Big data information security verification and reporting method based on blockchain

Abstract

The present invention relates to the field of information encryption, and in particular to a big data information security verification and reporting method based on blockchain. The method generates a node certificate corresponding to data to be verified on a user end, uploads the data to be verified and the corresponding node certificate to a blockchain network, and randomly distributes them to verification nodes. The verification node uses the node certificate to perform node verification on the authenticity of the visiting user, collects the verification time when the visiting user completes the node verification, pre-processes the verification time, and generates corresponding verification pre-data. The verification pre-data is learned using a verification time evaluation model to obtain a corresponding verification time change graph and trigger a data blocking instruction when the time value exceeds a time threshold. The specific content of the data to be verified will not be leaked when the node certificate is generated. The authenticity of the visiting user can be verified, and the information of the data itself cannot be obtained, thereby effectively protecting data privacy and ensuring the security of the data during transmission.

Description

Technical Field

[0001] The present invention relates to the field of information encryption, and in particular to a method for verifying and reporting big data information security based on blockchain. Background Art

[0002] Blockchain is a distributed ledger technology characterized by immutability, decentralization, and transparency. In a blockchain, data is recorded in blocks, with cryptography ensuring security and integrity. While blockchain technology provides a foundation for secure data storage and verification, it faces challenges with scalability and privacy protection.

[0003] With the development of big data technologies, data storage, transmission, and sharing have become increasingly complex, making data security and privacy protection critical issues. Traditional data verification methods often involve disclosing data content and fail to meet privacy protection requirements. Therefore, a technical solution is needed that can both verify data authenticity and protect data privacy.

[0004] Chinese Patent Authorization Announcement No. CN116828087B discloses an information security system based on blockchain connection, which obtains network messages from a network message stream; extracts the text description of each attribute item in the network message and the attribute information under each attribute item; extracts the network message semantic understanding feature vector from the text description of each attribute item and the attribute information under each attribute item; and, based on the network message semantic understanding feature vector, determines whether to upload the network message to the blockchain network. In this way, it is possible to achieve balanced processing of network messages based on deep learning and blockchain technology, optimize the distribution method of network messages, make the traffic distribution of each output port more uniform, and thus improve the overall performance and stability of the network information security system. At the same time, uploading network messages to the blockchain can improve the reliability of the information security system and help prevent and respond to network threats.

[0005] Chinese Patent Authorization Announcement No.: CN111669377B discloses a method for secure management of information on a blockchain, comprising the following steps: adding an authentication unit for identifying the user identity and an encoding unit for recompiling the parent data chain information to both ends of the parent data chain that sends the blockchain; the authentication unit identifies the verification code information input by the user; the encoding unit encrypts all data mirrors in the parent data chain according to the incorrect verification code identified by the authentication unit to obtain a virtual data chain, separates the virtual data chain from the parent data chain and sends it to the incorrect user identity; the parent data chain continues to be transmitted to the destination blockchain, and the encoding unit separates the encoding unit from the parent data chain and saves it separately according to the secondary identification result of the authentication unit; this scheme realizes the security protection of information data, improves the fault tolerance when the information data is stolen, and ensures high encryption of the account identity.

[0006] However, the above method has the following problems: the information management process requires the disclosure of data content and cannot meet the requirements of privacy protection. Summary of the Invention

[0007] To this end, the present invention provides a big data information security verification and reporting method based on blockchain to overcome the problem that the information management process in the existing technology requires the disclosure of data content and cannot meet the requirements of privacy protection.

[0008] To achieve the above objectives, the present invention provides a blockchain-based big data information security verification and reporting method, which is characterized by comprising:

[0009] The user end encrypts the data to be verified and generates a node certificate corresponding to the data to be verified on the user end;

[0010] The user terminal uploads the encrypted data to be verified and the corresponding node certificate to the blockchain network, and randomly distributes the data to be verified and the corresponding node certificate to each verification node in the blockchain network;

[0011] When a visiting user accesses the data to be verified, the verification node performs node verification on the authenticity of the visiting user using the node certificate, and collects the verification time when the visiting user completes the node verification;

[0012] Selecting several change features of the verification time, preprocessing the verification time, and generating corresponding verification pre-data, and using the verification time evaluation model to learn the verification pre-data to obtain a corresponding verification time change map;

[0013] Extracting a time value from the verification time variation graph, comparing the time value with a time threshold, and triggering a data blocking instruction when the time value exceeds the time threshold, wherein:

[0014] The node certificate is used to prove the authenticity of the visiting user without revealing the specific content of the data to be verified;

[0015] The change characteristics include the change rate and periodic characteristics of the verification time;

[0016] The time threshold is the minimum standard of verification time for the visiting user to complete the node verification.

[0017] Furthermore, the step of generating a node certificate corresponding to the data to be verified includes:

[0018] The user terminal encrypts the data to be verified and selects a node certification algorithm;

[0019] The user terminal constructs a corresponding polynomial representation according to the data to be verified;

[0020] The user terminal calculates the relevant node proof based on the polynomial representation.

[0021] Furthermore, the step of randomly assigning the data to be verified and the corresponding nodes to each verification node in the blockchain network includes:

[0022] Uploading the encrypted data to be verified and the corresponding node certificate to the blockchain network;

[0023] Randomly assigning verification nodes corresponding to the data to be verified using a hash function calculation;

[0024] The node certificate is matched with the data to be verified, and the node certificate is uploaded to the corresponding verification node.

[0025] Furthermore, the verification node uses the node certificate to verify the authenticity of the visiting user, including the following steps:

[0026] When the visiting user accesses the data to be verified, the verification node generates an encrypted proof value based on the node proof;

[0027] The verification node sends a random challenge value to the visiting user;

[0028] The visiting user combines the random challenge value with his / her own secret key to calculate and return an encrypted response value;

[0029] The encrypted response value is compared with the encrypted proof value to determine whether the node verification is successful.

[0030] Furthermore, when the encrypted response value is consistent with the encrypted proof value, the node verification is judged to be successful; when the encrypted response value is inconsistent with the encrypted proof value, the node verification is judged to be failed.

[0031] Furthermore, a timer is provided in the verification node. When the verification node detects that the visiting user accesses the data to be verified, the timer starts timing. When the node verification is successful, the timer stops timing and counts the corresponding verification time. When the node verification fails, the timer invalidates the verification time.

[0032] Furthermore, the step of generating pre-verification data includes:

[0033] Selecting several variation characteristics of the verification time;

[0034] The verification time is divided according to the standard sampling rate to form corresponding verification pre-data, wherein,

[0035] The standard sampling rate is a learning rate that can be recognized by the verification time evaluation model, and for single learning, the corresponding standard sampling rate is a single learning rate.

[0036] Furthermore, the verification pre-data and the change characteristics are passed into the verification time evaluation model, the verification time evaluation model learns the verification pre-data, generates a corresponding verification time change map, and annotates and verifies the verification time change map according to the change characteristics to obtain the corresponding time value.

[0037] Furthermore, the time value is compared with a time threshold, and when the time value is less than the time threshold, the data to be verified is marked as safe data and reported.

[0038] Furthermore, when the time value is greater than the time threshold, the user terminal triggers the data blocking instruction, marks the data to be verified as leaked data and blocks it.

[0039] Compared with the existing technology, the present invention generates a node certificate corresponding to the data to be verified on the user side, uploads the data to be verified and the corresponding node certificate to the blockchain network, and randomly distributes them to each verification node. The verification node uses the node certificate to perform node verification on the authenticity of the visiting user, collects the verification time of the visiting user to complete the node verification, pre-processes the verification time, and generates corresponding verification pre-data. The verification pre-data is learned using a verification time evaluation model to obtain a corresponding verification time change map and trigger a data blocking instruction when the time value exceeds the time threshold. The specific content of the data to be verified will not be leaked when the node certificate is generated. It can verify the authenticity of the visiting user and cannot obtain information about the data itself, thereby effectively protecting data privacy and ensuring the security of data during transmission.

[0040] Furthermore, by using polynomial representation to calculate node proof, the authenticity of the data can be proved without leaking the data content, that is, the verifier can be sure of the authenticity of the data without knowing the specific content of the data. The generation of node proof is based on polynomial representation, and the calculation process is efficient and easy to verify, which reduces the verification time and the consumption of computing resources. The generation process of node proof can be adjusted according to the amount and complexity of the data, and is suitable for the processing and verification of large-scale data.

[0041] Furthermore, by associating node proofs with the data to be verified, verification nodes are able to accurately identify and verify the authenticity of the data during the verification process. Node proofs are uploaded to the corresponding verification nodes, making the verification process more efficient. Verification nodes can directly use node proofs for verification without recalculating or obtaining additional information. Node proofs can prove the authenticity of data without revealing its content, further protecting data privacy.

[0042] Furthermore, the unpredictability of the verification process is increased by sending a random challenge value, making each verification process unique and preventing replay attacks. The visiting user combines the random challenge value with his or her own secret key to calculate and return an encrypted response value. This process ensures that only users with the correct secret key can generate a valid response value. By generating an encrypted response value, the user's identity information is ensured not to be leaked during the verification process. By comparing the encrypted response value with the encrypted proof value, the verification node can quickly determine whether the identity of the visiting user is authentic, thereby improving verification efficiency. The entire verification process enables the verification node to verify the user's identity without obtaining the user's secret key.

[0043] Furthermore, when the encrypted response value matches the encrypted proof value, the verification node determines that the node verification is successful, stops the timer, and calculates the corresponding verification time. This step ensures that each successful verification process has a clear time record, facilitating subsequent analysis and evaluation. When the encrypted response value does not match the encrypted proof value, the verification node determines that the node verification has failed, and the timer invalidates the verification time. This step ensures that only successful verification processes are recorded, preventing the time data of failed verifications from interfering with subsequent analysis.

[0044] Furthermore, by sampling and extracting features of the verification time to generate pre-verification data, the complexity of the data can be reduced and the training and learning efficiency of the verification time evaluation model can be improved. The selection of a standard sampling rate ensures that the verification time evaluation model can effectively identify and learn the changing characteristics of the verification time, thereby improving the performance of the model. The generation of pre-verification data ensures the integrity and consistency of the verification time data, providing a reliable data foundation for subsequent security analysis.

[0045] Furthermore, by comparing the time value with the time threshold, abnormal behavior in the verification process can be discovered in a timely manner to prevent data leakage or tampering. When an anomaly is detected, a data blocking instruction is immediately triggered to ensure that potential leaked data will not be further disseminated or used. By analyzing the changing characteristics of the verification time, the verification process can be optimized, unnecessary verification steps can be reduced, and overall efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1This is a flowchart of the blockchain-based big data information security verification and reporting method of the present invention;

[0047] Figure 2 A flowchart for generating a node certificate corresponding to data to be verified according to an embodiment of the present invention;

[0048] Figure 3 A flowchart of an embodiment of the present invention for randomly assigning data to be verified and corresponding nodes to verification nodes in a blockchain network;

[0049] Figure 4 This is a flow chart of a verification node using node certification to verify the authenticity of a visiting user according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] See also Figure 1 As shown, it is a flow chart of the big data information security verification and reporting method based on blockchain of the present invention, including:

[0055] Step S1: The user terminal encrypts the data to be verified and generates a node certificate corresponding to the data to be verified on the user terminal;

[0056] Step S2: The user uploads the encrypted data to be verified and the corresponding node certificate to the blockchain network, and randomly distributes the data to be verified and the corresponding node certificate to each verification node in the blockchain network;

[0057] Step S3: When a visiting user accesses the data to be verified, the verification node uses the node certificate to perform node verification on the authenticity of the visiting user and collects the verification time when the visiting user completes the node verification;

[0058] Step S4: Select several change features of the verification time, pre-process the verification time, and generate corresponding verification pre-data. Use the verification time evaluation model to learn the verification pre-data to obtain the corresponding verification time change map;

[0059] Step S5: extract the time value in the verification time change graph, compare the time value with the time threshold, and trigger the data blocking instruction when the time value exceeds the time threshold, wherein:

[0060] Node proof is used to prove the authenticity of the visiting user without revealing the specific content of the data to be verified;

[0061] The change characteristics include the change rate and periodic characteristics of the verification time;

[0062] The time threshold is the minimum verification time required for a visiting user to complete node verification.

[0063] By generating a node certificate corresponding to the data to be verified on the user side, uploading the data to be verified and the corresponding node certificate to the blockchain network, and randomly assigning them to each verification node, the verification node uses the node certificate to verify the authenticity of the visiting user, and collects the verification time of the visiting user to complete the node verification, pre-processes the verification time, and generates corresponding verification pre-data. The verification pre-data is learned using the verification time evaluation model to obtain the corresponding verification time change map and trigger the data blocking instruction when the time value exceeds the time threshold. The specific content of the data to be verified will not be leaked when generating the node certificate. It can verify the authenticity of the visiting user and cannot obtain the information of the data itself, thereby effectively protecting data privacy and ensuring the security of data during transmission.

[0064] See also Figure 2 As shown in FIG, it is a flowchart of generating a node certificate corresponding to the data to be verified according to an embodiment of the present invention, including:

[0065] Step S11: The user terminal encrypts the data to be verified and selects a node certification algorithm;

[0066] Step S12: The user terminal constructs a corresponding polynomial representation based on the data to be verified;

[0067] In step S13, the user terminal calculates the relevant node proof based on the polynomial representation.

[0068] In practice, encryption prevents data from being leaked during transmission and storage. Even if the data is intercepted or accessed, unauthorized users cannot obtain the actual content. The choice of encryption algorithm can be adjusted based on the sensitivity of the data and the application scenario, further enhancing data security.

[0069] Users can choose different node proof algorithms based on their specific needs, such as proof algorithms (such as ZK-SNARKs, ZK-STARKs, etc.) to adapt to different application scenarios and security requirements. Different node proof algorithms may have advantages in performance, security, and privacy protection. Users can choose the most suitable algorithm based on their actual needs.

[0070] Polynomial representation can simplify complex data structures into polynomial form, thereby reducing data storage and transmission costs.

[0071] By using polynomial representation to calculate node proof, the authenticity of the data can be proved without revealing the data content. That is, the verifier can be sure of the authenticity of the data without knowing the specific content of the data. The generation of node proof is based on polynomial representation. The calculation process is efficient and easy to verify, which reduces the verification time and the consumption of computing resources. The generation process of node proof can be adjusted according to the amount and complexity of the data, and is suitable for the processing and verification of large-scale data.

[0072] See also Figure 3 As shown, it is a flowchart of an embodiment of the present invention for randomly assigning the data to be verified and the corresponding nodes to each verification node in the blockchain network, including:

[0073] Step St1: Upload the encrypted data to be verified and the corresponding node certificate to the blockchain network;

[0074] Step St2: Use a hash function to randomly assign verification nodes corresponding to the data to be verified;

[0075] Step St3: Match the node certificate with the data to be verified, and upload the node certificate to the corresponding verification node.

[0076] In specific implementations, data uploaded to the blockchain network is visible to all nodes, but the data content cannot be read by unauthorized users due to encryption, ensuring the transparency and privacy of the data.

[0077] The randomness of hash function output allows data to be randomly assigned to different verification nodes, increasing the unpredictability of data distribution and improving system security. Hash functions can evenly distribute data across verification nodes, preventing data from being concentrated on a small number of nodes, thereby improving system load balancing and efficiency. The irreversibility of hash functions ensures a unidirectional distribution process. Once data is assigned to a node, the hash value cannot be used to deduce the original data, further enhancing data security.

[0078] The entire process ensures the security and privacy of data during upload, distribution, and verification. Data is encrypted and uploaded to the blockchain network. It is then randomly assigned to verification nodes through a hash function. Node proofs verify data without revealing its content.

[0079] Through the random distribution of hash functions and efficient verification of node proofs, verification time and computing resource consumption are reduced, and the overall efficiency of the system is improved.

[0080] The hash function is used to evenly distribute data to each verification node, avoiding data concentration on a few nodes and improving the load balancing and reliability of the system.

[0081] The blockchain's immutable nature and data transparency ensure that the actions of all verification nodes are recorded on the chain, enhancing the credibility and transparency of the system.

[0082] This method is suitable for processing and verifying large-scale data, can effectively meet the information security needs in a big data environment, and can be adjusted and expanded according to actual needs.

[0083] By matching node proofs with the data to be verified, the verification node can accurately identify and verify the authenticity of the data during the verification process. Node proofs are uploaded to the corresponding verification node, making the verification process more efficient. Verification nodes can directly use node proofs for verification without recalculating or obtaining additional information. Node proofs can prove the authenticity of data without revealing its content, further protecting data privacy.

[0084] See also Figure 4 As shown in FIG, which is a flowchart of a verification node using a node certificate to verify the authenticity of a visiting user according to an embodiment of the present invention, including:

[0085] Step Sp1: When a visiting user accesses the data to be verified, the verification node generates an encrypted proof value based on the node proof;

[0086] Step Sp2: The verification node sends a random challenge value to the visiting user;

[0087] Step Sp3: The visiting user combines the random challenge value with his / her own secret key, calculates and returns an encrypted response value;

[0088] In step Sp4, the encrypted response value is compared with the encrypted proof value to determine whether the node verification is successful.

[0089] In practice, the verification node generates a cryptographic proof value based on the node's proof, which forms the basis of the verification process. This cryptographic proof value is key data used by the verification node to verify the identity of the visiting user. The generation of this cryptographic proof value ensures the security of the verification process. Even if the verification node is attacked, the attacker cannot directly obtain the visiting user's real information.

[0090] The use of random challenge values ​​ensures the security of the verification process. Even if an attacker intercepts the challenge value, he cannot predict the next challenge value, and thus cannot forge the verification process, ensuring the interactivity and security of the verification process.

[0091] The comparison process ensures the security of the verification process. Verification is considered successful only when the encrypted response value matches the encrypted proof value, preventing forgery and attacks.

[0092] The sending of random challenge values ​​increases the unpredictability of the verification process, making each verification process unique and preventing replay attacks. The visiting user combines the random challenge value with his or her own secret key to calculate and return an encrypted response value. This process ensures that only users with the correct secret key can generate a valid response value. By generating an encrypted response value, the user's identity information is ensured not to be leaked during the verification process. By comparing the encrypted response value with the encrypted proof value, the verification node can quickly determine whether the visiting user's identity is authentic, thereby improving verification efficiency. The entire verification process enables the verification node to verify the user's identity without obtaining the user's secret key.

[0093] Specifically, when the encrypted response value is consistent with the encrypted proof value, the node verification is judged to be successful; when the encrypted response value is inconsistent with the encrypted proof value, the node verification is judged to be failed.

[0094] Specifically, a timer is provided in the verification node. When the verification node detects that a visiting user accesses the data to be verified, the timer starts timing. When the node verification succeeds, the timer stops timing and counts the corresponding verification time. When the node verification fails, the timer will invalidate the verification time.

[0095] In practice, recording verification time can be used to evaluate the efficiency of the verification process. For example, this can be used to analyze performance differences between different verification nodes or optimize verification algorithms to reduce verification time. Verification time records can also be used for security monitoring. If a verification node's verification time is unusually short or long, it may indicate a potential security issue, such as an attack or hardware failure.

[0096] Discarding the time data for failed verifications improves the accuracy and reliability of statistical analysis. For example, when calculating average verification time or evaluating verification efficiency, excluding this data can avoid biased results. Discarding this data also prevents attackers from gaining information about system performance through multiple attempts. If this data is retained, attackers could potentially exploit it to analyze system weaknesses. Recording verification times can help quickly locate system faults. If a verification node's verification time suddenly increases, it could indicate a hardware failure or network issue. Discarding this data ensures the accuracy and reliability of statistical analysis and provides data support for the stable operation of the system.

[0097] The entire verification process is carried out without disclosing the user's secret key and data content, ensuring the user's privacy.

[0098] When the encrypted response value matches the encrypted proof value, the verification node determines that the node verification is successful, stops the timer, and calculates the corresponding verification time. This step ensures that each successful verification process has a clear time record, which facilitates subsequent analysis and evaluation. When the encrypted response value does not match the encrypted proof value, the verification node determines that the node verification has failed, and the timer invalidates the verification time. This step ensures that only successful verification processes are recorded, preventing the time data of failed verifications from interfering with subsequent analysis.

[0099] Specifically, the steps for generating pre-verification data include:

[0100] Select several changing characteristics of verification time;

[0101] The verification time is divided according to the standard sampling rate to form the corresponding verification pre-data, where:

[0102] The standard sampling rate is the learning rate that the validation time evaluation model can recognize, and for single-shot learning, the corresponding standard sampling rate is a single learning rate.

[0103] In specific implementations, the rate of change of verification time, i.e., the difference between adjacent verification times, is calculated. This can help identify fluctuations in verification time, such as abnormal acceleration or deceleration. Periodic changes in verification time are analyzed, such as whether there are regular peaks or valleys. This can help identify periodic behavior in the verification process, such as longer or shorter verification times during certain time periods.

[0104] The selection of the standard sampling rate needs to be determined based on the requirements of the validation time evaluation model. A sampling rate that is too high may result in overly dense data and increase computational costs; a sampling rate that is too low may cause the data to lose important information.

[0105] Split the validation time into multiple sampling points at a standard sampling rate. Each sampling point contains a segment of the validation time, and these segments together constitute the validation pre-data. Format the segmented validation time segments into a format acceptable to the validation time evaluation model, for example, by converting the time data into a numeric vector or matrix.

[0106] By sampling and extracting features from the verification time and generating pre-verification data, the complexity of the data can be reduced and the training and learning efficiency of the verification time evaluation model can be improved. The selection of a standard sampling rate ensures that the verification time evaluation model can effectively identify and learn the changing characteristics of the verification time, thereby improving the performance of the model. The generation of pre-verification data ensures the integrity and consistency of the verification time data, providing a reliable data foundation for subsequent security analysis.

[0107] Specifically, the verification pre-data and change characteristics are passed into the verification time evaluation model. The verification time evaluation model learns the verification pre-data and generates a corresponding verification time change map. The verification time change map is labeled and verified according to the change characteristics to obtain the corresponding time value.

[0108] Specifically, the time value is compared with the time threshold. When the time value is less than the time threshold, the data to be verified is marked as safe data and reported.

[0109] Specifically, when the time value is greater than the time threshold, the user terminal triggers a data blocking instruction, marks the data to be verified as leaked data and blocks it.

[0110] In practice, pre-verification data, which has undergone sampling and feature extraction, is fed into the verification time evaluation model. This data contains characteristics of verification time variations, such as rate of change and periodicity. These characteristics are then fed into the model to help it better understand and learn the patterns of verification time.

[0111] The verification time assessment model learns from pre-verification data to generate a corresponding verification time variation map. This map reflects the changing trends and patterns of verification time. The verification time variation map is annotated based on the changing characteristics to ensure its accuracy and reliability. Through the verification process, anomalies or errors in the map can be discovered and corrected.

[0112] The time threshold is a pre-set standard used to determine whether the verification time is within the normal range. The time threshold can be adjusted according to the actual application scenario and security requirements.

[0113] Time value is less than the time threshold: When the time value is less than the time threshold, it means that the verification process is within the normal range and the data is considered safe.

[0114] The time value is greater than the time threshold: When the time value is greater than the time threshold, it indicates that the verification process is abnormal and there may be a security risk.

[0115] When the time value is less than the time threshold, the data to be verified is marked as safe data and reported. This step ensures that only verified and safe data is further processed and used. Safe data can be reported to the system normally for use by other users or systems.

[0116] When the time value exceeds the time threshold, the user triggers a data blocking instruction, marking the data to be verified as leaked and blocking it. This step prevents the potential leaked data from being further disseminated or used. Blocking measures can include restricting data access, notifying system administrators, and logging abnormal events to ensure data security.

[0117] By comparing the time value with the time threshold, abnormal behavior in the verification process can be discovered in a timely manner to prevent data leakage or tampering. When an anomaly is detected, the data blocking instruction is immediately triggered to ensure that the potential leaked data will not be further disseminated or used. By analyzing the changing characteristics of the verification time, the verification process can be optimized, unnecessary verification steps can be reduced, and overall efficiency can be improved.

[0118] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0119] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A blockchain-based big data information security verification and reporting method, characterized in that: include: The user end encrypts the data to be verified and generates a node certificate corresponding to the data to be verified on the user end; The user terminal uploads the encrypted data to be verified and the corresponding node certificate to the blockchain network, and randomly distributes the data to be verified and the corresponding node certificate to each verification node in the blockchain network; When a visiting user accesses the data to be verified, the verification node performs node verification on the authenticity of the visiting user using the node certificate, and collects the verification time when the visiting user completes the node verification; Selecting several change features of the verification time, preprocessing the verification time, and generating corresponding verification pre-data, and using the verification time evaluation model to learn the verification pre-data to obtain a corresponding verification time change map; Extracting a time value from the verification time variation graph, comparing the time value with a time threshold, and triggering a data blocking instruction when the time value exceeds the time threshold, wherein: The node certificate is used to prove the authenticity of the visiting user without revealing the specific content of the data to be verified; The change characteristics include the change rate and periodic characteristics of the verification time; The time threshold is the minimum standard of verification time for the visiting user to complete the node verification.

2. The blockchain-based big data information security verification and reporting method according to claim 1 is characterized in that: The steps to generate the node proof corresponding to the data to be verified include: The user terminal encrypts the data to be verified and selects a node certification algorithm; The user terminal constructs a corresponding polynomial representation according to the data to be verified; The user terminal calculates the relevant node proof based on the polynomial representation.

3. The blockchain-based big data information security verification and reporting method according to claim 2 is characterized in that: The steps of randomly assigning the data to be verified and the corresponding nodes to the verification nodes in the blockchain network include: Uploading the encrypted data to be verified and the corresponding node certificate to the blockchain network; Randomly assigning verification nodes corresponding to the data to be verified using a hash function calculation; The node certificate is matched with the data to be verified, and the node certificate is uploaded to the corresponding verification node.

4. The blockchain-based big data information security verification and reporting method according to claim 3 is characterized in that: The steps for the verification node to verify the authenticity of the visiting user using the node proof include: When the visiting user accesses the data to be verified, the verification node generates an encrypted proof value based on the node proof; The verification node sends a random challenge value to the visiting user; The visiting user combines the random challenge value with his / her own secret key to calculate and return an encrypted response value; The encrypted response value is compared with the encrypted proof value to determine whether the node verification is successful.

5. The blockchain-based big data information security verification and reporting method according to claim 4 is characterized in that: When the encrypted response value is consistent with the encrypted proof value, the node verification is judged to be successful; when the encrypted response value is inconsistent with the encrypted proof value, the node verification is judged to be failed.

6. The blockchain-based big data information security verification and reporting method according to claim 5 is characterized in that: A timer is provided in the verification node. When the verification node detects that the visiting user accesses the data to be verified, the timer starts timing. When the node verification is successful, the timer stops timing and counts the corresponding verification time. When the node verification fails, the timer invalidates the verification time.

7. The blockchain-based big data information security verification and reporting method according to claim 6 is characterized in that: The steps to generate pre-validation data include: Selecting several variation characteristics of the verification time; The verification time is divided according to the standard sampling rate to form corresponding verification pre-data, wherein, The standard sampling rate is a learning rate that can be recognized by the verification time evaluation model, and for single learning, the corresponding standard sampling rate is a single learning rate.

8. The blockchain-based big data information security verification and reporting method according to claim 7 is characterized in that: The verification pre-data and the change characteristics are passed into the verification time evaluation model, the verification time evaluation model learns the verification pre-data, generates a corresponding verification time change map, and annotates and verifies the verification time change map according to the change characteristics to obtain the corresponding time value.

9. The blockchain-based big data information security verification and reporting method according to claim 8 is characterized in that: The time value is compared with a time threshold, and when the time value is less than the time threshold, the data to be verified is marked as safe data and reported.

10. The blockchain-based big data information security verification and reporting method according to claim 9 is characterized in that: When the time value is greater than the time threshold, the user terminal triggers the data blocking instruction, marks the data to be verified as leaked data and blocks it.

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