A multi-layer certificate verification method and system based on digital certificate

Abstract

The present invention proposes a multi-layer certificate verification method and system based on digital certificates, the method comprising: performing a first-layer verification on the basic information of the certificate; if the first-layer verification passes, performing a second-layer verification on the sensitive information in the certificate; if the second-layer verification passes, access is allowed, and a behavior analysis model is used to perform real-time analysis on the current user behavior to identify potential abnormal behaviors and their patterns, wherein the behavior analysis model uses Transformer as the basic architecture, combined with a graph neural network, to capture the global dependencies in the behavior sequence and analyze the local structure and relationship patterns in the user behavior; if abnormal behavior is identified, access rights are terminated or an alarm is triggered according to the abnormal behavior pattern. The present invention uses a behavior analysis model that integrates Transformer and graph neural networks to perform real-time analysis on user behavior to identify potential abnormal behaviors, which can well identify potential access risk issues in complex application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of digital certificate verification, and in particular to a multi-layer certificate verification method and system based on digital certificates. Background Art

[0002] Digital certificates, as an essential means of ensuring network security, have been widely used in various fields. Digital certificate verification is particularly important in complex application scenarios such as enterprise-level security systems, financial institutions, cloud computing environments, the Internet of Things, and advanced persistent threat detection.

[0003] In these complex application scenarios, user behavior, device interactions, and network environments are highly complex and diverse. Traditional digital certificate verification methods rely primarily on checking basic certificate information (such as signature, validity period, issuer, etc.) and possibly verifying the certificate chain. However, this traditional verification method often falls short in the face of complex attack vectors, failing to effectively identify and prevent potential security threats. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to propose a multi-layer certificate verification method and system based on digital certificates to solve the above-mentioned problems.

[0005] A multi-layer certificate verification method based on digital certificates proposed in the present invention includes:

[0006] Perform the first level of verification on the basic information of the certificate;

[0007] If the first level of verification passes, the second level of verification is performed on the sensitive information in the certificate;

[0008] If the second-tier verification passes, access is allowed, and a behavioral analysis model is used to analyze the current user behavior in real time to identify potential abnormal behaviors and their patterns. The behavioral analysis model uses Transformer as its infrastructure, combined with a graph neural network, to capture global dependencies in behavior sequences and analyze local structures and relationship patterns in user behavior.

[0009] If abnormal behavior is identified, access rights will be terminated or an alarm will be triggered based on the abnormal behavior pattern.

[0010] Furthermore, the behavior analysis model is used to analyze the current user behavior in real time to identify potential abnormal behaviors and their patterns, including:

[0011] Collect user behavior data on the network platform in real time;

[0012] Preprocess behavioral data;

[0013] Extract key behavioral features from pre-processed behavioral data, including the timestamp of the user's behavior, behavior type, behavior object, behavior frequency, the user's social relationships in the network, geographic location, and device usage habits;

[0014] Convert key behavioral features into behavioral sequences to reflect the user's behavioral trajectory;

[0015] Deeply analyze the behavior sequence through the Transformer layer to capture global dependencies across time steps and extract a global representation;

[0016] Analyze the local structure and association patterns in the behavior sequence through the graph neural network layer to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes;

[0017] The global representation and the node representation are organically integrated to integrate global context information and local details to extract comprehensive user behavior features;

[0018] The user's comprehensive behavior features are further processed through a fully connected layer, and the softmax function is used to output the category of abnormal behavior patterns.

[0019] Furthermore, the Transformer layer performs in-depth analysis on the behavior sequence, captures global dependencies across time steps, and extracts a global representation, including:

[0020] Convert each behavior vector in the behavior sequence into a high-dimensional dense real number vector, setting it as an embedding vector;

[0021] In the Transformer layer, for each embedding vector in the behavior sequence, calculate the attention score between it and all other embedding vectors;

[0022] The output processed by the self-attention mechanism is passed through a feedforward neural network for further nonlinear transformation;

[0023] The encoder of the Transformer layer performs multi-level self-attention processing on the behavior sequence, extracts global context information, and outputs a global representation of the behavior sequence. The global representation contains the global dependencies in the behavior sequence and the behavior characteristics of the user at different time steps.

[0024] Furthermore, the graph neural network layer analyzes the local structure and association patterns in the behavior sequence to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes, including:

[0025] Each behavior or entity in the behavior sequence is defined as a node in a graph, where the nodes include users, devices, applications, and web pages;

[0026] Define edges based on relationships between behaviors or entities;

[0027] Assign initial attributes to each node, including the node type, characteristics and status;

[0028] For each node in the graph, the graph neural network layer receives and aggregates information from all its neighboring nodes;

[0029] Update the representation of the current node based on the aggregated neighbor information;

[0030] Through multi-layer graph convolution operations, information from distant neighbors is gradually integrated to obtain the final embedded representation of the node, which includes the characteristics of the node itself, information of neighboring nodes, and structural information of the entire graph.

[0031] Furthermore, the user's comprehensive behavior features are further processed by the fully connected layer, and the category of abnormal behavior patterns is output using the softmax function, including:

[0032] Performing a linear transformation on the user's comprehensive behavioral features through a fully connected layer to extract a feature combination that is sensitive to abnormal behavior patterns;

[0033] Apply the softmax function to perform exponential operation on the output of the fully connected layer and normalize it to obtain the probability distribution of each abnormal behavior pattern category;

[0034] Traverse the probability distribution and find the highest probability value and its corresponding category;

[0035] Compare the highest probability value with a preset threshold;

[0036] If the highest probability value exceeds the preset threshold and the corresponding category is marked as an abnormal behavior pattern, the user's behavior is determined to be abnormal, and the category with the highest probability is used as the predicted user abnormal behavior pattern category;

[0037] If the highest probability value does not exceed the preset threshold, or the category corresponding to the highest probability value is marked as normal behavior, the user's behavior is determined to be normal behavior.

[0038] Furthermore, the second level of verification of the sensitive information in the certificate includes:

[0039] If there is only one verification participant, a public-private key pair is generated;

[0040] Define a verification function based on verification requirements;

[0041] Write a smart contract that includes verification logic;

[0042] Extract sensitive information to be verified from digital certificates;

[0043] Use the public key to homomorphically encrypt the sensitive information to obtain the encrypted ciphertext of the sensitive information;

[0044] Use the private key and homomorphic encryption to decrypt or calculate the encrypted sensitive information ciphertext to execute the verification function and obtain the verification result;

[0045] Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the verification results.

[0046] Furthermore, the second level verification of the sensitive information in the certificate also includes:

[0047] If there is more than one verification participant, generate a public-private key pair for each participant;

[0048] Each participant keeps their own private key, divides the private key into multiple shares, distributes each share to other participants or third parties, and discloses the public key to all other participants;

[0049] Define a verification function based on verification requirements;

[0050] Write a smart contract that includes verification logic and secret sharing reconstruction logic;

[0051] Extract sensitive information to be verified from digital certificates;

[0052] Use their respective public keys to homomorphically encrypt sensitive information to obtain the encrypted sensitive information ciphertext, and send the encrypted sensitive information ciphertext to all other participants;

[0053] When verification is required, for other participants, the private key is rebuilt according to the reconstruction logic of secret sharing;

[0054] Using the reconstructed private key share and homomorphic encryption operations, partially decrypt or calculate the encrypted sensitive information ciphertext to perform part of the verification function;

[0055] After multiple rounds of partial computation, in each round, different participants use their private key shares to perform partial computation and pass the results to the participants of the next round, and finally obtain the verification result;

[0056] Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the final verification results.

[0057] Furthermore, the first level of verification of the basic information of the certificate includes:

[0058] Extract basic information from the digital certificate provided by the user, including the issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate;

[0059] The issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate are verified separately.

[0060] The present invention also proposes a multi-layer certificate verification system based on digital certificates, the system comprising:

[0061] First verification module: used to perform the first level verification on the basic information of the certificate;

[0062] Second verification module: used to perform a second-level verification on the sensitive information in the certificate using homomorphic encryption if the first-level verification passes;

[0063] Behavior Analysis Module: This module allows access if the second-tier verification passes, and uses a behavior analysis model to analyze current user behavior in real time to identify potential abnormal behaviors and their patterns. The behavior analysis model uses Transformer as its underlying architecture, combined with graph neural networks, to capture global dependencies in behavior sequences and analyze local structures and relationship patterns in user behavior.

[0064] Decision-making module: If abnormal behavior is identified, it will terminate access rights or trigger an alarm based on the abnormal behavior pattern.

[0065] In summary, the multi-layer certificate verification method based on digital certificates of the present invention uses a behavioral analysis model that integrates Transformer and graph neural network to perform real-time analysis of user behavior to identify potential abnormal behaviors and their patterns, so as to be able to well identify potential access risk issues in complex application scenarios. Specifically, through the efficient sequence modeling capabilities of Transformer, the subtle changes and potential patterns in time series data such as user behavior and device interaction are accurately captured and analyzed; at the same time, the deep parsing power of GNN for graph structure data is utilized to deeply analyze the complex association networks and dependencies between nodes (such as users, devices and applications). The digital certificate verification method of the present invention achieves efficient identification and rapid response to potential security threats while ensuring data privacy and security, and also has adaptability and robustness to different application scenarios.

[0066] Additional aspects and advantages of the present invention will be set forth in part in the following description and, in part, will be obvious from the following description, or may be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0068] Figure 1 This is a flowchart of a multi-layer certificate verification method based on digital certificates according to the first embodiment of the present invention;

[0069] Figure 2 This is a system block diagram of a multi-layer certificate verification system based on digital certificates according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0070] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0071] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0073] Example 1

[0074] See also Figure 1 The present invention proposes a multi-layer certificate verification method based on digital certificates, the method comprising steps S101 to S104:

[0075] S101, perform the first level verification on the basic information of the certificate.

[0076] It's important to note that this first-level verification of a certificate's basic information is intended to quickly screen out obviously non-compliant or invalid certificates, providing a foundation for subsequent, more in-depth verification. By verifying a certificate's basic information, we ensure that it superficially meets legality and validity requirements, thereby preventing communication with untrusted entities. This first-level verification focuses on basic certificate information, including but not limited to the digital certificate's issuing authority, validity period, serial number, holder information, and digital signature.

[0077] During the verification process, after receiving the certificate, the browser or client first reads the basic information in the certificate. This information is usually in plain text, which is convenient for quick reading and verification.

[0078] When verifying the issuing authority, the browser or client searches the built-in trusted CA list for an entry that matches the issuer CA in the certificate. If a matching CA is found, verification succeeds; otherwise, verification fails.

[0079] When checking the certificate validity period, the browser or client obtains the current time and compares it with the validity period field in the certificate. If the current time is within the validity period, the verification passes; otherwise, the verification fails.

[0080] When verifying the certificate holder, the owner information in the certificate is compared with the expected owner. If the information is consistent, the verification passes, otherwise the verification fails.

[0081] When verifying the certificate serial number, it is necessary to verify whether the certificate serial number is consistent with the serial number of the issuer's certificate. This is to prevent the certificate from being forged or tampered with. The specific operation usually involves checking whether the AuthorityKeyIdentifier extension Cert Serial Number (serial number of the issuing certificate) in the certificate matches the CertificateSerial Number (certificate serial number) in the CA certificate.

[0082] When verifying a digital signature, you can use the public key of the issuing authority (CA) to decrypt and verify the digital signature in the certificate. If the decryption is successful and the decrypted content is consistent with the certificate content, it means that the certificate has not been tampered with after issuance.

[0083] When performing the first level of verification, you should ensure that the browser or client used is secure and reliable to avoid being tampered with or bypassing the verification process by malicious software.

[0084] S102: If the first level verification is passed, a second level verification is performed on the sensitive information in the certificate.

[0085] It should be noted that in a multi-layer verification system based on digital certificates, after the first layer of verification (preliminary verification of the basic information of the certificate such as the issuing authority, validity period, certificate subject, etc.) is passed, the second layer of verification of sensitive information in the certificate will be entered to further ensure the authenticity and integrity of the certificate, especially for those sensitive data involving security, privacy or high value.

[0086] First, the sensitive information requiring verification is accurately extracted from the certificate. This information may include, but is not limited to, the public key, private key, certificate extension fields, and specific attributes. This extracted sensitive information is then encrypted and verified. Specifically, the properties of homomorphic encryption can be leveraged to perform calculations or comparisons on the encrypted sensitive information without decryption to verify its validity. The verification logic is embedded in a smart contract, which automatically executes the verification process and determines the certificate's validity based on the verification results.

[0087] In the case of multiple verification parties, sensitive information can be divided into multiple parts through a secret sharing mechanism, and the sensitive information can be reconstructed through multi-party cooperation when necessary to ensure the security and reliability of the verification. For example, in financial transactions involving multiple financial institutions or payment platforms, the certificates and sensitive information (such as account balances, transaction records, etc.) of both parties to the transaction need to be verified to ensure the legality and security of the transaction. In the case where sensitive data (such as census data, medical data, etc.) need to be shared between government departments for policy formulation and analysis, but personal privacy needs to be protected at the same time. In cross-enterprise cooperation projects, different companies need to exchange sensitive data for collaborative work, but are worried about the risk of data leakage.

[0088] The second layer of verification in the embodiments of the present invention complements and strengthens the first layer of verification, further improving the overall security of certificates and reducing potential security vulnerabilities. Performing a second layer of verification on sensitive information in certificates allows for in-depth verification and comprehensive protection of sensitive information, providing enhanced security for certificate use. In certain industries or scenarios, performing a second layer of verification on sensitive information in certificates is essential for meeting compliance requirements.

[0089] S103: If the second-level verification passes, access is allowed, and the current user behavior is analyzed in real time through a behavior analysis model to identify potential abnormal behaviors and their patterns. The behavior analysis model uses Transformer as the basic architecture and combines it with a graph neural network to capture the global dependencies in the behavior sequence and analyze the local structure and relationship patterns in the user behavior.

[0090] It should be noted that in the security mechanism based on the multi-level verification system of this embodiment, after the second-level verification is passed, artificial intelligence technology (Transformer combined with graph neural network) is used to perform real-time analysis of user behavior to further ensure the security of the system.

[0091] In complex application scenarios such as enterprise security systems, financial institutions, cloud computing environments, the Internet of Things, and advanced persistent threat detection, user behavior, device interactions, and network environments are highly complex and diverse. Initial verification at the first and second levels is insufficient to fully assess user authenticity and effectively identify and prevent potential security threats in the face of complex attack vectors. Real-time analysis of user behavior, however, can identify any unusual or suspicious activity patterns, enabling timely decision-making to prevent unauthorized access and potential security threats.

[0092] The behavior analysis model is based on the Transformer architecture, which excels at processing sequential data. It focuses not only on current behavior data but also on previous behavior data, capturing global dependencies within the behavior sequence—that is, long-range relationships between user behaviors. It is also combined with graph neural networks (GNNs) to analyze local structures and relational patterns within user behavior. This means that the behavior analysis model focuses not only on the behavior sequence itself but also on the correlations and patterns between behaviors, such as the frequency and order of user access to specific resources. The behavior analysis model monitors and analyzes user behavior in real time, ensuring timely detection of any abnormal behavior.

[0093] By capturing global dependencies through Transformers and analyzing local structures and relationship patterns through graph neural networks, the system provides a comprehensive understanding of user behavior. Based on the learned behavioral patterns, the system can identify abnormal behavior and make access decisions based on the results of user behavior analysis.

[0094] For example, in enterprise-level security systems, behavioral analysis models can collect behavioral data such as employee browsing history, email communications, and file access to identify abnormal behavior patterns, such as unauthorized data access and the spread of malware. Once abnormal behavior is detected, the system can immediately trigger an alarm mechanism to notify the security team for further investigation and handling.

[0095] In financial institutions, behavioral analysis models can identify potential fraudulent activities, such as unusually large transactions and frequent fund transfers, by analyzing customer transaction behavior, fund flow, and other behavioral data. These abnormal behaviors are often signs of fraudulent activity, allowing financial institutions to take timely measures, such as freezing accounts and contacting customers to confirm transactions, to prevent potential losses.

[0096] On government service websites or apps, behavioral analysis models can monitor and analyze user behavior patterns in real time, such as access paths, dwell time, and number of clicks, to detect potential security threats such as unauthorized access and malicious attacks. Once abnormal behavior is detected, the system can immediately trigger an alarm mechanism and take appropriate security measures, such as blocking access and recording logs.

[0097] Through real-time behavioral analysis, the system can more accurately identify potential security threats, and the model can be updated as user behavior changes to maintain its effectiveness.

[0098] By combining Transformers and graph neural networks to analyze user behavior in real time, we provide additional security for the multi-layered verification system. By comprehensively capturing and analyzing the global and local characteristics of user behavior, the system can more accurately identify abnormal behavior and make timely and effective access decisions based on this information.

[0099] S104: If abnormal behavior is identified, the access permission is terminated or an alarm is triggered according to the abnormal behavior pattern.

[0100] It should be noted that if abnormal behavior is identified, decisions are made quickly and measures are taken based on the abnormal behavior pattern to minimize potential losses. For example, when abnormal behavior is determined to be a potential security threat or illegal operation, such as unauthorized access, data leakage attempts, malware activity, etc., the user's access rights are immediately terminated, which may include logging out of the session, banning the IP address, etc., to prevent the continuation of the abnormal behavior and protect system resources and data security. When the abnormal behavior reaches or exceeds the preset threshold, or is considered a serious security incident, an alarm message is sent, which may include a detailed description of the abnormal behavior, the time of occurrence, the users or devices involved, etc., through email, SMS, system logs, etc.

[0101] Based on steps S101 to S104, the present invention uses a behavioral analysis model that integrates Transformer and graph neural network to perform real-time analysis of user behavior to identify potential abnormal behaviors and their patterns, so as to be able to well identify potential access risk issues in complex application scenarios. Specifically, through the efficient sequence modeling capabilities of Transformer, subtle changes and potential patterns in time series data such as user behavior and device interaction are accurately captured and analyzed; at the same time, the deep parsing power of GNN for graph structured data is utilized to deeply analyze the complex association networks and dependencies between nodes (such as users, devices and applications). The digital certificate verification method of the present invention achieves efficient identification and rapid response to potential security threats while ensuring data privacy and security, and also has adaptability and robustness to different application scenarios.

[0102] The following is a further detailed description of a multi-layer certificate verification method based on digital certificates according to an embodiment of the present invention:

[0103] Further optionally, in step S103, the current user behavior is analyzed in real time using a behavior analysis model to identify potential abnormal behaviors and patterns, including:

[0104] Collect user behavior data on the network platform in real time;

[0105] Preprocess behavioral data;

[0106] Extract key behavioral features from pre-processed behavioral data, including complex information such as user behavior timestamp, behavior type, behavior target, behavior frequency, user's social relationships in the network, geographic location, and device usage habits;

[0107] Convert key behavioral features into behavioral sequences to reflect the user's behavioral trajectory;

[0108] Deeply analyze the behavior sequence through the Transformer layer to capture global dependencies across time steps and extract a global representation;

[0109] Analyze the local structure and association patterns in the behavior sequence through the graph neural network layer to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes;

[0110] The global representation and the node representation are organically integrated to integrate global context information and local details to extract comprehensive user behavior features;

[0111] The user's comprehensive behavior features are further processed through a fully connected layer, and the softmax function is used to output the category of abnormal behavior patterns.

[0112] Understandably, real-time user behavior data on online platforms is collected and pre-processed, including data cleaning, data filling, and data conversion, to address data noise (such as invalid data, erroneous data, etc.), missing values (such as users not filling in certain information), and heterogeneity (such as differences in data formats generated by different devices and applications). Key behavioral features are then extracted from the pre-processed data. Behavioral feature types include timestamp, behavior type (such as clicks, purchases, comments, etc.), behavior object (such as products, articles, users, etc.), behavior frequency (such as the number of behaviors within a certain period of time), social relationships (such as following, being followed, etc.), geographic location (such as the user's region), and device usage habits (such as commonly used device types and operating systems, etc.).

[0113] Key behavioral features within a preset time period are converted into behavioral sequences to organize the user's behavioral data in chronological order to form a behavioral trajectory. Each behavior can be represented as a vector, and these vectors are arranged in chronological order to form a behavioral sequence. The behavioral sequence is input into the Transformer layer, which performs in-depth analysis of the behavioral sequence to capture global dependencies across time steps, understand the user's overall behavioral patterns in the network environment, and output a global representation, namely the user's overall behavioral characteristics throughout the entire behavioral sequence. The graph neural network layer then performs a detailed analysis of the local structure and association patterns in the behavioral sequence, deeply exploring the complex relationships and interaction patterns between nodes (such as between users and devices, applications, and other users), and outputs detailed node representations of all nodes, such as the specific characteristics and relationships of each user, device, application, etc. in the network.

[0114] Then, the global representation and the node representation are organically integrated. The global representation and the node representation can be integrated together by splicing, weighted summation, etc. to integrate global context information and local details, and extract the user's comprehensive behavioral characteristics. The comprehensive behavioral characteristics can comprehensively reflect the user's overall behavioral patterns and local interaction relationships on the network platform.

[0115] The fully connected layer further processes the user's comprehensive behavioral features and uses the softmax function to output the category of abnormal behavior patterns, enabling accurate assessment and classification of user behavior, especially in complex and volatile network environments. The fully connected layer performs nonlinear transformations and classifications on the user's comprehensive behavioral features. The softmax function converts the output into a probability distribution, representing the likelihood of each abnormal behavior pattern and outputting the category of the abnormal behavior pattern, such as fraudulent behavior or malicious attack.

[0116] Further optionally, the behavior sequence is deeply analyzed through the Transformer layer to capture the global dependencies across time steps and extract a global representation, including:

[0117] Convert each behavior vector in the behavior sequence into a high-dimensional dense real number vector, setting it as an embedding vector;

[0118] In the Transformer layer, for each embedding vector in the behavior sequence, the attention score between it and all other embedding vectors is calculated as follows:

[0119] Among them, Q, K, and V represent query, key, and value matrices respectively, which are usually obtained by linear transformation of the input embedding vector. k is the dimension of the bond matrix;

[0120] The output processed by the self-attention mechanism is passed through a feedforward neural network for further nonlinear transformation;

[0121] The encoder of the Transformer layer performs multi-level self-attention processing on the behavior sequence, extracts global context information, and outputs a global representation of the behavior sequence. The global representation contains the global dependencies in the behavior sequence and the behavior characteristics of the user at different time steps.

[0122] As you can understand, the Transformer layer performs in-depth analysis of the behavior sequence to capture global dependencies across time steps and extract a global representation. First, each behavior vector in the behavior sequence is converted into a high-dimensional, dense real-number vector, or embedding vector. This is because the original behavior vector may be sparse or low-dimensional and cannot fully represent the complexity of the behavior. By converting it to a high-dimensional, dense vector, the associations and characteristics between behaviors can be better captured. This conversion can be accomplished using an embedding layer or a pre-trained embedding model (such as Word2Vec or BERT).

[0123] In the Transformer layer, for each embedding vector in the action sequence, an attention score is calculated between it and all other embedding vectors to capture global dependencies between actions. This means that a particular action may be associated with multiple other actions in the sequence. The output of the self-attention mechanism is then passed through a feedforward neural network for further nonlinear transformations to increase the model's expressive power and enable it to capture more complex features. Feedforward neural networks can be constructed using fully connected layers and activation functions.

[0124] In the Transformer layer, in order to alleviate the problems of gradient vanishing and gradient exploding during deep network training, residual connections and layer normalization can be used to add the input embedding vector to the output of the self-attention mechanism and the feedforward neural network, and then normalize it to ensure that the network remains stable during training and accelerate convergence.

[0125] To capture more complex global dependencies, multiple Transformer layers can be stacked. Each layer takes the output of the previous layer as input and outputs a higher-level global representation. By chaining multiple Transformer layers together to form a deep network, and extracting features layer by layer, we can capture higher-level global dependencies and behavioral characteristics. The Transformer layer's encoder performs multi-level self-attention on the behavior sequence, extracting global contextual information and outputting a global representation of the behavior sequence. This global representation incorporates the global dependencies in the behavior sequence as well as the user's behavioral characteristics at different time steps.

[0126] Through the Transformer layer, we conduct in-depth analysis of behavior sequences, effectively capturing global dependencies across time steps and extracting a global representation containing rich information, which facilitates understanding user behavior and improves the accuracy of the system in detecting abnormal behavior.

[0127] Further optionally, the graph neural network layer is used to analyze the local structure and association patterns in the behavior sequence to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes, including:

[0128] Each behavior or entity in the behavior sequence is defined as a node in the graph. For example, users, devices, applications, web pages, etc. can all be nodes.

[0129] Define edges based on relationships between behaviors or entities;

[0130] Assign initial attributes to each node, including the node type, characteristics and status;

[0131] For each node in the graph, the graph neural network layer receives and aggregates information from all its neighboring nodes;

[0132] Update the representation of the current node based on the aggregated neighbor information;

[0133] Through multi-layer graph convolution operations, information from more distant neighbors is gradually integrated to obtain the final embedded representation of the node, which includes the characteristics of the node itself, information of neighboring nodes, and structural information of the entire graph.

[0134] Understandably, the behavior sequence is analyzed through the graph neural network (GNN) to deeply explore the complex relationships and interaction patterns between nodes (such as users, devices, applications, web pages, etc.) and obtain representations of all nodes. First, each behavior or entity in the behavior sequence is defined as a node in the graph to ensure that all important elements (such as users, devices, applications, web pages, etc.) are included in the graph and serve as the basic unit for subsequent analysis. Edges are defined based on the relationships between behaviors or entities to determine the connection method and information flow path between nodes. The definition of edges should be based on actual behavioral logic, such as users visiting web pages, users using devices, etc. These relationships reflect the interactions and dependencies between nodes. Each node is given initial attributes, including the type, characteristics, and status of the node. These attributes are the basic information of the node and provide initial input data for the graph neural network.

[0135] The graph neural network layer receives and aggregates information from all neighboring nodes of each node, allowing the node to obtain a wider range of information through its neighbors. Aggregation can be achieved through graph convolution operations, which update the representation of the current node by taking a weighted average of the information of neighboring nodes. This can be achieved through a neural network layer (such as a fully connected layer), which performs a nonlinear transformation on the aggregated information to generate a new node representation. The updated node representation not only contains the characteristics of the node itself, but also integrates the information of neighboring nodes, thereby more comprehensively reflecting the position and role of the node in the graph.

[0136] Through multiple layers of graph convolution, information from more distant neighbors is gradually integrated. Each layer of graph convolution further expands the node's receptive field, enabling it to capture a wider range of information. The resulting node embedding incorporates the node's own characteristics, information about neighboring nodes, and the structural information of the entire graph.

[0137] Further optionally, the user comprehensive behavior features are further processed by a fully connected layer, and the category of abnormal behavior patterns is output using a softmax function, including:

[0138] Performing a linear transformation on the user's comprehensive behavioral features through a fully connected layer to extract a feature combination that is sensitive to abnormal behavior patterns;

[0139] Apply the softmax function to perform exponential operation on the output of the fully connected layer and normalize it to obtain the probability distribution of each abnormal behavior pattern category;

[0140] Traverse the probability distribution and find the highest probability value and its corresponding category;

[0141] Compare the highest probability value with a preset threshold;

[0142] If the highest probability value exceeds the preset threshold and the corresponding category is marked as an abnormal behavior pattern, the user's behavior is determined to be abnormal, and the category with the highest probability is used as the predicted user abnormal behavior pattern category;

[0143] If the highest probability value does not exceed the preset threshold, or the category corresponding to the highest probability value is marked as normal behavior, the user's behavior is determined to be normal behavior.

[0144] It can be understood that the user's comprehensive behavioral features are linearly transformed through the fully connected layer. The role of the fully connected layer (also called the dense layer) is to linearly combine the input features to extract feature combinations that are sensitive to abnormal behavior patterns. Specifically, the user's comprehensive behavioral features are matrix multiplied by the weights and a bias term is added, which can be expressed as y=Wx+b, where W is the weight matrix, x is the user's comprehensive behavioral features, b is the bias term, and y is the output after linear transformation.

[0145] Apply the softmax function to the output of the fully connected layer, perform an exponential operation on it, and normalize it to convert the output of the fully connected layer into a probability distribution so that the sum of the probabilities of all categories is 1. Specifically, apply the softmax function to the output of the fully connected layer, that is, perform an exponential operation on each element, and normalize the result so that the sum of all elements is equal to 1.

[0146] The probability distribution output by the softmax function is traversed to find the element with the highest probability value and its corresponding category to determine the most likely predicted category. The highest probability value is then compared with a preset threshold. This threshold can be determined based on experience or experimentation to balance precision and recall, and is used to determine whether the highest probability value is high enough to be considered reliable.

[0147] If the highest probability value exceeds the preset threshold and the corresponding category is marked as an abnormal behavior pattern, the user's behavior is determined to be abnormal, and the category with the highest probability is used as the predicted user abnormal behavior pattern category.

[0148] If the highest probability value does not exceed the preset threshold, or the category corresponding to the highest probability value is marked as normal behavior, the user's behavior is determined to be normal behavior.

[0149] Further optionally, in step S101, the first level verification of the basic information of the certificate includes:

[0150] Extract basic information from the digital certificate provided by the user, including the issuing authority, validity period, certificate serial number, holder information, and digital signature;

[0151] The issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate are verified separately.

[0152] Further optionally, performing a second layer of verification on the sensitive information in the certificate includes:

[0153] If there is only one verification participant, a public-private key pair is generated;

[0154] Define a verification function based on verification requirements;

[0155] Write a smart contract that includes verification logic;

[0156] Extract sensitive information to be verified from digital certificates;

[0157] Use the public key to homomorphically encrypt the sensitive information to obtain the encrypted ciphertext of the sensitive information;

[0158] Use the private key and homomorphic encryption to decrypt or calculate the encrypted sensitive information ciphertext to execute the verification function and obtain the verification result;

[0159] Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the verification results.

[0160] As you can understand, when there's only one verification party, a public-private key pair is first generated using an encryption algorithm (such as RSA or ECC). The public key is made public and used for subsequent encryption operations, while the private key is kept strictly confidential and used only for decryption and specific calculations. Furthermore, depending on the specific verification requirements, a verification function is written that performs calculations based on the encrypted sensitive information to verify the certificate's validity or check other specific properties.

[0161] Next, we develop a smart contract, an automated script on the blockchain. This contract contains the verification logic and trigger conditions, automatically executing the verification process when specific conditions are met. The introduction of smart contracts ensures the automation and transparency of the verification process.

[0162] Next, use a certificate parsing tool or library to extract the sensitive information to be verified from the digital certificate. To protect the security and privacy of this information, homomorphically encrypt the sensitive information using the previously generated public key, generating the encrypted ciphertext of the sensitive information. Homomorphic encryption allows computation to be performed on encrypted data without first decrypting it.

[0163] Subsequently, using the private key and the computational properties of homomorphic encryption, the encrypted sensitive information ciphertext is decrypted or calculations are performed directly on the ciphertext. The decryption or calculation result is used as input to the verification function, which is executed to obtain a verification result. Finally, the verification result is used as input to the smart contract, triggering the verification logic within the contract. The smart contract uses the verification result and pre-set verification criteria to determine whether the sensitive information in the certificate meets expectations. Based on the smart contract's judgment, the certificate's validity is determined and recorded on the blockchain for subsequent query and use.

[0164] Further optionally, in step S102, the second-level verification of the sensitive information in the certificate further includes:

[0165] If there is more than one verification participant, generate a public-private key pair for each participant;

[0166] Each participant keeps their own private key, divides the private key into multiple shares, distributes each share to other participants or third parties, and discloses the public key to all other participants;

[0167] Define a verification function based on verification requirements;

[0168] Write a smart contract that includes verification logic and secret sharing reconstruction logic;

[0169] Extract sensitive information to be verified from digital certificates;

[0170] Use their respective public keys to homomorphically encrypt sensitive information to obtain the encrypted sensitive information ciphertext, and send the encrypted sensitive information ciphertext to all other participants;

[0171] When verification is required, for other participants, the private key is rebuilt according to the reconstruction logic of secret sharing;

[0172] Using the reconstructed private key share and homomorphic encryption operations, partially decrypt or calculate the encrypted sensitive information ciphertext to perform part of the verification function;

[0173] After multiple rounds of partial computation, in each round, different participants use their private key shares to perform partial computation and pass the results to the participants of the next round, and finally obtain the verification result;

[0174] Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the final verification results.

[0175] As you can understand, when multiple verification parties are involved, a separate public-private key pair is first generated for each party, and the private key is securely stored. To enhance security, the private key is split into multiple shares and securely distributed to other parties or trusted third parties. The public key is then made public for subsequent encryption operations. Furthermore, based on the verification requirements, a verification function is defined that performs calculations based on encrypted sensitive information to verify the validity or other specific properties of the certificate. Simultaneously, a smart contract is written on the blockchain, containing the verification logic, secret sharing reconstruction logic, and the conditions that trigger verification and reconstruction.

[0176] After extracting the sensitive information to be verified from the digital certificate, each participant uses their own public key to homomorphically encrypt the sensitive information, obtaining the encrypted sensitive information ciphertext. Each participant then sends the encrypted sensitive information ciphertext to all other participants, ensuring that each participant can perform calculations on the encrypted sensitive information.

[0177] When verification is required, participants collaborate to reconstruct a share or specific portion of the private key based on secret sharing reconstruction logic, without having to fully recover the entire private key. Through multiple rounds of partial computation, each participant uses their own reconstructed share of the private key and the operational properties of homomorphic encryption to partially decrypt or compute the encrypted sensitive information ciphertext, obtaining a partial result of the verification function. These partial results are passed to the next participant in each round, and after gradual aggregation, the final verification result is obtained.

[0178] The final verification result is entered into a smart contract, which automatically determines the certificate's validity based on pre-set verification criteria. The verification results can be published to all participants or only to authorized parties, as needed. If verification passes, the certificate is considered valid; if verification fails, it is invalid. This conclusion is recorded on the blockchain for subsequent query and use.

[0179] Example 2

[0180] See also Figure 2 The present invention proposes a multi-layer certificate verification system based on digital certificates, the system comprising:

[0181] First verification module: used to perform the first level verification on the basic information of the certificate;

[0182] Second verification module: used to perform a second-level verification on the sensitive information in the certificate using homomorphic encryption if the first-level verification passes;

[0183] Behavior Analysis Module: This module allows access if the second-tier verification passes, and uses a behavior analysis model to analyze current user behavior in real time to identify potential abnormal behaviors and their patterns. The behavior analysis model uses Transformer as its underlying architecture, combined with graph neural networks, to capture global dependencies in behavior sequences and analyze local structures and relationship patterns in user behavior.

[0184] Decision-making module: If abnormal behavior is identified, it will terminate access rights or trigger an alarm based on the abnormal behavior pattern.

[0185] Optionally, the behavior analysis module is further configured to:

[0186] Collect user behavior data on the network platform in real time;

[0187] Preprocess behavioral data;

[0188] Extract key behavioral features from pre-processed behavioral data, including the timestamp of the user's behavior, behavior type, behavior object, behavior frequency, the user's social relationships in the network, geographic location, and device usage habits;

[0189] Convert key behavioral features into behavioral sequences to reflect the user's behavioral trajectory;

[0190] Deeply analyze the behavior sequence through the Transformer layer to capture global dependencies across time steps and extract a global representation;

[0191] Analyze the local structure and association patterns in the behavior sequence through the graph neural network layer to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes;

[0192] The global representation and the node representation are organically integrated to integrate global context information and local details to extract comprehensive user behavior features;

[0193] The user's comprehensive behavior features are further processed through a fully connected layer, and the softmax function is used to output the category of abnormal behavior patterns.

[0194] Optionally, the behavior analysis module is further configured to:

[0195] Convert each behavior vector in the behavior sequence into a high-dimensional dense real number vector, setting it as an embedding vector;

[0196] In the Transformer layer, for each embedding vector in the behavior sequence, calculate the attention score between it and all other embedding vectors;

[0197] The output processed by the self-attention mechanism is passed through a feedforward neural network for further nonlinear transformation;

[0198] The encoder of the Transformer layer performs multi-level self-attention processing on the behavior sequence, extracts global context information, and outputs a global representation of the behavior sequence. The global representation contains the global dependencies in the behavior sequence and the behavior characteristics of the user at different time steps.

[0199] Optionally, the behavior analysis module is further configured to:

[0200] Each behavior or entity in the behavior sequence is defined as a node in a graph, where the nodes include users, devices, applications, and web pages;

[0201] Define edges based on relationships between behaviors or entities;

[0202] Assign initial attributes to each node, including the node type, characteristics and status;

[0203] For each node in the graph, the graph neural network layer receives and aggregates information from all its neighboring nodes;

[0204] Update the representation of the current node based on the aggregated neighbor information;

[0205] Through multi-layer graph convolution operations, information from distant neighbors is gradually integrated to obtain the final embedded representation of the node, which includes the characteristics of the node itself, information of neighboring nodes, and structural information of the entire graph.

[0206] Optionally, the behavior analysis module is further configured to:

[0207] Performing a linear transformation on the user's comprehensive behavioral features through a fully connected layer to extract a feature combination that is sensitive to abnormal behavior patterns;

[0208] Apply the softmax function to perform exponential operation on the output of the fully connected layer and normalize it to obtain the probability distribution of each abnormal behavior pattern category;

[0209] Traverse the probability distribution and find the highest probability value and its corresponding category;

[0210] Compare the highest probability value with a preset threshold;

[0211] If the highest probability value exceeds the preset threshold and the corresponding category is marked as an abnormal behavior pattern, the user's behavior is determined to be abnormal, and the category with the highest probability is used as the predicted user abnormal behavior pattern category;

[0212] If the highest probability value does not exceed the preset threshold, or the category corresponding to the highest probability value is marked as normal behavior, the user's behavior is determined to be normal behavior.

[0213] Further optionally, the second verification module is further configured to:

[0214] If there is only one verification participant, a public-private key pair is generated;

[0215] Define a verification function based on verification requirements;

[0216] Write a smart contract that includes verification logic;

[0217] Extract sensitive information to be verified from digital certificates;

[0218] Use the public key to homomorphically encrypt the sensitive information to obtain the encrypted ciphertext of the sensitive information;

[0219] Use the private key and homomorphic encryption to decrypt or calculate the encrypted sensitive information ciphertext to execute the verification function and obtain the verification result;

[0220] Through smart contracts, we determine whether the sensitive information in the certificate meets the expected verification standards based on the verification results;

[0221] If the verification passes, the certificate is considered valid; otherwise, the certificate is invalid.

[0222] Further optionally, the second verification module is further configured to:

[0223] If there is more than one verification participant, generate a public-private key pair for each participant;

[0224] Each participant keeps their own private key, divides the private key into multiple shares, distributes each share to other participants or third parties, and discloses the public key to all other participants;

[0225] Define a verification function based on verification requirements;

[0226] Write a smart contract that includes verification logic and secret sharing reconstruction logic;

[0227] Extract sensitive information to be verified from digital certificates;

[0228] Use their respective public keys to homomorphically encrypt sensitive information to obtain the encrypted sensitive information ciphertext, and send the encrypted sensitive information ciphertext to all other participants;

[0229] When verification is required, for other participants, the private key is rebuilt according to the reconstruction logic of secret sharing;

[0230] Using the reconstructed private key share and homomorphic encryption operations, partially decrypt or calculate the encrypted sensitive information ciphertext to perform part of the verification function;

[0231] After multiple rounds of partial computation, in each round, different participants use their private key shares to perform partial computation and pass the results to the participants of the next round, and finally obtain the verification result;

[0232] Through smart contracts, the final verification results are used to determine whether the sensitive information in the certificate meets the expected verification standards;

[0233] If the verification passes, the certificate is considered valid; otherwise, the certificate is invalid.

[0234] Further optionally, the first verification module is further configured to:

[0235] Extract basic information from the digital certificate provided by the user, including the issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate;

[0236] The issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate are verified separately.

[0237] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A multi-layer certificate verification method based on digital certificates, characterized in that: The method comprises: Perform the first level of verification on the basic information of the certificate; If the first level of verification passes, the second level of verification is performed on the sensitive information in the certificate; If the second-tier verification passes, access is allowed, and a behavioral analysis model is used to analyze the current user behavior in real time to identify potential abnormal behaviors and their patterns. The behavioral analysis model uses Transformer as its infrastructure, combined with a graph neural network, to capture global dependencies in behavior sequences and analyze local structures and relationship patterns in user behavior. If abnormal behavior is identified, access rights will be terminated or an alarm will be triggered based on the abnormal behavior pattern; The second level verification of the sensitive information in the certificate includes: If there is more than one verification participant, generate a public-private key pair for each participant; Each participant keeps their own private key, divides the private key into multiple shares, distributes each share to other participants or third parties, and discloses the public key to all other participants; Define a verification function based on verification requirements; Write a smart contract that includes verification logic and secret sharing reconstruction logic; Extract sensitive information to be verified from digital certificates; Use their respective public keys to homomorphically encrypt sensitive information to obtain the encrypted sensitive information ciphertext, and send the encrypted sensitive information ciphertext to all other participants; When verification is required, for other participants, the private key is rebuilt according to the reconstruction logic of secret sharing; Using the reconstructed private key share and homomorphic encryption operations, partially decrypt or calculate the encrypted sensitive information ciphertext to perform part of the verification function; After multiple rounds of partial computation, in each round, different participants use their private key shares to perform partial computation and pass the results to the participants of the next round, and finally obtain the verification result; Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the final verification results.

2. The multi-layer certificate verification method based on digital certificates according to claim 1, characterized in that: The behavior analysis model is used to analyze current user behavior in real time to identify potential abnormal behaviors and their patterns, including: Collect user behavior data on the network platform in real time; Preprocess the behavioral data; extract key behavioral features from the preprocessed behavioral data, including the timestamp of the user's behavior, the type of behavior, the target of the behavior, the frequency of the behavior, the user's social relationships in the network, the geographic location, and the device usage habits; Convert key behavioral features into behavioral sequences to reflect the user's behavioral trajectory; Deeply analyze the behavior sequence through the Transformer layer to capture global dependencies across time steps and extract a global representation; Analyze the local structure and association patterns in the behavior sequence through the graph neural network layer to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes; The global representation and the node representation are organically integrated to integrate global context information and local details to extract comprehensive user behavior features; The user's comprehensive behavior features are further processed through a fully connected layer, and the softmax function is used to output the category of abnormal behavior patterns.

3. The multi-layer certificate verification method based on digital certificates according to claim 2, characterized in that: The Transformer layer performs in-depth analysis of the behavior sequence, captures global dependencies across time steps, and extracts a global representation, including: Convert each behavior vector in the behavior sequence into a high-dimensional dense real number vector, setting it as an embedding vector; In the Transformer layer, for each embedding vector in the behavior sequence, calculate the attention score between it and all other embedding vectors; The output processed by the self-attention mechanism is passed through a feedforward neural network for further nonlinear transformation; The encoder of the Transformer layer performs multi-level self-attention processing on the behavior sequence, extracts global context information, and outputs a global representation of the behavior sequence. The global representation contains the global dependencies in the behavior sequence and the behavior characteristics of the user at different time steps.

4. The multi-layer certificate verification method based on digital certificates according to claim 2, characterized in that: The graph neural network layer analyzes the local structure and association patterns in the behavior sequence to deeply explore the complex relationships and interaction patterns between nodes and obtain node representations of all nodes, including: Each behavior or entity in the behavior sequence is defined as a node in a graph, where the nodes include users, devices, applications, and web pages; Define edges based on relationships between behaviors or entities; Assign initial attributes to each node, including the node type, characteristics and status; For each node in the graph, the graph neural network layer receives and aggregates information from all its neighboring nodes; Update the representation of the current node based on the aggregated neighbor information; Through multi-layer graph convolution operations, information from distant neighbors is gradually integrated to obtain the final embedded representation of the node, which includes the characteristics of the node itself, information of neighboring nodes, and structural information of the entire graph.

5. The multi-layer certificate verification method based on digital certificates according to claim 2, characterized in that: The user's comprehensive behavior features are further processed by the fully connected layer, and the category of abnormal behavior patterns is output using the softmax function, including: Performing a linear transformation on the user's comprehensive behavioral features through a fully connected layer to extract a feature combination that is sensitive to abnormal behavior patterns; Apply the softmax function to perform exponential operation on the output of the fully connected layer and normalize it to obtain the probability distribution of each abnormal behavior pattern category; Traverse the probability distribution and find the highest probability value and its corresponding category; Compare the highest probability value with a preset threshold; If the highest probability value exceeds the preset threshold and the corresponding category is marked as an abnormal behavior pattern, the user's behavior is determined to be abnormal, and the category with the highest probability is used as the predicted user abnormal behavior pattern category; If the highest probability value does not exceed the preset threshold, or the category corresponding to the highest probability value is marked as normal behavior, the user's behavior is determined to be normal behavior.

6. The multi-layer certificate verification method based on digital certificates according to claim 1, characterized in that: The second level verification of the sensitive information in the certificate further includes: If there is only one verification participant, a public-private key pair is generated; Define a verification function based on verification requirements; Write a smart contract that includes verification logic; Extract sensitive information to be verified from digital certificates; Use the public key to homomorphically encrypt the sensitive information to obtain the encrypted ciphertext of the sensitive information; Use the private key and homomorphic encryption to decrypt or calculate the encrypted sensitive information ciphertext to execute the verification function and obtain the verification result; Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the verification results.

7. The multi-layer certificate verification method based on digital certificates according to claim 1, characterized in that: The first level of verification of the basic information of the certificate includes: Extract basic information from the digital certificate provided by the user, including the issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate; The issuing authority, validity period, certificate serial number, holder information and digital signature of the digital certificate are verified separately.

8. A multi-layer certificate verification system based on digital certificates, characterized in that: The system comprises: First verification module: used to perform the first level verification on the basic information of the certificate; Second verification module: used to perform a second-level verification on the sensitive information in the certificate using homomorphic encryption if the first-level verification passes; Behavior Analysis Module: This module allows access if the second-tier verification passes, and uses a behavior analysis model to analyze current user behavior in real time to identify potential abnormal behaviors and their patterns. The behavior analysis model uses Transformer as its underlying architecture, combined with graph neural networks, to capture global dependencies in behavior sequences and analyze local structures and relationship patterns in user behavior. Decision module: used to terminate access rights or trigger an alarm based on abnormal behavior patterns if abnormal behavior is identified; Wherein, the second verification module is further used for: If there is more than one verification participant, generate a public-private key pair for each participant; Each participant keeps their own private key, divides the private key into multiple shares, distributes each share to other participants or third parties, and discloses the public key to all other participants; Define a verification function based on verification requirements; Write a smart contract that includes verification logic and secret sharing reconstruction logic; Extract sensitive information to be verified from digital certificates; Use their respective public keys to homomorphically encrypt sensitive information to obtain the encrypted sensitive information ciphertext, and send the encrypted sensitive information ciphertext to all other participants; When verification is required, for other participants, the private key is rebuilt according to the reconstruction logic of secret sharing; Using the reconstructed private key share and homomorphic encryption operations, partially decrypt or calculate the encrypted sensitive information ciphertext to perform part of the verification function; After multiple rounds of partial computation, in each round, different participants use their private key shares to perform partial computation and pass the results to the participants of the next round, and finally obtain the verification result; Smart contracts are used to determine whether the sensitive information in the certificate meets the expected verification standards based on the final verification results.

Images