A multi-tenant mall security supervision method and system based on big data analysis

Abstract

This invention relates to the field of e-commerce security supervision technology, and discloses a method and system for multi-tenant e-commerce security supervision based on big data analysis. The method includes: hierarchical heterogeneous data adaptation to construct a unified data representation; constructing an identity association graph based on probabilistic reasoning; using a federated graph neural network for distributed feature learning; implementing hierarchical threat detection and risk assessment; constructing a differential privacy-preserving security response mechanism; and deploying a cloud-native architecture and self-healing mechanism. This invention effectively solves problems related to heterogeneous data processing, cross-platform identity association, balancing privacy protection and security supervision, and system reliability in multi-tenant e-commerce environments. It improves the accuracy, comprehensiveness, and reliability of security supervision while protecting user data privacy, providing comprehensive technical support for the secure operation of e-commerce platforms.

Description

Technical Field

[0001] This invention relates to the field of e-commerce security supervision technology, and more specifically, to a method and system for supervising the security of multi-tenant e-commerce platforms based on big data analysis. Background Technology

[0002] With the rapid development of e-commerce, multi-tenant e-commerce platforms have become an important part of the modern business ecosystem. These platforms are typically used by multiple merchants, forming complex business environments and data structures. Against this backdrop, e-commerce platform security supervision faces unprecedented challenges. Traditional security supervision methods mainly rely on data analysis and rule matching within a single platform, which is insufficient to address the complex security threats in cross-platform, multi-tenant environments. In particular, they have significant shortcomings in areas such as heterogeneous data processing, identity association analysis, privacy protection, and system reliability.

[0003] The main problems currently facing e-commerce platform security monitoring technologies include: low efficiency in processing heterogeneous data, with significant differences in data formats, structures, and semantics across different platforms, leading to difficulties in data integration; low accuracy in cross-platform identity recognition, making it difficult to effectively correlate the behavioral data of the same user across different platforms; a conflict between data privacy protection and security monitoring, as traditional methods often require centralized storage and processing of sensitive data, increasing the risk of data leakage; and insufficient system reliability and scalability, making it difficult to cope with the dynamic load changes and continuous operation requirements of large-scale e-commerce platforms.

[0004] To address the aforementioned issues, a novel technical solution is needed that can effectively handle heterogeneous data, accurately associate cross-platform identities, balance privacy protection and security oversight, and ensure system reliability. This solution should be adaptable to the complex business scenarios of multi-tenant e-commerce platforms, support large-scale data processing, and possess good scalability and maintainability, thereby providing comprehensive, efficient, and reliable technical support for e-commerce platform security oversight. Summary of the Invention

[0005] This invention provides a method and system for security supervision of multi-tenant e-commerce platforms based on big data analysis, which solves problems in related technologies such as how to achieve efficient heterogeneous data processing and accurate cross-platform identity association in a multi-tenant e-commerce environment.

[0006] This invention provides a method for security supervision of multi-tenant e-commerce platforms based on big data analysis, comprising the following steps:

[0007] Layered adaptation and standardization of heterogeneous data on multi-tenant platforms, semantic alignment based on knowledge graphs, and progressive fusion to form a multi-confidence data structure;

[0008] Identity feature vectors are constructed by extracting multimodal features, and identity reasoning is realized by using Bayesian networks to quantify uncertainty and construct a probabilistic identity association graph.

[0009] Federated graph neural networks are used for distributed feature learning. The feature learning network is constructed through graph segmentation and multi-head attention mechanism to achieve cross-tenant secure computation and differential privacy protection.

[0010] Perform hierarchical threat detection, including rapid screening, refined detection, and rule verification, and use an improved SIR model for risk propagation modeling;

[0011] To achieve differential privacy-enhanced collaborative learning, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, a privacy-preserving model is constructed.

[0012] Deploy the security model in a cloud-native microservice architecture to achieve multi-layered protection through service governance, security protection, and observability systems.

[0013] In a preferred embodiment, the data quality score for hierarchical adaptation and standardization of heterogeneous data on a multi-tenant platform is as follows:

[0014] Q score =w1·Completeness+w2·Accuracy+w3·Consistency;

[0015] Among them, Q score The completeness score represents the overall quality score; w1 represents the weighting coefficient for completeness, used to control the importance of completeness indicators in the overall quality score; w2 represents the weighting coefficient for accuracy, used to control the importance of accuracy indicators in the overall quality score; w3 represents the weighting coefficient for consistency, used to control the importance of consistency indicators in the overall quality score; Completeness is the data completeness score, representing the data field fill rate and validity; Accuracy is the data accuracy score, representing the correctness and precision level of data values; Consistency is the data consistency score, representing the logical relationships and rule compliance between data.

[0016] In a preferred embodiment, the likelihood function in the identity feature vector constructed through multimodal feature extraction is calculated as follows:

[0017]

[0018] Among them, P(ID) virtual ID real ) represents the conditional probability of observing a specific virtual identity feature given that the real identity is known; This represents the value of the j-th feature dimension of the virtual identity; This represents the value of the j-th feature dimension representing the true identity. represents the conditional probability of observing a specific virtual identity feature given the real identity feature values; j represents the index of the feature dimension, from 1 to m; m represents the total number of feature dimensions, i.e., the number of all features used for identity association; ∏ represents the chain multiplication operation, which multiplies the conditional probabilities of all feature dimensions.

[0019] In a preferred embodiment, the loss function for distributed feature learning in the federated graph neural network is:

[0020] L total =L recon +β·KL(q φ (z|x)||p(z));

[0021] Among them, L total L represents the total loss function, which is the overall optimization objective of model training; recon Represents the reconstruction loss, used to measure the difference between the reconstructed data and the original data; KL represents the KL divergence regularization term; q φ (z|x) represents the posterior distribution generated by the encoder, φ represents the encoder parameters, z represents the latent variable, and x represents the input data; p(z) represents the prior distribution; β represents the trade-off parameter used to control the balance between reconstruction loss and KL divergence regularization term; || represents the KL divergence calculation symbol between the two distributions.

[0022] In a preferred embodiment, risk propagation prediction in hierarchical threat detection employs an improved SIR model, including:

[0023] Susceptible state S: Nodes that may be affected by the risk; Infected state I: Nodes where the risk has been confirmed; Recovered state R: Nodes where the risk has been controlled; The risk transmission process is described by the infection rate β, recovery rate γ, and immune failure rate δ.

[0024] In a preferred embodiment, the dynamic confidence update in collaborative learning to achieve differential privacy enhancement is as follows:

[0025]

[0026] Among them, P (t+1) (ID real ID virtual P represents the posterior probability at time t+1; (t) (ID real ID virtual () represents the posterior probability at time t; evidence (t+1)P represents the newly acquired evidence at time t+1; η is a normalization constant that ensures the sum of the posterior probabilities of all possible true identities is 1; P(evidence) (t+1) ID real Let P(evidence) be the likelihood function of the new evidence, representing the probability of observing the new evidence given the known true identity; (t+1) ) represents the marginal probability of new evidence, and represents the overall probability of observing new evidence.

[0027] In a preferred embodiment, the security model is deployed on a cloud-native microservice architecture to achieve automatic registration and health checks of system components through service registration and discovery mechanisms, dynamic load balancing is achieved through DNS-based service resolution, and multi-level failover strategies are supported to ensure service availability.

[0028] In a preferred embodiment, a multi-tenant e-commerce platform security monitoring system based on big data analytics is used to execute a multi-tenant e-commerce platform security monitoring method based on big data analytics, including:

[0029] The hierarchical heterogeneous data adaptation module is used to perform hierarchical adaptation and standardization of heterogeneous data on multi-tenant platforms, achieve semantic alignment based on knowledge graphs, and form a multi-confidence data structure through progressive fusion.

[0030] The identity association analysis module is used to construct identity feature vectors through multimodal feature extraction, realize identity reasoning using Bayesian networks, quantify uncertainty, and construct a probabilistic identity association graph.

[0031] The federated graph neural network module is used for distributed feature learning using federated graph neural networks. It constructs a feature learning network through graph segmentation and multi-head attention mechanisms to achieve cross-tenant secure computation and differential privacy protection.

[0032] The hierarchical threat detection module is used to perform hierarchical threat detection, including rapid screening, fine-grained detection, and rule verification, and uses an improved SIR model for risk propagation modeling.

[0033] A differential privacy-enhanced collaborative learning module is used to implement differential privacy-enhanced collaborative learning, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, to build a privacy-preserving model;

[0034] The cloud-native microservice architecture module is used to deploy security models on cloud-native microservice architectures, achieving multi-layered protection through service governance, security protection, and observability systems.

[0035] In a preferred embodiment, a multi-tenant e-commerce security monitoring system based on big data analytics can monitor and evaluate data quality in real time and dynamically adjust processing strategies; it uses homomorphic encryption to achieve secure computation and protect data privacy; and it has the ability to automatically detect and recover from system failures to ensure continuous service availability.

[0036] In a preferred embodiment, a multi-tenant e-commerce security monitoring system based on big data analytics is deployed in a distributed architecture, supporting horizontal scaling of the system; it achieves dynamic routing and load balancing through a service mesh; it executes fine-grained access control policies; and it establishes a comprehensive monitoring and tracing mechanism.

[0037] The beneficial effects of this invention are as follows:

[0038] This system addresses the technical challenges of handling heterogeneous data in a multi-tenant e-commerce environment. Through a layered data adaptation mechanism and knowledge graph-based semantic alignment technology, it effectively processes heterogeneous data from different platforms, achieving unified data format and semantic consistency, thus improving the accuracy and efficiency of cross-platform data fusion. A progressive fusion strategy further enhances the system's ability to handle large-scale heterogeneous data, enabling it to adapt to inputs of varying data quality and format.

[0039] While protecting data privacy, the system achieves efficient security oversight. Identity association analysis technology based on probabilistic reasoning can accurately identify user identities across platforms without directly obtaining sensitive personal information. The application of differential privacy protection and federated learning technologies enables the system to achieve collaborative and secure modeling and analysis among multiple parties while protecting their data privacy, effectively balancing the conflict between security oversight and privacy protection. Attached Figure Description

[0040] Figure 1 This is a flowchart of a multi-tenant e-commerce security supervision method based on big data analysis according to the present invention;

[0041] Figure 2 This is a module diagram of a multi-tenant e-commerce security monitoring system based on big data analysis according to the present invention. Detailed Implementation

[0042] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0043] At least one embodiment of the present invention discloses a multi-tenant e-commerce security supervision method based on big data analysis, such as... Figure 1 As shown, it includes the following steps:

[0044] Step 1: Perform layered adaptation and standardization of heterogeneous data on the multi-tenant platform, achieve semantic alignment based on knowledge graph, and adopt progressive fusion to form a multi-confidence data structure;

[0045] Input preprocessing:

[0046] In the data preprocessing stage, the system first performs comprehensive standardization processing on all types of data.

[0047] For continuous numerical features (such as transaction amount, operation frequency, etc.), the system uses the Z-score standardization method to normalize the data, converting it into a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0048] For timestamp features, the system converts them into relative time intervals for easier subsequent analysis. Amount-related features are processed using a logarithmic transformation algorithm. This method compresses the data range by taking the natural logarithm, reducing the difference between large and small amounts and effectively handling the skewed distribution of monetary data.

[0049] To handle outliers, the system employs the Winsorization method. This method sets upper and lower quantile thresholds (95th and 5th percentiles, respectively) to limit data exceeding these thresholds to within their range, thereby reducing the impact of extreme outliers while maintaining the overall distribution characteristics of the data.

[0050] During outlier detection, the system also uses the interquartile range (IQR) method for auxiliary judgment. That is, the difference between the 75th percentile (Q3) and the 25th percentile (Q1) of the data is calculated, and values ​​that exceed the range of [Q1-1.5×IQR, Q3+1.5×IQR] are marked as potential outliers. This method does not depend on the specific distribution characteristics of the data and has strong robustness.

[0051] In terms of categorical data encoding, the system employs multiple encoding strategies to adapt to the characteristics of different features.

[0052] For high cardinality category features (such as user ID and product ID), the system uses feature hashing and sets the hash space dimension to twice the feature cardinality to reduce the probability of collisions.

[0053] Low cardinality category features (such as user level and product category) are encoded using One-Hot encoding and stored in a sparse matrix format to optimize storage efficiency.

[0054] For ordered category features (such as risk level), the system uses label encoding and maintains the original order relationship.

[0055] In addition, the system also performs combined encoding on important category feature pairs, such as "user level - product category", to capture the interaction relationships between features.

[0056] In the text data processing stage, the system implements comprehensive text feature engineering.

[0057] First, the TF-IDF algorithm is used to vectorize texts such as product descriptions and user reviews, with the maximum number of features set to 10,000 to balance feature expressiveness and computational efficiency.

[0058] Subsequently, the system uses PCA dimensionality reduction technology to reduce the text vector to 128 dimensions while maintaining 95% of the variance information, effectively reducing the feature dimension while retaining key information.

[0059] The system also uses the TextRank algorithm to extract keywords from the text. This algorithm is based on the idea of ​​PageRank and identifies keywords in the text by constructing a word co-occurrence graph and iteratively calculating the importance of nodes.

[0060] Specifically, the algorithm first segments the text into words and constructs a word co-occurrence window (default size is 5), then calculates the edge weights between words (based on co-occurrence frequency), and finally calculates the weight score of each word iteratively. The words with higher scores are the keywords.

[0061] In terms of text semantic analysis, the system uses a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for sentiment polarity analysis. The BERT model is pre-trained using a bidirectional Transformer encoder and can dynamically generate word vector representations based on the context.

[0062] In this system, the BERT model receives text input, captures semantic relationships between words through a multi-layer self-attention mechanism, and finally maps the [CLS]-tagged output vector to sentiment polarity scores (positive, negative, or neutral) through a fully connected layer. The model uses weights pre-trained on a large-scale Chinese corpus and is fine-tuned on domain-specific sentiment-annotated data to adapt to the characteristics of the e-commerce scenario.

[0063] Processing procedure:

[0064] In the data adaptation layer processing stage, the system designs a dedicated data adapter for each tenant platform to achieve unified processing of heterogeneous data sources.

[0065] During the initialization phase, the system first loads the platform-specific field mapping configuration (Schema_i, where i represents the configuration of the i-th tenant platform) and data quality rules, and establishes a multi-level set of field validation rules.

[0066] These rules include basic rules such as data type, length, and range; business rules such as business logic and data association; and quality rules such as integrity, consistency, and accuracy.

[0067] At the same time, the system initializes a configurable data converter, supporting custom conversion rules to meet the specific needs of different platforms.

[0068] During the data preprocessing stage, the system implements comprehensive data format verification. Through encoding consistency checks (verifying the uniformity of character encoding formats), structural integrity verification (checking the integrity and consistency of the data structure), and format standardization checks (ensuring that the data format conforms to predefined standards), the system ensures that the input data meets basic format requirements.

[0069] For handling missing values, the system first calculates the missing patterns and proportions, and then selects the appropriate processing strategy based on the importance of the field: critical fields are rejected, while optional fields are handled by imputation or setting default values.

[0070] In terms of outlier detection, the system adopts multi-dimensional detection methods, including detection based on statistical features such as Z-score (converting raw values ​​into standard scores) and IQR (calculating interquartile range), violation detection based on business constraints (such as numerical range, logical relationships, etc.), and abrupt change point identification based on time-series patterns (such as moving average, rate of change, etc.).

[0071] Field mapping and transformation are the core aspects of data adaptation. The system implements a dynamic field mapping mechanism, which ensures accurate mapping of data fields through configuration-based field name mapping (converting field names according to predefined mapping rules), support for complex combination mapping rules (handling multi-field combinations and derived fields), and handling field conflicts and ambiguities (through priority rules and conflict resolution strategies).

[0072] In terms of intelligent data type conversion, the system achieves high-quality type conversion through automatic type inference (based on data features and pattern recognition) and verification, precision loss and overflow handling (ensuring the reasonableness of numerical precision and range), and preservation of data semantic information (maintaining the business meaning of the data).

[0073] For advanced format standardization, the system unified the date and time format (converted to a standard timestamp format), implemented the conversion of measurement units (based on predefined conversion rules), and standardized currency amounts (unified currency units and precision).

[0074] In the data validation and quality assessment phase, the system has established a multi-dimensional validation rule system. Through single-field validity checks (verifying whether field values ​​conform to predefined rules), cross-field correlation checks (checking the logical relationships between fields), and business rule compliance checks (ensuring that the data conforms to business logic), the system ensures that the data meets the quality requirements at all levels.

[0075] The system uses a weighted calculation method to evaluate data quality and calculates a quality score using a quality scoring formula:

[0076] Q score =w1·Completeness+w2·Accuracy+w3·Consistency;

[0077] Among them, Q score The values ​​represent the quality score; w1 represents the weighting coefficient for completeness, with a default value of 1 / 3, used to control the importance of completeness in the overall quality score; w2 represents the weighting coefficient for accuracy, with a default value of 1 / 3, used to control the importance of accuracy in the overall quality score; w3 represents the weighting coefficient for consistency, with a default value of 1 / 3, used to control the importance of consistency in the overall quality score; Completeness is the data completeness score, ranging from [0, 1], representing the data field fill rate and validity; Accuracy is the data accuracy score, ranging from [0, 1], representing the correctness and precision of data values; Consistency is the data consistency score, ranging from [0, 1], representing the logical relationships and rule compliance between data; each score is normalized to the [0, 1] interval to ensure comparability of the indicators.

[0078] Meanwhile, the system generates detailed quality reports, including statistics on problem distribution, quality trend analysis, and improvement suggestions, providing a basis for continuous optimization of data quality.

[0079] In one embodiment of the present invention, the core conversion function of the data adapter is defined as:

[0080]

[0081] in, This represents the standardized output data of the i-th tenant platform, which is the final result of the adapter processing; Adapter iThis represents the data adapter for the i-th tenant platform, a function that converts raw data into a standard format, responsible for performing operations such as data cleaning, field mapping, and format conversion; This represents the raw input data of the i-th tenant platform, the initial data without any processing; Schema i This represents the data schema configuration for the i-th tenant platform, defining the data structure, field types, and constraints; Mapping i This represents the field mapping rules for the i-th tenant platform, used to map raw field names to standard field names; Rules i This represents the data processing rule set of the i-th tenant platform, which includes specific rules for data cleaning, transformation, and verification.

[0082] This function employs a multi-step data processing workflow, including validity checks, data cleaning, field mapping, quality assessment, and data augmentation, to ultimately generate standardized data with quality labels. This multi-layered data adaptation mechanism ensures effective integration and quality assurance of heterogeneous data sources.

[0083] The semantic alignment layer process implements a cross-platform semantic alignment mechanism based on knowledge graphs, which includes two core components: entity alignment and relation mapping.

[0084] In terms of entity semantic representation learning, the system adopts a multimodal feature fusion method, which integrates text features (text semantic vectors extracted using BERT), structural features (node ​​embedding vectors based on graph structure) and contextual features (semantic vectors enhanced with domain knowledge) through a feature fusion algorithm.

[0085] To optimize feature learning performance, the system introduces a contrastive learning mechanism, which improves the discriminativeness and robustness of feature representations by minimizing the distance between positive sample pairs (different representations of the same entity) while maximizing the distance between negative sample pairs (representations of different entities).

[0086] In the knowledge graph construction and alignment stage, the system first constructs a basic knowledge graph through entity extraction (identifying entities in text based on named entity recognition technology), relation extraction (extracting relationships between entities from text based on remote supervised learning), and attribute alignment (aligning entity attributes based on pattern matching rules).

[0087] Subsequently, the system uses the TransE method for graph embedding, generating a vector representation of the knowledge graph using a formula:

[0088] E KG =TransE(G KG ,d dim ,γ);

[0089] Among them, GKG The input knowledge graph structure includes a set of entities and a set of relations; TransE represents modeling relations as translation operations within the entity embedding space; d dim γ is the dimension of the embedding vector, used to control the complexity of the representation; γ is a marginal parameter used to control the discrimination between positive and negative samples; E KG The embedding matrix is ​​used to embed the output knowledge graph.

[0090] The core idea of ​​the TransE method is to model relations as translation operations in entity embedding space. That is, for a correct triple (head entity, relation, tail entity), the embedding vector of the head entity plus the embedding vector of the relation should be close to the embedding vector of the tail entity.

[0091] The system implements a multi-strategy semantic matching mechanism. For entity alignment scoring, a weighted combination method is used to calculate the alignment score:

[0092] Score align (e i ,e j )=w4·sim text (e i ,e j )+w5·sim struct (e i ,e j )+w6·sim attr (e i ,e j );

[0093] Among them, Score align (e i ,e j The score represents the alignment similarity score between two entities, which measures the degree of semantic matching between them. A higher score indicates that the two entities are more likely to refer to the same concept or object; i and e j These represent the two entities to be aligned; sim text This represents a BERT-based text similarity function; sim struct Represents a graph-based similarity function; sim attr This represents the attribute-based similarity function; w4, w5, and w6 are the weight coefficients of the text similarity function, the graph structure similarity function, and the attribute similarity function, respectively.

[0094] In terms of relation mapping learning, the system achieves dynamic relation mapping through an attention mechanism, adaptively learning the mapping relation based on the features of the source relation and the target relation.

[0095] Attention mechanisms achieve dynamic combination of relation representations by calculating the similarity between the query vector and the key vector, and using these similarities to perform a weighted summation of the value vectors.

[0096] To ensure alignment quality, the system implements a strict consistency check mechanism. Through transitivity verification (if entity A is aligned with B, and B is aligned with C, then A should be aligned with C), mutual exclusion verification (ensuring that an entity can be aligned with at most one target entity), and domain constraint verification (ensuring that the alignment result conforms to domain knowledge rules), the system guarantees the reliability of the alignment result.

[0097] Meanwhile, the system calculates alignment confidence through multi-evidence fusion, comprehensively evaluating the degree of support for the alignment results from different evidence sources (such as text similarity, structural similarity, attribute matching, etc.).

[0098] The final semantic alignment results are filtered by setting a similarity threshold (default 0.8, used to filter alignment results with low similarity) and a confidence threshold (default 0.7, used to filter alignment results with low confidence) to ensure that only high-quality alignment results are retained. This multi-layered semantic alignment mechanism ensures the semantic consistency and reliability of cross-platform data.

[0099] Progressive blending layer processing:

[0100] The system implements a progressive data fusion mechanism based on multi-dimensional evaluation to ensure the accuracy and real-time nature of data fusion.

[0101] In terms of multi-dimensional quality assessment, the system first establishes an assessment framework for basic quality dimensions and calculates basic quality scores through weighted combination.

[0102] The rating comprehensively considers three key dimensions: data completeness (data field fill rate), accuracy (correctness of data values), and consistency (logical relationships between data), with weights set at 0.4, 0.4, and 0.2 respectively, to balance the importance of each dimension.

[0103] In terms of timeliness assessment, the system adopts an exponential decay model to calculate the timeliness score based on the data delay time.

[0104] The system is set to allow a maximum delay of 24 hours, and the rate of timeliness decay is controlled by a timeliness decay coefficient (default 0.1). This design ensures that the timeliness score of the data will decrease exponentially with the increase of delay time, but will not drop to zero completely.

[0105] For credibility assessment, the system uses a multi-indicator averaging method to calculate the overall credibility of the data, comprehensively considering the credibility of the data source, the credibility of the processing process, and the credibility of the verification results.

[0106] In the adaptive weight calculation stage, the system comprehensively considers basic quality, timeliness, and reliability through the core weight calculation formula:

[0107]

[0108] in, This represents the basic weight calculated at time t, which is used for weighting different data sources during the data fusion process; The baseline quality score at time t reflects the basic quality characteristics of the data, such as completeness, accuracy, and consistency. The timeliness score at time t represents the freshness and time relevance of the data. represents the credibility score at time t, reflecting the reliability of the data source and the credibility of the processing process; j represents the index of the data source, an integer from 1 to k; k represents the total number of data sources, i.e., the number of different data sources participating in the fusion. This represents the summation operation over all k data sources, used for normalizing weights.

[0109] The system will dynamically adjust these weights based on historical fusion results, and continuously optimize the weight allocation through a feedback learning mechanism.

[0110] In terms of incremental data fusion, the system adopts a gradual fusion strategy for time-series data, and achieves dynamic data updates through formulas:

[0111]

[0112] in, This represents the fusion result at time t, i.e., the fused data at the current time point; η represents the fusion result at the previous time point (t-1), i.e., the fused data at the previous time point; η2 represents the update rate parameter, which controls the degree of influence of new data on the fusion result, and is set to 0.3 to balance the stability of historical data and the real-time nature of new data; k represents the total number of data sources participating in the fusion; i represents the index of the data source, an integer from 1 to k; This represents the weight of the i-th data source at time t, reflecting the importance and reliability of that data source; This represents the standardized input data provided by the i-th data source at time t; This indicates the handling of data conflicts in the weighted summation operation across all k data sources. The system processes these conflicts according to a predefined set of conflict resolution rules, including priority rules (based on data source credibility), timestamp rules (based on data update time), and majority voting rules (based on data consistency), to ensure the consistency of the fusion results.

[0113] In the quality feedback and optimization phase, the system evaluates the fusion results through multi-dimensional quality assessment indicators, including data consistency (checking the logical relationship of the fused data), completeness (assessing the coverage of data fields), accuracy (verifying the correctness of data values), and timeliness (checking the timeliness of data updates).

[0114] The system dynamically adjusts the fusion parameters based on the evaluation results, adopts a small step size (default 0.01) learning strategy, and continuously optimizes the parameter configuration through the gradient descent algorithm to ensure continuous improvement of the fusion effect.

[0115] The final fusion result consists of three main parts: fused data (including data values ​​and their attributes), fusion quality score (reflecting the reliability of the fusion result), and fusion metadata information (recording key parameters and decisions of the fusion process).

[0116] This gradual fusion mechanism not only ensures the accuracy of data fusion, but also enables continuous optimization of the fusion process through dynamic adjustments and quality feedback.

[0117] Missing data imputation:

[0118] The system implements a high-precision missing data imputation mechanism based on a deep generative model, which includes three main steps: data preprocessing, model training, and imputation optimization.

[0119] During the data preprocessing and analysis phase, the system first performs a comprehensive missing pattern analysis, including MCAR (completely random missing, where the missing data is completely random and unrelated to the data itself) test, MAR (random missing, where the missing data is related to other observed variables) assessment, and MNAR (non-random missing, where the missing data is related to unobserved variables) identification.

[0120] The system assesses the degree of missing data by calculating the missing rate (the ratio of the number of missing values ​​to the total sample size), providing a basis for selecting subsequent processing strategies.

[0121] In the feature preprocessing stage, the system uses a standardization method to process continuous features, mapping the data to the interval [-1, 1]. This normalization process can eliminate scale differences between different features and improve the stability of model training.

[0122] For discrete features, the system employs multiple encoding methods: One-Hot encoding (converting categorical variables into binary vectors) is used to handle unordered categorical features, label encoding (mapping categories to integers) is used to handle ordered categorical features, and embedding encoding (mapping categories to low-dimensional dense vectors) is used to handle high cardinality categorical features.

[0123] At the same time, the system generates a binary mask matrix to mark the missing data locations for subsequent model training.

[0124] In terms of deep generative model design, the system implements an encoder-decoder architecture based on a variational autoencoder (VAE). The encoder encodes features of the masked input data using a multilayer perceptron to generate the distribution parameters of the latent variables, and then samples them using reparameterization techniques.

[0125] The core optimization goal is:

[0126] L total =L recon +β·KL(q φ (z|x)||p(z));

[0127] Among them, L total L represents the total loss function, which is the overall optimization objective of model training; recon The reconstruction loss represents the difference between the reconstructed data and the original data, typically calculated using mean squared error or cross-entropy loss; KL represents the KL divergence (Kullback-Leibler divergence) regularization term, used to ensure that the latent variable distribution approximates a standard normal distribution, preventing overfitting; q φ (z|x) represents the posterior distribution generated by the encoder, φ represents the encoder parameters, z represents the latent variable, and x represents the input data; p(z) represents the prior distribution, which is usually set to the standard normal distribution N(0,1) as the expected distribution of the latent variable; β represents the trade-off parameter (default value is 1.0), which is used to control the balance between reconstruction loss and KL divergence regularization term. A larger β value will enhance the regularization effect, while a smaller β value will prioritize reconstruction accuracy; || represents the KL divergence calculation symbol between the two distributions.

[0128] In the interpolation strategy optimization stage, the system adopts a multi-interpolation ensemble method, which improves the stability of interpolation by sampling and averaging multiple times.

[0129] Specifically, the system performs multiple (10 times by default) sampling predictions for each missing value. Each sampling randomly samples a latent variable based on the latent variable distribution generated by the encoder, and then generates a possible missing value through the decoder.

[0130] The results of these multiple samplings are weighted and averaged to obtain the final imputed value, with the weights adaptively adjusted based on the uncertainty of each prediction. The system assesses the uncertainty of the imputed value by calculating the variance of these sampling results; a larger variance indicates higher uncertainty. Based on the uncertainty estimate, the system calculates the confidence level of each imputed value, with higher confidence levels receiving higher weights in subsequent analyses.

[0131] To ensure the quality of imputation, the system implements a comprehensive verification mechanism. Through methods such as random mask verification (randomly marking known values ​​as missing and verifying the accuracy of imputation), pattern similarity verification (checking whether the imputed values ​​maintain the statistical characteristics of the original data), and distribution consistency testing (verifying whether the distribution of the imputed data is consistent with the original distribution), the system comprehensively evaluates the imputation effect.

[0132] The quality assessment adopts a multi-indicator weighted approach, comprehensively considering key indicators such as root mean square error, mean absolute error, and coefficient of determination.

[0133] The final interpolation result consists of four main parts: complete interpolated data (including original and interpolated values), interpolation confidence (reflecting the reliability of each interpolated value), quality score (comprehensively reflecting the interpolation effect), and process metadata (recording key parameters and decisions of the interpolation process).

[0134] This deep generative model-based imputation mechanism can not only accurately estimate missing values, but also provide reliable uncertainty assessment, thus providing strong support for improving data quality.

[0135] Output result:

[0136] The system's output mainly includes a multi-confidence hierarchical data structure, which divides the data into three levels: high, medium, and low, based on the confidence level of the data.

[0137] The high-confidence layer contains data with a confidence level exceeding 0.8. This data has undergone rigorous verification and quality assessment and can be directly used for key decisions and core business logic.

[0138] The medium confidence level contains data with confidence levels between 0.5 and 0.8. While these data are of reliable quality, they may contain some uncertainty and are mainly used to assist in analysis and risk assessment.

[0139] The low confidence layer contains data with a confidence level below 0.5. This data may have quality issues or anomalous patterns and requires manual review or additional verification. It is mainly used for anomalous pattern detection and quality improvement.

[0140] The system also generates a detailed data quality assessment report, including assessment results from multiple dimensions. The quality indicator set covers four core dimensions: completeness, accuracy, consistency, and timeliness.

[0141] In addition, the system maintains complete metadata information, recording key information about the processing flow. This information includes three main parts: processing flow logs (recording each step of data processing, execution time, and processing results), configuration parameter sets (containing parameter settings, threshold configurations, and rule definitions for each processing module), and statistical summary information (containing data distribution characteristics, quality indicator statistics, and summaries of anomalies).

[0142] This metadata provides important information for monitoring and optimizing system operation, helping managers understand the entire data processing process and promptly identify and resolve potential problems.

[0143] Step 2: Construct identity feature vectors through multimodal feature extraction, use Bayesian networks to perform identity reasoning, quantify uncertainty, and construct a probabilistic identity association graph;

[0144] Input content: Multi-confidence hierarchical data structure D from step 1 layered It includes user behavior data, transaction data, and device data that have undergone quality assessment and standardization.

[0145] Processing procedure:

[0146] Multimodal feature extraction:

[0147] The system extracts user identity features from five dimensions and constructs a feature vector:

[0148] F identity =[F behavior ,F temporal ,F geo ,F network ,F device ];

[0149] Among them, F identity The comprehensive feature vector representing user identity consists of five sub-vectors: behavioral features, time features, geographic features, network features, and device features. Each dimension's features have undergone standardization and dimension alignment. Specifically, they include:

[0150] Behavioral characteristics F behavior The system extracts user purchase preference vectors (based on product category, price range, and purchase frequency), browsing patterns (including page visit sequence and dwell time distribution), and operation habits (such as click patterns and scrolling behavior) through deep learning models. A pre-trained Transformer model is used to process the behavior sequence, capturing the temporal dependencies of user behavior.

[0151] Time feature F temporal The system analyzes the temporal patterns of user activity, including active time periods (24-hour activity distribution), behavior frequency (daily / weekly / monthly activity frequency), and periodicity patterns (identifying the periodicity of behavior through Fourier transform). The system normalizes each temporal feature to ensure comparability across different time scales.

[0152] Geographical features F geoThe system constructs geographic features based on user location information, including frequently used addresses (identifying high-frequency activity areas through density clustering), movement trajectories (extracting key waypoints using trajectory compression algorithms), and location preferences (based on location type and access frequency). The system employs geohashing encoding to ensure the privacy of location information.

[0153] Network feature F network The system analyzes users' network access characteristics, including IP address ranges (using CIDR notation), network type (such as mobile network, fixed broadband, etc.), and connection mode (such as connection duration, bandwidth usage, etc.). It uses feature hashing technology to map high-dimensional network features to a fixed-dimensional space.

[0154] Equipment Feature F device The system extracts characteristic information from user devices, including device fingerprints (generated based on hardware and software configuration), operating system (version and configuration information), and browser characteristics (including UserAgent parsing results and Canvas fingerprints). The system employs a standardized device feature extraction protocol to ensure cross-platform consistency.

[0155] Bayesian identity reasoning:

[0156] The system employs a Bayesian network model to model the association probability between virtual and real identities. Given the characteristics of a virtual identity, it calculates the posterior probability of its corresponding real identity.

[0157] The core of the model is Bayes' theorem:

[0158]

[0159] Among them, P(ID) real ID virtual Given virtual identity features, P(ID) represents the posterior probability of inferring a real identity, indicating the probability that a particular virtual identity feature corresponds to a specific real identity given that a certain virtual identity feature has been observed. virtual ID real P(ID) is the likelihood function, representing the probability that a real identity will generate a specific virtual identity feature; that is, the conditional probability of observing a specific virtual identity feature given the real identity. real P(ID) represents the prior probability of real identity, based on the distribution of real identities derived from historical data statistics, reflecting the system's initial belief in real identities; virtual ) represents the marginal probability of virtual identity features, used to normalize the posterior probability and ensure that the sum of the probabilities is 1.

[0160] The system simplifies calculations by employing the feature independence assumption, decomposing the likelihood function into a product of conditional probabilities for each feature dimension:

[0161]

[0162] Among them, P(ID) virtual ID real ) represents the conditional probability of observing a specific virtual identity feature given that the real identity is known; This represents the value of the j-th feature dimension of the virtual identity; This represents the value of the j-th feature dimension representing the true identity. represents the conditional probability of observing a specific virtual identity feature given the real identity feature values; j represents the index of the feature dimension, from 1 to m; m represents the total number of feature dimensions, i.e., the number of all features used for identity association; ∏ represents the chain multiplication operation, which multiplies the conditional probabilities of all feature dimensions.

[0163] The system calculates the conditional probability of continuous features using kernel density estimation and the conditional probability of discrete features using frequency statistics.

[0164] Quantification of uncertainty:

[0165] The system uses information entropy to measure the uncertainty of identity association, and the calculation formula is as follows:

[0166]

[0167] Among them, H(ID) real ID virtual ) represents the conditional entropy of the real identity given the characteristics of the virtual identity, and is used to quantify the uncertainty of identity association; Let represent the posterior probability of the i-th candidate real identity, that is, the probability that a given virtual identity feature corresponds to the i-th real identity; ∑ i This represents the summation of all possible real identity candidates; log represents the natural logarithm function, used to calculate the information content; i represents the index of the real identity, traversing all possible candidate real identities.

[0168] Based on the magnitude of entropy, the system classifies uncertainty into three levels:

[0169] High certainty: H < 0.5, indicating that the identity association is highly reliable; Medium certainty: 0.5 ≤ H < 1.0, indicating that there is some uncertainty; Low certainty: H ≥ 1.0, indicating that the identity association is less reliable.

[0170] Dynamic confidence update:

[0171] The system employs a recursive Bayesian method to dynamically update the confidence level of identity associations as new data arrives.

[0172]

[0173] Among them, P (t+1) (ID real ID virtual P represents the posterior probability at time t+1, that is, the probability of inferring the real identity given the updated virtual identity; (t) (ID real ID virtual () represents the posterior probability at time t, i.e., the probability of inferring the true identity obtained in the previous round of calculation; evidence (t+1) This represents newly acquired evidence at time t+1 (such as new behavioral data, transaction records, login information, etc.); η is a normalization constant to ensure that the sum of the posterior probabilities of all possible real identities is 1, calculated as the sum of the reciprocals of all probabilities; P(evidence) (t+1) ID real Let P(evidence) be the likelihood function of the new evidence, representing the probability of observing the new evidence given the known true identity; (t+1) ) represents the marginal probability of new evidence, and represents the overall probability of observing new evidence.

[0174] The system uses a sliding window mechanism to process time-series data. The default window size is 30 days, which can be dynamically adjusted according to business needs. Data within the window is assigned a higher weight, while historical data outside the window is reduced in impact using an exponential decay method.

[0175] Construction of probabilistic identity association graph:

[0176] Based on the above calculation results, the system constructs a probabilistic identity association graph:

[0177] G prob =(V,E,P);

[0178] Among them, G prob This represents a probabilistic identity association graph; the node set V represents each node as a virtual identity, containing its feature vector and a set of candidate real identities; the edge set E represents the association relationships between virtual identities, constructed based on feature similarity and behavioral relevance; the probability set P represents the weight of each edge as the association probability. Calculated using joint probability distribution.

[0179] The system employs a graph partitioning algorithm to divide large-scale association graphs into blocks, improving computational efficiency. Simultaneously, regular graph structure updates and probability recalculations ensure the timeliness of association relationships.

[0180] Output result:

[0181] Probabilistic Identity Association Graph G prob It includes node features, edge relationships, and probability weights;

[0182] Identity uncertainty matrix U ij The matrix representing the uncertainty measure of the association between identities i and j is constructed as follows:

[0183] For each pair of virtual identities (i,j), the system combines the conditional entropy H(ID) real ID virtual Calculate the associated uncertainty:

[0184]

[0185] Among them, U ij This represents the uncertainty measure of the association between the i-th and j-th identities in the uncertainty matrix; This represents the conditional entropy for inferring the i-th real identity given the j-th virtual identity. This represents the conditional entropy for inferring the j-th real identity given the i-th virtual identity; α1 is a balance factor used to adjust the weight ratio of the two-way conditional entropy in the calculation, with a default value of 0.5, indicating that the conditional entropy in both directions has equal importance.

[0186] The matrix element value range is [0, log(k)], where k is the maximum number of candidate real identities;

[0187] The lower the uncertainty value, the higher the credibility of the identity association.

[0188] Step 3: A federated graph neural network is used for distributed feature learning. The feature learning network is constructed through graph segmentation and multi-head attention mechanism to achieve cross-tenant secure computation and differential privacy protection.

[0189] Input: The probabilistic identity association graph G from step 2 prob The uncertainty matrix U contains node features, edge relationships, and probability weight information.

[0190] Processing procedure:

[0191] Data preprocessing and standardization:

[0192] The system first performs comprehensive preprocessing and standardization on the input data:

[0193] Continuous feature processing: The Z-score normalization method is used to map the data to a standard normal distribution, thereby achieving feature normalization.

[0194] Categorical feature processing: For categorical features, the system employs different encoding strategies based on the cardinality.

[0195] Low cardinality categories (<10 values): Use One-Hot encoding;

[0196] Medium cardinality categories (10-100 values): use label encoding;

[0197] High cardinality categories (>100 values): use embedding layer technology and ensure reasonable weight distribution through Xavier initialization;

[0198] Temporal feature processing: Timestamps are converted into relative time intervals, and logarithmic transformation and normalization are applied to make the temporal features more suitable for model learning.

[0199] Graph partitioning and distributed deployment:

[0200] The system uses an improved Louvain algorithm for graph segmentation, and the core steps include:

[0201] Initialization: Initialize each node as an independent community.

[0202] Optimization phase: Calculate modularity gain

[0203]

[0204] Where ΔQ represents the modularity gain after moving node i from its current community to the target community; ∑ in k represents the sum of edge weights within the target community. i,in ∑ represents the sum of edge weights between node i and nodes within the target community; tot k represents the sum of the weights of all edges connected to the target community. i represents the degree of node i (the sum of the weights of all edges connected to node i); m represents the sum of the weights of all edges in the graph, which is the normalization factor for the entire network.

[0205] Community Merging and Quality Assessment: Community merging is performed based on modularity gain, and the segmentation quality is assessed using metrics such as internal density, external density, and balance.

[0206] A distributed computing architecture is built based on the partitioning results, including mechanisms such as data sharding, load balancing, and communication optimization.

[0207] Local feature learning:

[0208] The system constructs a graph neural network (GAT) based on a multi-head attention mechanism. The core computations include:

[0209] Attention coefficient calculation:

[0210]

[0211] Where, α ijdenoted by , represents the attention coefficient from node i to node j, i.e., the importance weight of node j to node i; 'a' represents the parameter vector of the attention mechanism, which are learnable model parameters; 'W' represents the linear transformation matrix used for feature projection; 'h' represents the attention coefficient from node i to node j. i and h j Let [Wh] represent the feature vectors of node i and node j, respectively; i ||Wh j The symbol ] represents the concatenation operation of the transformed feature vectors of node i and node j; LeakyReLU represents the modified linear unit activation function with a negative slope to avoid gradient vanishing. Let represent the set of neighboring nodes of node i; exp represents the natural exponential function, used to convert the attention score to a positive value; This formula represents the summation of all neighboring nodes k of node i, used to normalize the attention weights; this formula calculates the attention weights between nodes, achieving adaptive feature aggregation.

[0212] Feature aggregation and update: Efficient learning and updating of node features are achieved through multi-head attention mechanism and residual connection.

[0213] Loss function optimization:

[0214]

[0215] in, This represents the total loss function, which is the overall optimization objective for model training. This represents the supervised learning loss, used to measure the difference between the model's predictions and the true labels; λ represents the structure preservation loss, used to preserve the topological properties of the graph structure; λ1 represents the weight coefficient of the structure preservation loss, used to balance the importance between supervised learning and structure preservation; λ2 represents the weight coefficient of the regularization term, used to control model complexity; Θ represents the set of model parameters; ||Θ||2 represents the L2 norm of the model parameters, used as a regularization term to prevent overfitting; combining supervised learning loss and structure preservation loss ensures the effectiveness of feature learning.

[0216] Cross-tenant boundary node processing:

[0217] The system employs a Shamir-based (t,n) threshold scheme to achieve secure multi-party computation.

[0218] Secret sharing generation: Constructing polynomials over finite fields and generating sharing.

[0219] Secure computation protocol: Enables secure computation of vector dot products and matrix multiplication.

[0220] Zero-knowledge proofs: Proofs are generated and verified to ensure the correctness of computation results.

[0221] Parameter aggregation and privacy protection:

[0222] The system employs a differential privacy-enhanced federated averaging algorithm:

[0223] Local update:

[0224]

[0225] in, This represents the model parameters of the i-th tenant after the t-th iteration; η1 represents the model parameters of the i-th tenant after the (t-1)-th iteration; η1 represents the learning rate, which controls the step size of each parameter update. Represents the loss function For parameters The gradient indicates the direction of parameter updates; i represents the tenant index, used to distinguish model parameters for different tenants; t represents the iteration round, used to track the progress of model training.

[0226] Differential privacy protection: Protects the privacy of model updates through noise injection and dynamic budget allocation.

[0227] Aggregation optimization: Implements mechanisms such as gradient compression and asynchronous updates to improve communication efficiency.

[0228] Uncertainty Modeling and Feature Propagation:

[0229] The system implements probabilistic feature representation and uncertainty propagation:

[0230] Feature distribution modeling: Gaussian distribution is used to represent the uncertainty of node features.

[0231] Uncertainty propagation:

[0232]

[0233] in, This represents the uncertainty variance of the output feature of node i, quantifying the degree of uncertainty in the feature representation; α represents the set of neighboring nodes of node i, i.e., all nodes directly connected to node i; j represents the index of a neighboring node, traversing all neighbors of node i; ij This represents the attention weight of node i to its neighbor node j, reflecting the degree of influence of neighbor node j on node i; The variance of the feature uncertainty of neighbor node j represents the degree of uncertainty of the feature of neighbor node j. This represents the inherent uncertainty variance of the model, reflecting the predictive uncertainty of the model itself.

[0234] Quality control: Ensure feature quality through uncertainty thresholds and regular evaluations.

[0235] Output result:

[0236] The system outputs the following key results:

[0237] Distributed node embedding H: This includes low-dimensional feature representations and uncertainty estimates of the nodes. The embedding is calculated using the Gaussian distribution representation and uncertainty propagation formula from the aforementioned uncertainty modeling and feature propagation steps. Each node's representation includes both a feature vector and the corresponding uncertainty measure.

[0238] Global Feature Extractor F global The pre-trained GAT model supports incremental updates and privacy protection. This extractor is trained using the aforementioned differential privacy-enhanced federated averaging algorithm.

[0239] Quality Assessment Report: This report includes evaluation metrics such as feature representation quality, privacy protection level, and computational efficiency. Based on the uncertainty threshold and periodic evaluation results from the aforementioned quality control mechanism, this report comprehensively reflects the performance of feature learning.

[0240] Step 4: Perform hierarchical threat detection, including rapid screening, refined detection, and rule verification, and use an improved SIR model for risk propagation modeling;

[0241] After obtaining the distributed node embedding and global feature extractor from step 3, the system begins to perform hierarchical threat detection and risk assessment. First, the system uses a sliding window technique to achieve real-time behavior monitoring, with the window size set to 1 hour and the step size to 5 minutes. In this way, it continuously extracts features such as user behavior frequency, time interval, and behavior sequence, and performs standardization and hierarchical sampling to ensure data quality.

[0242] During the rapid threat screening phase, the system employs a feature importance assessment method based on information gain, calculating the contribution of features to the target variable using a formula:

[0243] IG(Y,X)=H(Y)-H(Y|X);

[0244] Wherein, IG(Y,X) represents the information gain of feature X on target variable Y, quantifying the contribution of feature X to reducing the uncertainty of Y; H(Y) represents the entropy of target variable Y, measuring the magnitude of uncertainty of Y, with a larger entropy value indicating higher uncertainty; H(Y|X) represents the conditional entropy of target variable Y under the condition that feature X is known, measuring the uncertainty of Y under the condition that X is known; X represents the input feature used to predict the target variable; Y represents the target variable, i.e. the threat type or risk level that needs to be predicted.

[0245] Based on the evaluation results, the system uses a regularized linear classifier for rapid judgment and continuously updates the model parameters through an online learning mechanism.

[0246] In the refined detection stage, the system constructs a detection framework based on ensemble learning.

[0247] Multi-model fusion prediction is achieved through formulas:

[0248]

[0249] Where P(y|x) represents the final fusion prediction probability of the target variable y given the input feature x; K represents the total number of base models in the ensemble learning; k represents the index of the base model, an integer from 1 to K; w k P represents the weight coefficient of the k-th base model, reflecting the importance of that model in the final prediction; k (y|x) represents the prediction probability of the target variable y by the k-th base model given the input feature x; This represents a weighted summation operation over all K basic models.

[0250] The system selects Random Forest (500 decision trees, maximum depth 6) and XGBoost (200 base learners, learning rate 0.1) as the base models, and controls the model complexity through L1 and L2 regularization.

[0251] To ensure the reliability of the test results, the system incorporates an expert system for verification. Verification rules are defined using formal rule representations.

[0252] Rule i :(Condition i →Action i ,Conf i );

[0253] Among them, Rule i A unique identifier for a rule, used to distinguish different validation rules; Condition i This indicates the triggering conditions of the rule, defining the scenarios and preconditions under which the rule applies; Action i This indicates the actions to be performed after a rule is triggered, including verification and response measures; Conf i The confidence level of a rule reflects the reliability and importance of the rule's judgment result.

[0254] The system resolves rule conflicts based on rule priority and confidence level to ensure the consistency of verification results.

[0255] In terms of risk propagation modeling, the system describes the risk propagation process in the network based on an improved SIR model. The dynamic changes in risk states are characterized by a set of state transition equations:

[0256]

[0257] Where S represents the proportion of nodes in a susceptible state, i.e., the proportion of nodes that have not yet been affected by the risk but may be infected out of the total number of nodes; I represents the proportion of nodes in an infected state, i.e., the proportion of nodes that are currently affected by the risk out of the total number of nodes; R represents the proportion of nodes in a recovered state, i.e., the proportion of nodes that were previously affected by the risk but have recovered out of the total number of nodes; β1 represents the infection rate, which quantifies the speed at which the risk spreads in the network, with a larger value indicating faster risk spread; α represents the recovery rate, which quantifies the speed at which a node recovers from an infected state to a recovered state, with a larger value indicating faster recovery; γ1 represents the immune failure rate, which quantifies the speed at which a node changes back to a susceptible state from a recovered state, with a larger value indicating a shorter duration of immunity; These represent the rates of change of the proportions of susceptible, infected, and recovered nodes over time, respectively; t represents the time variable used to track the dynamic evolution of risk status over time.

[0258] The system uses the maximum likelihood estimation method to estimate the model parameters, controlling the relative error to be within 1e-6.

[0259] To adapt to dynamic changes in the risk landscape, the system implements an adaptive risk weight adjustment mechanism. Risk weights are updated using the following formula:

[0260]

[0261] Among them, w t The risk weight at time t represents the risk level of different risk factors, used to quantify their importance; w t-1 η represents the risk weight at the previous time step t-1, which serves as the base value for weight updates; η3 represents the dynamically adjusted learning rate, which controls the step size of weight updates. A larger value will lead to more aggressive adjustments. This indicates that the loss function L is applied to the weight w at the previous time step. t-1 The gradient indicates the direction and magnitude of the weight adjustment; L represents the loss function, used to evaluate the effectiveness of the current risk weights, and is usually calculated based on the prediction error.

[0262] The system ensures that the weights are non-negative and sum to 1 through constraints, thus guaranteeing the rationality of weight adjustments.

[0263] Based on the results of collaborative learning and differential privacy protection, the system generates comprehensive risk regulatory decision-making recommendations.

[0264] Calculate the optimal decision using the formula:

[0265]

[0266] Where, d * argmax represents the optimal decision, i.e., the best regulatory decision option recommended by the system under the current risk situation; D represents the set of all possible decision options, including regulatory measures and intervention strategies at different levels; argmax d∈D ∑ represents the decision that maximizes the objective function among all possible decision options; S represents the set of all possible risk states, describing the various risk situations the system may face; s represents a specific risk state, an element in set S, representing a possible risk situation; e represents the currently observed evidence or data used to assess the probability distribution of the risk state; P(s|e) represents the posterior probability of risk state s given evidence e, reflecting the system's estimate of the current risk state; U(d,s) represents the utility value of taking decision d under risk state s, quantifying the expected benefit of the decision; ∑ s∈S This represents the operation of weighted summation over all possible risk states.

[0267] The system balances security, compliance, and business continuity through multi-objective optimization to ensure the comprehensiveness and feasibility of decisions.

[0268] Ultimately, the system outputs three key results:

[0269] Risk situation report, including threat type distribution, risk level assessment and propagation trend prediction;

[0270] Security strategy recommendations, providing targeted defense measures and control strategies;

[0271] Compliance assessments ensure that security measures comply with relevant regulations and standards. The system displays these results through visual dashboards, supporting decision-makers in quickly understanding and responding to risk situations.

[0272] Step 5: Implement collaborative learning with differential privacy enhancement, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, to build a privacy-preserving model;

[0273] After obtaining threat detection and risk assessment results, the system enters the collaborative learning and regulatory decision-making phase. First, the system conducts a comprehensive privacy sensitivity assessment of different types of data, including personal information such as personal identification and behavioral characteristics, business data such as transaction records and operation logs, and relationship data such as social relationships and interaction records.

[0274] Based on the sensitivity assessment results, the system implements a dynamic privacy budget allocation mechanism. The privacy budget for each component is calculated using the following formula:

[0275]

[0276] Where, ∈ i This represents the privacy budget allocated to the i-th component, used to control the amount of privacy-preserving resources that component can use; ∈ total This represents the system's total privacy budget, which is the sum of the upper limits of the privacy budgets of all components; w i s represents the importance weight of the i-th component, reflecting its priority and importance in the entire system; i represents the sensitivity score of the i-th component, which quantifies the privacy sensitivity of the data processed by that component; n represents the total number of components in the system; j represents the index of the component, an integer from 1 to n; This represents the sum of the products of the importance weights and sensitivity scores of all components, used as a normalization factor.

[0277] The system dynamically adjusts the budget allocation based on usage and protection effectiveness.

[0278] Regarding differential privacy protection, the system employs different protection mechanisms for different types of data. For numerical data, the system uses the Laplace mechanism to add noise:

[0279]

[0280] Where M(x) represents the processed data after adding noise, i.e. the output result after differential privacy protection; x represents the original data, i.e. the input data value that needs to be protected for privacy. Let represent random noise sampled from a Laplace distribution, with the distribution's scale parameter being... Δf represents sensitivity, which quantifies the maximum impact of a change in a single record on the query results. A larger value indicates more sensitive data. ∈ represents privacy budget, which controls the strength of privacy protection. A smaller value indicates a higher degree of privacy protection, but it will lead to a decrease in data utility. + represents the operation of adding random noise to the original data, which is the core step in achieving differential privacy protection.

[0281] For discrete data, the system uses an exponential mechanism for randomization to ensure data privacy.

[0282] In the secure aggregation phase, the system employs a homomorphic encryption scheme to achieve secure computation. Data aggregation in the encrypted state is supported through encrypted computation formulas.

[0283] E(x+y)=E(x)·E(y);

[0284] Where E(·) represents the homomorphic encryption function, which is a mathematical operation that converts plaintext data into ciphertext; x represents the original data value, which may come from different tenants or data sources; y represents the second original data value that needs to be aggregated, which participates in the aggregation calculation together with x; x+y represents the addition operation performed on data x and y in plaintext, which is the basic operation of aggregation; E(x) represents the ciphertext result after homomorphic encryption of data x; E(y) represents the ciphertext result after homomorphic encryption of data y; E(x)·E(y) represents the multiplication operation performed on encrypted data in ciphertext, which corresponds to the addition operation in plaintext.

[0285] The system optimizes performance and improves computational efficiency through batch processing and parallel computing.

[0286] To compensate for the performance loss caused by privacy protection, the system introduces knowledge distillation technology. Knowledge transfer is achieved through formula generation using soft tags.

[0287]

[0288] Where, q i z represents the soft label probability of the i-th category, which is the target output in the knowledge distillation process, and its value ranges from [0,1]. i exp(z) represents the prediction score (logits) of the original model for the i-th class, reflecting the model's confidence in that class; i / T) indicates that the scaled predicted scores are subjected to an exponential transformation to enhance the numerical discrimination; T represents the temperature parameter, which controls the smoothness of the soft label distribution; a larger T value will produce a smoother probability distribution; ∑ j exp(z j / T) represents the sum of all categories after scaling and exponential transformation, used as a normalization factor; j represents the index of the category, traversing all possible categories.

[0289] A smart regulatory decision support framework is established. This framework employs a combination of Bayesian decision theory and multi-objective optimization, calculating the risk-weighted expected utility using the following formula:

[0290] EU(d)=∑ s∈S P(s|e)·[w1U security (d,s)+w2U compliance (d,s)+w3U business (d,s)];

[0291] Where EU(d) represents the expected utility of decision option d, taking into account security, compliance, and business continuity; w1, w2, and w3 represent the weight coefficients of security, compliance, and business continuity, respectively, and satisfy w1 + w2 + w3 = 1; U security(d,s) represents the safety utility of decision d under risk state s; U compliance (d,s) represents the compliance utility of decision d under risk state s; U business (d,s) represents the business continuity utility of decision d under risk state s; P(s|e) represents the probability distribution of risk state s inferred based on current evidence e.

[0292] The system ultimately selects the decision scheme d that maximizes expected utility. * .

[0293] The system balances security, compliance, and business continuity through multi-objective optimization to ensure the comprehensiveness and feasibility of decisions.

[0294] This step ultimately outputs the following three key categories of results:

[0295] Collaborative Learning Model Set: The system generates a set of differentially privacy-preserving collaborative learning models, each focusing on a specific type of risk detection, including:

[0296] Anomaly Transaction Detection Model: A transaction behavior analysis model based on differential privacy protection;

[0297] Identity fraud identification model: a fraud detection model that combines identity association graphs;

[0298] Attack pattern recognition model: A real-time recognition model for novel attack techniques;

[0299] These models maintain high accuracy through knowledge distillation techniques while meeting privacy protection requirements.

[0300] Privacy-preserving data assets: The system generates a set of data assets D that have undergone differential privacy processing. privacy ,include:

[0301] Anonymized User Behavior Summary: User behavior data that retains behavioral patterns but removes personal identifiers;

[0302] Cryptographic Transaction Pattern Library: A collection of transaction patterns protected by homomorphic encryption;

[0303] Statistical report with differential privacy protection: various statistical indicators and trend analysis after adding precise noise;

[0304] These data assets can be securely shared among different tenants and regulatory bodies, supporting collaborative analysis without disclosing sensitive information.

[0305] Adaptive Regulatory Strategy Framework: The system outputs a dynamically adjusted regulatory strategy framework. adaptive ,Include:

[0306] Dynamic risk threshold adjustment mechanism: Automatically adjusts risk assessment thresholds based on historical data and current situation;

[0307] Tiered Response Strategy Library: Standardized response processes and measures for different risk levels;

[0308] Tenant-specific regulatory rules: A set of regulatory rules customized to take into account the business characteristics of each tenant;

[0309] Compliance checklist: Check items to ensure that regulatory measures comply with relevant laws and standards;

[0310] This framework uses an optimal decision formula to calculate and minimize the impact on normal business operations while ensuring security.

[0311] These outputs collectively constitute a complete differential privacy-enhanced collaborative learning and oversight decision-making system, providing core functional support for subsequent cloud-native microservice architecture deployments. Through these results, the system achieves the goal of effectively identifying risks and formulating reasonable oversight strategies while protecting data privacy.

[0312] Step 6: Deploy the security model on a cloud-native microservice architecture to achieve multi-layered protection through service governance, security protection, and observability systems;

[0313] After completing the design of each functional module, the system was deployed using a cloud-native microservice architecture. Kubernetes was chosen as the container orchestration platform, Istio was used to implement the service mesh functionality, and a complete monitoring system was built using Prometheus and Grafana. This architectural design ensures high availability and scalability of the system.

[0314] In terms of service registration and discovery, the system implements a dynamic service management mechanism. Each service registers using standardized registration information, including service identifier, name, version, endpoint, and health check details. The system leverages DNS and service mesh technologies to achieve dynamic service discovery and routing.

[0315] In terms of traffic governance, the system implements a complete traffic management mechanism. It controls traffic distribution between services through fine-grained routing rules, supporting canary releases and A / B testing. The system implements a circuit breaker mechanism based on error rate and latency thresholds to prevent the cascading propagation of service failures.

[0316] In terms of security, the system has constructed a multi-layered security mechanism. It ensures the security of service access through diverse authentication methods and fine-grained access control, protects data transmission with TLS encryption, and ensures the security of multi-tenant environments through resource isolation.

[0317] In terms of observability, the system has established a comprehensive monitoring framework. By collecting metrics at the system, business, and container levels, it achieves unified log collection and analysis, supports end-to-end request tracing, and sets multi-level alerting policies to ensure real-time monitoring of system operation status and rapid response to issues.

[0318] like Figure 2 As shown, in one embodiment of the present invention, a multi-tenant e-commerce platform security monitoring system based on big data analysis is provided, comprising:

[0319] The hierarchical heterogeneous data adaptation module is used to perform hierarchical adaptation and standardization of heterogeneous data on multi-tenant platforms, achieve semantic alignment based on knowledge graphs, and form a multi-confidence data structure through progressive fusion.

[0320] The identity association analysis module is used to construct identity feature vectors through multimodal feature extraction, realize identity reasoning using Bayesian networks, quantify uncertainty, and construct a probabilistic identity association graph.

[0321] The federated graph neural network module is used for distributed feature learning using federated graph neural networks. It constructs a feature learning network through graph segmentation and multi-head attention mechanisms to achieve cross-tenant secure computation and differential privacy protection.

[0322] The hierarchical threat detection module is used to perform hierarchical threat detection, including rapid screening, fine-grained detection, and rule verification, and uses an improved SIR model for risk propagation modeling.

[0323] A differential privacy-enhanced collaborative learning module is used to implement differential privacy-enhanced collaborative learning, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, to build a privacy-preserving model;

[0324] The cloud-native microservice architecture module is used to deploy security models on cloud-native microservice architectures, achieving multi-layered protection through service governance, security protection, and observability systems.

[0325] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for security supervision of multi-tenant e-commerce platforms based on big data analysis, characterized in that: include: The heterogeneous data of the multi-tenant platform is layered for adaptation and standardization, semantic alignment is achieved based on knowledge graph, and a multi-confidence data structure is formed by progressive fusion. This includes data adaptation layer performing data format verification, missing value handling, outlier detection, field mapping and transformation. The semantic alignment layer achieves cross-platform semantic alignment based on knowledge graphs, including entity alignment and relation mapping; The progressive fusion layer integrates the multi-source data after the above two layers of processing based on the data quality, timeliness, and reliability, and finally forms a data structure with three confidence levels: high, medium, and low. Identity feature vectors are constructed by extracting behavioral, temporal, geographical, network, and device features. Bayesian networks are used for identity reasoning, uncertainty is quantified, and a probabilistic identity association graph is built, including: The system uses a Bayesian network model to model the probability of association between virtual and real identities: ； Among them, P(ID) real |ID virtual Given virtual identity features, infer the posterior probability of the real identity; P(ID) virtual |ID real P(ID) is the likelihood function, representing the probability that a real identity will generate a specific virtual identity characteristic; real P(ID) represents the prior probability of a real identity, based on the distribution of real identities derived from historical data statistics; virtual ) represents the marginal probability of virtual identity features, used to normalize the posterior probability; Construction of probabilistic identity association graph: G prob =(V,E,P); Among them, G prob This represents a probabilistic identity association graph; the node set V represents each node as a virtual identity, containing its feature vector and a set of candidate real identities; the edge set E represents the association relationships between virtual identities, constructed based on feature similarity and behavioral relevance; the probability set P represents the weights of the edges, which are the association probabilities calculated through a joint probability distribution. Federated graph neural networks are used for distributed feature learning. The feature learning network is constructed through graph segmentation and multi-head attention mechanism to achieve cross-tenant secure computation and differential privacy protection. Perform hierarchical threat detection, including rapid screening, refined detection, and rule validation, and employ an improved SIR model for risk propagation modeling, including: ； ； ； Where S represents the proportion of nodes in a susceptible state; I represents the proportion of nodes in an infected state; R represents the proportion of nodes in a recovered state; β1 represents the infection rate; α represents the recovery rate; and γ1 represents the immune failure rate, which quantifies the speed at which a node changes from a recovered state back to a susceptible state. The larger the value, the shorter the duration of immunity. To achieve differential privacy-enhanced collaborative learning, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, a privacy-preserving model is constructed. Deploy the security model in a cloud-native microservice architecture to achieve multi-layered protection through service governance, security protection, and observability systems.

2. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The data quality score for layered adaptation and standardization of heterogeneous data on multi-tenant platforms is as follows: ； Among them, Q score The completeness score represents the overall quality score; w1 represents the weighting coefficient for completeness, used to control the importance of completeness indicators in the overall quality score; w2 represents the weighting coefficient for accuracy, used to control the importance of accuracy indicators in the overall quality score; w3 represents the weighting coefficient for consistency, used to control the importance of consistency indicators in the overall quality score; Completeness is the data completeness score, representing the data field fill rate and validity; Accuracy is the data accuracy score, representing the correctness and precision level of data values; Consistency is the data consistency score, representing the logical relationships and rule compliance between data.

3. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The likelihood function in the identity feature vector constructed through multimodal feature extraction is calculated as follows: ； Among them, P(ID) virtual |ID real ) represents the conditional probability of observing a specific virtual identity feature given that the real identity is known; This represents the value of the j-th feature dimension of the virtual identity; This represents the value of the j-th feature dimension representing the true identity. This represents the conditional probability of observing a specific virtual identity feature value given that the real identity feature value is known; j represents the index of the feature dimension, from 1 to m; m represents the total number of feature dimensions, i.e., the number of all features used for identity association. This indicates a chain multiplication operation, which multiplies the conditional probabilities of all feature dimensions.

4. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The loss function for distributed feature learning in federated graph neural networks is: ； Among them, L total L represents the total loss function, which is the overall optimization objective of model training; recon Represents the reconstruction loss, used to measure the difference between the reconstructed data and the original data; KL represents the KL divergence regularization term; q Φ (z|x) represents the posterior distribution generated by the encoder, Φ represents the encoder parameters, z represents the latent variable, and x represents the input data; p(z) represents the prior distribution; β represents the trade-off parameter used to control the balance between the reconstruction loss and the KL divergence regularization term; ∥ represents the KL divergence calculation symbol between the two distributions.

5. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The risk propagation prediction in hierarchical threat detection employs an improved SIR model, including: Susceptible state S: Nodes that may be affected by the risk; Infected state I: Nodes where the risk has been confirmed; Recovered state R: Nodes where the risk has been controlled; The risk transmission process is described by the infection rate β, recovery rate γ, and immune failure rate δ.

6. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The dynamic confidence level is updated as follows: ； Among them, P (t+1) (ID real |ID virtual P represents the posterior probability at time t+1; (t) (ID real |ID virtual () represents the posterior probability at time t; evidence (t+1) P represents the newly acquired evidence at time t+1; η is a normalization constant that ensures the sum of the posterior probabilities of all possible true identities is 1; P(evidence) (t+1) |ID real Let P(evidence) be the likelihood function of the new evidence, representing the probability of observing the new evidence given the known true identity; (t+1) ) represents the marginal probability of new evidence, and represents the overall probability of observing new evidence.

7. The method for security supervision of a multi-tenant e-commerce platform based on big data analysis according to claim 1, characterized in that, The security model is deployed in a cloud-native microservice architecture. It enables automatic registration and health checks of system components through service registration and discovery mechanisms, adopts DNS-based service resolution to achieve dynamic load balancing, and supports multi-level failover strategies to ensure service availability.

8. A multi-tenant e-commerce platform security monitoring system based on big data analysis, characterized in that: include: The hierarchical heterogeneous data adaptation module is used to perform hierarchical adaptation and standardization of heterogeneous data on multi-tenant platforms. It achieves semantic alignment based on knowledge graphs and adopts progressive fusion to form a multi-confidence data structure. The data adaptation layer performs data format verification, missing value handling, outlier detection, field mapping and transformation. The semantic alignment layer achieves cross-platform semantic alignment based on knowledge graphs, including entity alignment and relation mapping; The progressive fusion layer integrates the multi-source data after the above two layers of processing based on the data quality, timeliness, and reliability, and finally forms a data structure with three confidence levels: high, medium, and low. The identity association analysis module is used to construct identity feature vectors by extracting behavioral, temporal, geographical, network, and device features, perform identity reasoning using Bayesian networks, quantify uncertainty, and construct a probabilistic identity association graph, including: The system uses a Bayesian network model to model the probability of association between virtual and real identities: ； Among them, P(ID) real |ID virtual Given virtual identity features, infer the posterior probability of the real identity; P(ID) virtual |ID real P(ID) is the likelihood function, representing the probability that a real identity will generate a specific virtual identity characteristic; real P(ID) represents the prior probability of a real identity, based on the distribution of real identities derived from historical data statistics; virtual ) represents the marginal probability of virtual identity features, used to normalize the posterior probability; Construction of probabilistic identity association graph: G prob =(V,E,P); Among them, G prob This represents a probabilistic identity association graph; the node set V represents each node as a virtual identity, containing its feature vector and a set of candidate real identities; the edge set E represents the association relationships between virtual identities, constructed based on feature similarity and behavioral relevance; the probability set P represents the weights of the edges, which are the association probabilities calculated through a joint probability distribution. The federated graph neural network module is used for distributed feature learning using federated graph neural networks. It constructs a feature learning network through graph segmentation and multi-head attention mechanisms to achieve cross-tenant secure computation and differential privacy protection. The hierarchical threat detection module performs hierarchical threat detection, including rapid screening, fine-grained detection, and rule validation. It employs an improved SIR model for risk propagation modeling, including: ； ； ； Where S represents the proportion of nodes in a susceptible state; I represents the proportion of nodes in an infected state; R represents the proportion of nodes in a recovered state; β1 represents the infection rate; α represents the recovery rate; and γ1 represents the immune failure rate, which quantifies the speed at which a node changes from a recovered state back to a susceptible state. The larger the value, the shorter the duration of immunity. A differential privacy-enhanced collaborative learning module is used to implement differential privacy-enhanced collaborative learning, including budget allocation, Laplace mechanism, homomorphic encryption and knowledge distillation, to build a privacy-preserving model; The cloud-native microservice architecture module is used to deploy security models on cloud-native microservice architectures, achieving multi-layered protection through service governance, security protection, and observability systems.

9. A multi-tenant e-commerce security monitoring system based on big data analysis according to claim 8, characterized in that, Real-time monitoring and evaluation of data quality, dynamic adjustment of processing strategies; use homomorphic encryption to achieve secure computing and protect data privacy; automatic detection and recovery capabilities for system failures to ensure continuous service availability.

10. A multi-tenant e-commerce security monitoring system based on big data analysis according to claim 8, characterized in that, It adopts a distributed architecture for deployment, supporting horizontal scaling of the system; and achieves dynamic routing and load balancing through a service mesh. Implement fine-grained access control policies; establish comprehensive monitoring and tracing mechanisms.

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