the simulation protection effect obtains a final protection strategy, including: comparing the information leakage risk with a preset risk threshold; If the information leakage risk is higher than the preset risk threshold, adjusting a protection rule in the optimized protection strategy; generating the adjusted protection strategy as the final protection strategy; The output of the final protection strategy to the event reservation software execution includes: Converting the final protection strategy into executable instructions; Integrating the executable instructions into the reservation process of the event reservation software to monitor the protection status of the user real-name information in real time.

[0015] Preferably, the application also includes a user real-name information protection system for event reservation software, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the user real-name information protection method for event reservation software as described above.

[0016] Compared with the prior art, the application has the following advantages: Through a series of coherent and rigorous steps, a comprehensive and dynamic solution is provided for user real-name information protection. First, the user real-name information is preprocessed to obtain desensitization preparation data, which can reduce the risk of sensitive information leakage in the initial stage of data processing and lay a good foundation for subsequent protection measures. Extracting sensitive features of desensitization preparation data and determining sensitive feature distribution information helps to accurately grasp the sensitivity and distribution of different information, making the protection strategy more targeted and avoiding the problem of resource waste or insufficient protection caused by blind protection.

[0017] Based on the preset privacy constraint information combined with the sensitive feature distribution information, the initial protection strategy is generated, which can ensure that the protection measures meet the basic privacy protection requirements and the specific needs of the event reservation scene from the beginning, so that information protection is carried out within the compliance framework. And by predicting the sensitive feature distribution information and usage record data to obtain predicted feature distribution information and predicted usage data, the protection strategy can be forward-looking. Through this prediction, changes in information sensitivity and possible usage risks can be perceived in advance, thereby providing a basis for the optimization of the protection strategy and avoiding the lag of static protection strategies when facing changes.

[0018] The initial protection strategy is compensated and optimized based on the predicted characteristic distribution information and the predicted use data to generate an optimized protection strategy, so that the protection strategy can be dynamically adjusted according to the actual situation, and the accurate matching of the protection strength and the information use scene and the sensitive degree change is realized. This dynamic adjustment can better adapt to the data use demand of different links in the event reservation process while ensuring information security, ensuring the efficient cooperation of event registration, review, verification and other links, and will not affect the normal event organization process due to excessive protection.

[0019] After the user real-name information is protected by applying the optimized protection strategy, the protection effect is simulated in reverse according to the optimized protection strategy, and the optimized protection strategy is adjusted based on the simulation effect to obtain the final protection strategy, which forms a closed-loop optimization mechanism. Through reverse deduction, possible vulnerabilities or unreasonable places in the protection strategy can be found in advance, and then adjusted and improved, so that the final protection strategy is more reliable.

[0020] The final protection strategy is output to the event reservation software for execution, which can ensure that the protection measures are effectively implemented in actual application. This method not only can cope with the complex data interaction and use situation in the event reservation scene, but also can adapt to the influence of user scale fluctuation, maintain the running efficiency of the software while ensuring information security. At the same time, since the entire protection process fully considers the sensitive feature change and potential risks, it can better meet the legal and regulatory requirements of different regions for personal information protection, reduce the legal risks of software operation, enhance the trust of users in the event reservation software, and is conducive to the long-term stable operation of the software. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A working principle diagram of the user real-name information protection method for the event reservation software according to the present application; Figure 2 A flowchart of user real-name information preprocessing; Figure 3 A flowchart of initial protection strategy generation; Figure 4 A flowchart of generating an optimized protection strategy by compensation optimization; Figure 5 A flowchart of generating a final protection strategy by adjusting the optimized protection strategy. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] Referring to Figure 1 The application provides a user real-name information protection method for event reservation software, and the method comprises the following steps: The user real-name information is comprehensively protected through a multi-level dynamic protection mechanism. First, the user real-name information including the user's name, ID number and contact information is obtained, and then the information is preprocessed to form desensitization preparation data. By extracting sensitive features and analyzing their distribution rules, an initial protection strategy is generated in combination with preset privacy constraints. The system further predicts feature distribution and usage trends, dynamically optimizes and compensates the initial strategy, and forms an optimized protection strategy. After the protection processing is implemented, the protection effect is simulated through reverse deduction, and finally an executable final protection strategy is generated and output to the event reservation software.

[0024] Embodiment 1: Referring to Figure 2 , the preprocessing and sensitive feature extraction process of user real-name information. In the preprocessing stage, the system receives original real-name information data including the user's name, ID number and contact information. The information recognition module adopts a multi-level scanning mechanism, and the first level scanning is based on a predefined sensitive field dictionary for pattern matching. The dictionary contains common sensitive data types and their feature patterns. For the ID number, the system applies a regular expression engine to identify the structural features of an 18-character sequence, focusing on the 7th to 14th birth date segment and the last 1st check code. For the mobile phone number, the system identifies the first three digits of the operator code and the middle four digits of the user number through a number segment database.

[0025] The labeling process uses a dynamic labeling system to automatically generate a three-level labeling system according to the data type. The first level label identifies the basic type of the field, the second level label marks the sensitivity, and the third level label records the data processing suggestions. During the labeling process, the system synchronously establishes a field metadata index to record the original position, length and association of each field. The classification engine performs threefold filtering according to the labeling results: first, it excludes fields that are required to be retained by laws and regulations, second, it analyzes the necessity of the field in the business scenario, and finally it evaluates the sensitivity of the field combination. The classification results form a three-color marking system, with red marks for completely desensitized fields, yellow marks for partially desensitized fields, and green marks for non-desensitized fields.

[0026] The generation of desensitization preparation data uses structured packaging technology. The system creates a data container object, which includes three core components: original data reference, classification label matrix and field association graph. The container object uses a hierarchical storage structure, with the original data layer retaining the initial information, the metadata layer storing the labeling classification results, and the strategy mapping layer recording the subsequent processing rule index. A data fingerprint is generated simultaneously during data packaging, using the SHA-256 algorithm to calculate the data integrity check value.

[0027] In the feature extraction stage, the user information model is constructed as a multi-dimensional feature space. The model input layer receives the desensitization preparation data container, and the feature mapping layer converts the data elements into a 128-dimensional feature vector. The vector dimension design includes three categories of basic attributes, associated attributes, and behavior attributes, each category has multiple sub-dimensions. The feature conversion process uses a convolutional neural network architecture, which includes three convolutional layers and two pooling layers, with convolution kernel sizes of 3x3, 5x5, and 3x3, respectively, and maximum value pooling is used.

[0028] The generation of the sensitive feature cloud map is based on feature vector space calculation. The system establishes a three-dimensional coordinate system, with the X-axis representing the feature sensitivity score, which is determined by the field type, data content, and associated fields; the Y-axis represents the feature usage frequency, which is dynamically calculated based on historical access logs; and the Z-axis represents the feature association risk value, reflecting the association strength of the feature with other sensitive data. The cloud map generation algorithm uses kernel density estimation technology to calculate the density distribution of feature points in space through a Gaussian kernel function. When rendering the cloud map, heat gradient coloring is used, with high-density areas showing warm colors and low-density areas showing cool colors.

[0029] The privacy constraint analysis module integrates a legal rule engine to convert preset privacy constraints into mathematical constraint conditions. The constraint conditions include seven categories such as data type constraints, usage scenario constraints, and storage period constraints. The analysis process uses a constraint satisfaction problem solving framework to match and calculate the feature cloud map data with the constraint conditions. The calculation process is divided into three steps: first, establish a feature-constraint association matrix, second, calculate the constraint satisfaction score, and finally generate a feature weight distribution map. The output results include a feature sensitivity distribution heat map, a feature association network graph, and a feature weight matrix table.

[0030] The determination of sensitive feature distribution information uses multi-source data fusion technology. The system integrates feature cloud map analysis results, constraint analysis results, and historical feature distribution data to generate a comprehensive feature distribution report through data fusion algorithms. The report includes a feature distribution statistics table, a feature clustering analysis graph, and an abnormal feature warning list. The statistics table records the distribution frequency, sensitivity mean, and risk value standard deviation of each type of feature; the clustering analysis graph displays the similarity relationship between features, and uses spectral clustering algorithm to divide features into different groups; the warning list marks feature points that exceed the normal fluctuation range, and labels their deviation degree and potential risk level. The entire processing process is completed in an encrypted memory area, and the intermediate calculation data is automatically cleared after processing is completed.

[0031] Example 2: see Figure 3, involving the generation mechanism of the initial protection policy and the feature trend prediction process. The decision space correlation calibration stage uses multi-dimensional space modeling technology to construct a six-dimensional decision hyperspace structure. Each dimension of the space represents the feature type distribution, usage scenario classification, access permission level, time decay coefficient, geographical restriction range, and emergency unlocking condition. The calibration process performs Monte Carlo simulation to generate a million-level virtual scenario dataset in a simulated environment, with each scenario containing a mapping relationship between feature combinations and usage records. The simulation is performed using a distributed computing architecture, which distributes the computational load to multiple processing nodes through a task fragmentation mechanism.

[0032] The space calibration algorithm uses a sliding window detection mechanism. The window size is dynamically adjusted according to the standard deviation of the feature distribution, with an initial window size of three times the standard deviation range. When the window slides along each dimension of the decision space, the density distribution of the data points within the window is calculated in real time. When the aggregation degree of the feature combination exceeds the preset threshold, the system marks the region as a decision feature trigger interval. The trigger interval boundary is calculated using the convex hull algorithm to generate the smallest boundary polygon. The first decision feature trigger space is formed from this, containing multiple interrelated trigger regions and their boundary parameters.

[0033] The initial protection decision generation uses a genetic algorithm framework. The algorithm initializes by randomly generating 200 decision schemes to form the initial population, with each scheme encoded as a binary gene sequence. The gene sequence includes parameters such as protection rule type, execution intensity, and scope of action. The iterative evolution process performs 50 rounds, with each iteration containing three basic operations: selection, crossover, and mutation. The selection operation uses the tournament selection mechanism, randomly selecting 10 individuals to compete for entry into the next generation each time. The crossover operation uses the two-point crossover method, with a crossover probability of 0.85. The mutation operation uses the bit flip mechanism, with a mutation probability of 0.01.

[0034] The fitness function is designed as a multi-objective comprehensive evaluation system. The information security dimension includes data confidentiality, integrity, and availability, with a weight ratio of 60%. The system performance dimension includes processing delay, resource consumption, and throughput, with a weight ratio of 30%. The user experience dimension includes operation convenience and interface friendliness, with a weight ratio of 10%. The scores of each sub-item are obtained through simulation testing, and the final weighted comprehensive fitness value is calculated. The preset fitness constraint condition is set as a threshold standard of a comprehensive score not less than 0.85. When the decision scheme reaches this threshold, the system decodes its gene sequence into executable rules and adds it to the initial protection policy library.

[0035] The loading of historical feature distribution data is managed using a time series database. The system retrieves the feature distribution records for the past three years and aligns the data by time granularity. In the data preprocessing stage, missing value imputation and smoothing are performed, and linear interpolation is used to supplement incomplete data points. Time series analysis is used to build an ARIMA prediction model, and the model parameters are determined through an automatic optimization process. The optimization process calculates the AIC information criterion value for different parameter combinations, and selects the parameter configuration with the smallest AIC. The order of difference is determined by the ADF unit root test to ensure the stationarity of the sequence.

[0036] The future feature change trend prediction uses an LSTM neural network architecture. The network contains three hidden layers, with 256 neurons in each layer. The input layer receives the historical feature sequence, with a time step of 30 consecutive periods. The network training uses the Adam optimization algorithm, with an initial learning rate of 0.001 and a decay of 50% every 10 rounds. The training data is divided into 70% training set, 15% validation set and 15% test set. The prediction process performs multi-step rolling prediction, predicting the feature distribution of the next three periods each time. The prediction of recorded data uses a collaborative filtering algorithm to analyze the similarity of user behavior patterns and infer the probability distribution of future usage scenarios.

[0037] The prediction result output includes predicted feature distribution information and predicted usage data. The feature distribution information is stored in the form of a three-dimensional tensor, recording the expected distribution state of each feature at different time periods. The usage data prediction result generates a probability distribution table, listing the occurrence probability and expected frequency of each usage scenario. All prediction results are accompanied by a confidence score, reflecting the reliability of the prediction results. The system establishes a prediction result version management mechanism to retain historical prediction records for continuous model optimization. The prediction module is equipped with an automatic calibration function that triggers model retraining when actual data deviates from predicted values beyond the allowed range. The entire prediction process is executed on a dedicated prediction server cluster, and the calculation results are encrypted and transmitted to the strategy generation module.

[0038] Embodiment 3: refer to Figure 4 , which involves the compensation optimization process of the protection strategy. This process achieves dynamic improvement of the strategy by expanding the decision space dimension and applying reinforcement learning techniques. The second calibration decision space adds a prediction dimension to the first calibration decision space, forming a seven-dimensional decision hyperspace structure. The new prediction dimension includes three sub-dimensions: feature distribution rate of change, usage frequency fluctuation coefficient, and risk trend slope, which are calculated through time series analysis. Space calibration uses an improved particle swarm optimization algorithm, deploying 200 probe particles in the seven-dimensional space, each carrying a position vector and a velocity vector. The particle movement process follows the spatial topological constraints and automatically adjusts the search step when detecting feature clusters. The identification of decision feature trigger intervals uses the density peak clustering algorithm to determine the key decision points in the space by calculating local density and relative distance.

[0039] The implementation of deep Q-learning algorithm is based on Markov model of decision space. The state space is defined as the transition probability matrix of feature distribution, containing 512 discrete states. The action space design contains 12 standard protection operations such as data encryption, field desensitization and access control, and three intensity levels are set for each operation. The design of reward function considers the balance between risk control and system overhead, and its calculation expression is: ; Where: R represents the comprehensive reward value, n represents the number of risk types, represents the weight coefficient of the ith risk, represents the reduction degree of this type of risk, m represents the number of resource types, represents the cost coefficient of the jth resource, represents the consumption of this type of resource. The discount factor γ is set to 0.9 to control the decay rate of long-term return. The network structure adopts a double DQN architecture, including evaluation network and target network, and the network parameters are updated synchronously in each training round.

[0040] The generation process of compensation protection strategy adopts policy gradient method. The policy network contains four fully connected layers with 256, 128, 64 and 12 neurons respectively. The learning rate is set to 0.001, and the adaptive momentum optimization method is used to update the network parameters. The training process performs 1000 iterations, and 256 transition samples are sampled from the experience replay pool in each iteration. The policy improvement adopts trust region optimization technology to limit the variation amplitude of the policy in each update. The final output compensation strategy contains a priority queue, which sorts the execution order of protection measures according to the operation urgency.

[0041] The strategy fusion stage adopts ensemble learning method to construct the optimized protection strategy. The initial strategy and the compensation strategy are mixed in the ratio of 7:3, and the mixing process performs weighted integration at the rule level. For conflicting protection rules, the system calculates the confidence score of each rule, which considers three factors: rule source, historical execution effect and current environment matching degree. Rules with higher confidence scores are given priority to be retained, and rule conflict resolution records are generated. The integrated strategy is compressed through knowledge distillation technology, using a teacher-student network framework to convert complex strategies into lightweight decision trees.

[0042] The structure of the optimized protection strategy includes three parts: rule set, execution condition, and monitoring index. The rule set adopts a hierarchical organization method, with scene classification rules at the top, feature matching rules in the middle, and specific operation rules at the bottom. The execution condition defines the threshold criteria for triggering the strategy, including feature distribution threshold, risk level threshold, and system load threshold. The monitoring index sets up the evaluation system for the execution effect of the strategy, including risk control rate, response delay, and resource occupancy rate. The strategy version management adopts an incremental update mechanism, generating a new strategy branch each time the optimization is performed, and preserving historical versions for rollback.

[0043] The strategy optimization process sets up multiple verification links. Static verification checks the logical consistency of the strategy rules, excluding mutually contradictory rule combinations. Dynamic verification simulates the execution of the strategy in a sandbox environment to evaluate the actual operation effect. The expert verification link invites security analysts to review key strategy changes. After all verifications are passed, the optimized protection strategy is marked as deployable and waits for subsequent processing stages. The intermediate data generated during the entire optimization process is protected using differential privacy technology to ensure the confidentiality of the strategy generation process.

[0044] The update mechanism of the strategy knowledge base realizes the function of continuous learning. After each strategy execution, the system collects actual effect data and calculates the strategy utility value. Strategy fragments with utility values higher than the threshold enter the excellent strategy library, and those with utility values lower than the threshold trigger optimization alarms. The knowledge base uses a graph database to store the association between strategies, supporting strategy retrieval based on similarity. Regularly perform strategy knowledge distillation to convert complex strategy networks into interpretable rule sets, improving the transparency and maintainability of the strategy. The strategy rollback mechanism preserves the complete strategy set of the last five versions, which can quickly recover to the historical stable version when abnormal conditions are detected.

[0045] Embodiment 4: Covers the application execution and protection effect simulation verification process of the optimized protection strategy. In the strategy parsing stage, the system loads the optimized protection strategy file and decomposes the strategy statements into executable atomic operations through the semantic analysis engine. The parsing process uses context-free grammar to build a syntax tree, with leaf nodes corresponding to specific protection operation instructions. Each instruction contains operation type, object, and execution parameter. After parsing, the operation instruction sequence is generated, and the instruction dependency graph is established to ensure that the operations are executed in the correct order.

[0046] Taking the real-name information protection of user Zhang San as an example, the original data contains the name "Zhang San", the ID number "11010119900307783X", and the mobile phone number "13901234567". After policy analysis, three operation instructions are generated: the first instruction performs partial desensitization on the ID number, retaining the first 6 digits and the last 4 digits; the second instruction performs AES encryption storage on the mobile phone number; and the third instruction adds an access control label to the name field. The system determines the execution order based on data correlation: first process the ID number, then process the mobile phone number, and finally set the access control.

[0047] The protection processing is executed in a hybrid operation mode. The desensitization operation implements dynamic masking technology and applies differentiated processing rules for different field types. The ID number processing uses segmented masking, retaining the administrative division code and the check code, and masking the birth date segment. The mobile phone number processing uses a combination of encryption storage and display masking, using AES-256 algorithm to encrypt the complete number during storage, and using a (3,5) threshold secret sharing scheme for key management, and during display, only showing the first three and last four digits. The encryption operation implements a hybrid encryption system, using SM4 algorithm for symmetric encryption to process the data subject, and using SM2 algorithm for asymmetric encryption to protect the symmetric key. The access control of sensitive fields implements an attribute-based access control model, setting up a multi-level permission system.

[0048] The protected user data generates three core components: the processed data subject, operation metadata, and security labels. The data subject stores the desensitized or encrypted actual content; the operation metadata records the operation type, timestamp, and operator identity; and the security label defines the data security level and access policy. The system embeds digital watermarks during data packaging, with watermark information including data processing serial number, operation time, device fingerprint, etc., using discrete cosine transform algorithm to embed in the frequency domain. The final output data packet is packaged in JSON format, containing header, payload, and signature three-part structure.

[0049] A multi-scenario test environment is constructed to simulate the protection effect. The system preloads 100 typical scenario data, divided into normal operation scenarios and attack scenarios. Normal scenarios simulate regular operations such as reservation, query, and modification; attack scenarios include malicious behaviors such as SQL injection, privilege escalation, and data sniffing. Scenario data is described in YAML format, including initial state, operation sequence, and expected results.

[0050] Table 1: Shows some typical test scenarios.

[0051] Scenario number Scenario type Description Data characteristics Expected operation S-07 Normal operation User booking events Contains complete real name information Desensitization display part field S-12 Normal operation Customer service query user information Need to verify identity Decrypt complete mobile phone number A-03 Unauthorized access Ordinary user attempts to access other people's information Cross-account ID request Block access and warn A-15 Data crawling High-frequency batch query user information Same IP short-term multiple requests Trigger flow limiting and verification code challenge A-22 Injection attack SQL injection attempts to obtain plaintext data Contains malicious injection statements Filter special characters and block requests F-05 System failure Database exception interruption Service unavailable Start backup and data consistency check The simulation execution engine adopts a containerized deployment architecture, with each scenario running in an independent container instance. The engine core components include a scenario loader, a policy executor, and a behavior monitor. The execution process records detailed operation logs, including key events such as policy trigger points, executed actions, and system responses. The effect analysis module processes the execution logs and extracts risk feature indicators.

[0052] The information leakage risk analysis uses a multi-dimensional evaluation model. The access path dimension analyzes the reliability of the request source, distinguishing between trusted terminals, unknown devices, and anonymous access. The impact range dimension assesses the amount and sensitivity of data that may be leaked. The repair difficulty dimension measures the complexity of remedial measures after an incident. Risk quantification uses a weighted scoring method, with weights adjusted dynamically according to scenario type. The analysis results generate a risk heat map, marking the distribution of system vulnerabilities and protection strengths.

[0053] The risk report contains three parts: the first part is a scenario execution summary, which statistics basic indicators such as total scenario number, successful interception number, and false operation number; the second part is a risk distribution analysis, which shows the proportion of different risk levels and associated scenarios; the third part lists key findings, including effective protection points, potential vulnerabilities, and improvement suggestions. The report output uses an interactive visualization interface, supporting multi-dimensional filtering and viewing by risk level, scenario type, and attack vector.

[0054] The simulation verification process sets up an iterative optimization mechanism. When a protection vulnerability is found, the system automatically generates a scenario reproduction package containing the initial state, operation sequence, and actual results. Security analysts debug protection rules based on the reproduction package and modify them for retesting. The verification cycle continues until all high-risk scenarios are effectively protected. The final verification report is stored with the optimized protection strategy as a basis for policy deployment. Temporary data generated during the simulation process is automatically destroyed at the end of execution, ensuring strict isolation between the test environment and the production environment.

[0055] Example 5: Referring to Figure 5 , covers the final adjustment of the protection strategy and the system integration process. The risk threshold comparison mechanism uses a dynamic calculation model, with a basic risk threshold set at 7.0 points. This score is based on the Common Vulnerability Scoring Standard. The threshold floating parameters include a software version coefficient and a user scale coefficient. The version coefficient is adjusted according to the major version number, and the user scale coefficient is calculated using a logarithmic function. The system monitors information leakage risk values in real-time, triggering a policy adjustment process when the risk value exceeds the dynamic threshold.

[0056] The policy adjustment process performs a rule-level incremental update. The system scans all rules in the optimized protection policy and identifies rule entries with risk contribution higher than average. Parameter fine-tuning is performed for high-contribution rules in three modes: enhancing protection strength, expanding the scope of action, and adding verification links. The adjustment amplitude of each rule is proportional to its risk contribution, while being constrained by system performance margins. The adjusted rule set recalculates priorities, considering rule effectiveness speed, resource consumption rate, and false interception rate.

[0057] The final protection policy generation includes policy signing and version marking. The policy file is attached with a digital signature, and the signature value is generated using the elliptic curve digital signature algorithm. The version marking uses a four-segment encoding structure, representing the main version, feature version, revision version, and build number, respectively. The policy file is packaged into a specific format policy package, containing four components: policy body, dependent library list, compatibility declaration, and rollback pointer.

[0058] The instruction conversion process uses a hierarchical compilation technique. The high-level policy language is first compiled into an intermediate representation, which uses a control-flow-based instruction sequence. The intermediate representation is platform-independent optimized, including constant propagation, dead code elimination, and loop unrolling. The target code generation stage performs specific optimization for the running environment of the event reservation software, generating ARM instruction sets for mobile applications and x86 instruction sets for server-side. The final output executable instructions include protection operation instruction sets and monitoring instruction sets.

[0059] The system integration process uses a modular injection method. Five key hook points are inserted in the software's data access layer: data read before hook, data read after hook, data write before hook, data write after hook, and data transmission hook. Each hook point registers the corresponding protection processing function, and the function call follows a unified interface specification. The integration process performs dependency checks to ensure system library version compatibility and meet minimum resource requirements.

[0060] The real-time monitoring system builds a distributed data collection network. Lightweight probes are deployed on terminal devices to collect basic indicators such as data access frequency, operation type, and response time. Deep monitoring agents are deployed on the server side to record memory access patterns, network traffic characteristics, and abnormal behavior traces. Monitoring data uses a streaming processing architecture, transmitting data to the analysis center through a message queue.

[0061] The anomaly detection mechanism uses a multi-model fusion architecture. The basic detection layer uses an improved isolation forest algorithm, adding time series analysis dimensions. The secondary detection layer applies a hidden Markov model to identify state transition anomalies. The tertiary detection layer deploys a deep anomaly detection network containing long short-term memory units and self-attention mechanisms. The detection result generates an anomaly score, and an alarm is triggered when the score exceeds the threshold.

[0062] A three-level linkage mechanism is designed for the response system. The first-level response performs local interception, including basic operations such as termination, session reset, cache clearing, etc. The second-level response triggers system-level protection, including advanced measures such as process isolation, network throttling, key rotation, etc. The third-level response initiates global emergency, including comprehensive measures such as data migration, service degradation, audit tracking, etc. The response actions are executed in order of priority, and high-priority responses can interrupt low-priority responses.

[0063] The visualization system constructs a dynamic topology model. Nodes represent data entities and user entities, and edges represent access relationships and operation flow directions. A graphical rendering engine calculates node positions in real time, and important nodes are automatically focused on for display. The views include data flow views, risk distribution views, and protection status views Figure 3 The system supports multiple modes, allowing free switching. A risk heat map is displayed on the topology graph, with color gradients representing risk levels.

[0064] The state subscription service supports multiple notification channels. Users can configure WeChat, SMS, and email notifications, and set notification thresholds for different risk levels. Notification content includes event type, risk level, impact range, and recommended measures. Key event notifications have an additional confirmation mechanism, requiring recipients to confirm the alert within a limited time.

[0065] The system maintenance mechanism includes automatic inspection and manual debugging modes. Automatic inspection is performed every morning to check policy validity, monitoring completeness, and response readiness. Inspection reports are generated in HTML format, with notes on pending items. The manual debugging mode is open to security administrators, supporting policy sandbox testing and monitoring simulation playback. All operations are recorded in detailed audit logs, and log retention periods meet regulatory requirements.

[0066] It should be noted that, in the present text, relational terms such as first and second are used merely to distinguish one entity or action from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0067] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A user real name information protection method for event reservation software, characterized in that, The method comprises the following steps: obtaining user real-name information, wherein the user real-name information comprises user name, ID number and contact information; preprocessing the user real-name information to obtain desensitization preparation data; extracting sensitive features of the desensitization preparation data to determine sensitive feature distribution information; generating an initial protection strategy based on preset privacy constraint information and the sensitive feature distribution information; trend prediction of the sensitive feature distribution information and usage record data to obtain predicted feature distribution information and predicted usage data; compensation optimization of the initial protection strategy based on the predicted feature distribution information and the predicted usage data to generate an optimized protection strategy; applying the optimized protection strategy to protect the user real-name information; reverse deduction according to the optimized protection strategy to simulate the protection effect; adjusting the optimized protection strategy based on the simulated protection effect to obtain a final protection strategy; outputting the final protection strategy to a competition reservation software for execution. The method comprises the following steps: calibrating the decision space based on the sensitive feature distribution information and usage record data to obtain a first calibrated decision space; identifying the decision feature trigger interval in the first calibrated decision space to obtain a first decision feature trigger space; generating an initial protection decision based on the first decision feature trigger space; calculating the fitness value of the initial protection decision; judging whether the fitness value meets the preset fitness constraint condition; if yes, adding the initial protection decision to the initial protection strategy.

2. The user real name information protection method for event reservation software according to claim 1, characterized in that, The method comprises the following steps: identifying sensitive fields in the user real-name information; annotating the sensitive fields according to preset desensitization rules; classifying the annotated user real-name information into desensitizable data and non-desensitizable data; generating the desensitization preparation data based on the classification results.

3. The user real name information protection method for event reservation software according to claim 2, characterized in that, The method comprises the following steps: constructing a user information model; inputting the desensitization preparation data into the user information model to output a sensitive feature cloud map; analyzing the sensitive feature distribution information based on the preset privacy constraint information and the sensitive feature cloud map.

4. The user real name information protection method for event reservation software according to claim 3, characterized in that, The method comprises the following steps: loading historical feature distribution data; predicting future feature change trends based on the historical feature distribution data and current usage record data; outputting the predicted feature distribution information and the predicted usage data.

5. The user real name information protection method for event reservation software according to claim 4, characterized in that, The method comprises the following steps: calibrating the decision space based on the predicted feature distribution information and the predicted usage data to obtain a second calibrated decision space; identifying the decision feature trigger interval in the second calibrated decision space to obtain a second decision feature trigger space; generating a compensation protection strategy based on preset fitness constraint conditions and the second decision feature trigger space; optimizing the initial protection strategy based on the compensation protection strategy to obtain an optimized protection strategy.

6. The user real name information protection method for event reservation software according to claim 5, characterized in that, The application of the optimized protection strategy to the protection processing of the user real-name information includes: parsing the protection rules in the optimized protection strategy; performing desensitization or encryption operations on the user real-name information according to the protection rules; generating protected user data.

7. The user real name information protection method for event reservation software according to claim 6, characterized in that, The reverse deduction according to the optimized protection strategy includes: loading simulation use scenario data; applying the optimized protection strategy to the simulation use scenario data to output simulation protection results; analyzing information leakage risks in the simulation protection results.

8. The user real name information protection method for event reservation software according to claim 7, characterized in that, The adjustment of the optimized protection strategy based on the simulation protection effect to obtain a final protection strategy includes: comparing the information leakage risks with preset risk thresholds; if the information leakage risks are higher than the preset risk thresholds, adjusting the protection rules in the optimized protection strategy; generating an adjusted protection strategy as the final protection strategy; The output of the final protection strategy to the event reservation software for execution includes: converting the final protection strategy into executable instructions; integrating the executable instructions into the reservation process of the event reservation software to monitor the protection state of the user real-name information in real time. 9.A user real name information protection system for a sports event reservation software, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, implements the steps of the user real-name information protection method for the event reservation software in any one of claims 1 to 8.

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