Block chain-based big data information security verification and filling method

Abstract

The invention relates to the field of information encryption, in particular to a block chain-based big data information security verification and filling method, and the method comprises the steps: generating a node proof corresponding to to-be-verified data at a user side, uploading the to-be-verified data and the corresponding node proof to a block chain network, and randomly distributing the to-be-verified data and the corresponding node proof to each verification node, the verification node performs node verification on the authenticity of the visiting user by using the node proof, collects the verification time of completing the node verification by the visiting user, pre-processes the verification time, generates corresponding verification pre-data, and learns the verification pre-data by using the verification time evaluation model; the corresponding verification time change graph is obtained, the data blocking instruction is triggered when the time value exceeds the time threshold value, the specific content of the to-be-verified data cannot be leaked when the node proof is generated, the authenticity of a visiting user can be verified, the information of the data cannot be obtained, and therefore the data privacy is effectively protected, and the user experience is improved. And the security of the data in the transmission process is ensured.

Description

Technical Field

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

[0002] Blockchain is a distributed ledger technology with characteristics such as immutability, decentralization, and transparency. In blockchain, data is recorded in the form of blocks and its security and integrity are ensured through cryptographic techniques. Blockchain technology provides a basis for the secure storage and verification of data, but faces challenges in scalability and privacy protection.

[0003] With the development of big data technology, the storage, transmission, and sharing of data have become increasingly complex, and data security and privacy protection have become key issues. Traditional data verification methods often require the disclosure of data content and cannot meet the requirements of privacy protection. Therefore, a technical solution that can both verify data authenticity and protect data privacy is needed.

[0004] Chinese Patent Authorization Publication No.: CN116828087B discloses an information security system based on blockchain connection, which obtains network packets in a network packet stream; extracts the text descriptions of each attribute item and the attribute information under each attribute item in the network packets; extracts a network packet semantic understanding feature vector from the text descriptions of each attribute item and the attribute information under each attribute item; and determines whether to upload the network packet to the blockchain network based on the network packet semantic understanding feature vector. In this way, the balanced processing of network packets can be realized based on deep learning and blockchain technology, the distribution method of network packets can be optimized, the traffic distribution of each output port can be made more uniform, and thus the overall performance and stability of the network information security system can be improved. At the same time, uploading network packets to the blockchain can improve the reliability of the information security system and help prevent and respond to network threats.

[0005] Chinese Patent Authorization Publication No.: CN111669377B discloses a security control method for blockchain on-chain information, including the following steps: adding an authentication unit for identifying user identity and an encoding unit for recompiling the information of the parent data chain at both ends of the head and tail of the parent data chain sent to the blockchain; the authentication unit identifies the verification code information input by the user; the encoding unit mirrors and encrypts all the data in the parent data chain to obtain a virtual data chain according to the incorrect verification code identified by the authentication unit, detaches 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 target blockchain, and the encoding unit saves the encoding unit detached from the parent data chain respectively according to the secondary identification result of the authentication unit; this solution realizes the security protection of information data, improves the fault tolerance when information data is stolen, and ensures the high encryption of account identity.

[0006] However, the above method has the following problems: the information management process needs to disclose the data content, which cannot meet the requirements of privacy protection. Summary of the Invention

[0007] Therefore, the present invention provides a method for verifying and filling big data information security based on blockchain to overcome the problem in the prior art that the information management process needs to disclose the data content and cannot meet the requirements of privacy protection.

[0008] To achieve the above object, the present invention provides a method for verifying and filling big data information security based on blockchain, which is characterized by including:

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

[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 uses the node certificate to perform node verification on the authenticity of the visiting user and collects the verification time for the visiting user to complete the node verification;

[0012] Select several change characteristics of the verification time, preprocess the verification time, and generate corresponding verification pre-data. Use the verification time evaluation model to learn the verification pre-data to obtain a corresponding verification time change map;

[0013] Extract the time value in the verification time change map, compare the time value with a time threshold, and trigger a data locking instruction when the time value exceeds the time threshold, where

[0014] The node certificate is used to prove the authenticity of the visiting user without disclosing 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 for the verification time for the visiting user to complete the node verification.

[0017] Further, 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 certificate algorithm;

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

[0020] The client calculates relevant node proofs according to the polynomial representation.

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

[0022] Upload the encrypted data to be verified and the corresponding node proofs to the blockchain network;

[0023] Use a hash function to calculate the verification nodes randomly allocated for the data to be verified;

[0024] Correspond the node proofs with the data to be verified, and upload the node proofs to the corresponding verification nodes.

[0025] Further, the step of the verification node using the node proofs to verify the authenticity of the visiting user includes:

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

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

[0028] The visiting user combines the random challenge value with its own secret key, calculates and returns an encrypted response value;

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

[0030] Further, when the encrypted response value is consistent with the encrypted proof value, it is determined that the node verification is successful, and when the encrypted response value is inconsistent with the encrypted proof value, it is determined that the node verification fails.

[0031] Further, a timer is provided in the verification node. When the verification node monitors 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] Further, the step of generating verification pre-data includes:

[0033] Select several change characteristics of the verification time;

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

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

[0036] Further, the verification pre-data and the change features are input into the verification time evaluation model. The verification time evaluation model learns the verification pre-data to generate a corresponding verification time change map, annotates and verifies the verification time change map according to the change features, and obtains a corresponding time value.

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

[0038] Further, when the time value is greater than the time threshold, the client triggers the data lockdown instruction, marks the data to be verified as leaked data and locks it.

[0039] Compared with the prior art, the present invention generates a node certificate corresponding to the data to be verified at the client, 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 for the visiting user to complete the node verification, preprocesses the verification time, and generates corresponding verification pre-data. The verification time evaluation model is used to learn the verification pre-data to obtain a corresponding verification time change map and trigger a data lockdown instruction when the time value exceeds the time threshold. The specific content of the data to be verified is not leaked when generating the node certificate, which can not only verify the authenticity of the visiting user but also cannot obtain the information of the data itself, thus effectively protecting data privacy and ensuring the security of data during transmission.

[0040] Further, by calculating the node certificate using polynomial representation, the authenticity of the data can be proven without leaking the data content, that is, the verifier can be convinced of the authenticity of the data without knowing the specific content of the data. The generation of the node certificate is based on polynomial representation, the calculation process is efficient and easy to verify, reducing the consumption of verification time and computing resources. The generation process of the node certificate can be adjusted according to the data volume and complexity, and is suitable for the processing and verification of large-scale data.

[0041] Furthermore, by correlating the node proofs with the data to be verified, it is ensured that the verification nodes can accurately identify and verify the authenticity of the data during the verification process. The node proofs are uploaded to the corresponding verification nodes, making the verification process more efficient. The verification nodes can directly use the node proofs for verification without having to recalculate or obtain additional information. The node proofs can prove the authenticity of the data without revealing the data content, further protecting the privacy of the data.

[0042] Furthermore, the unpredictability of the verification process is increased by sending random challenge values, making each verification process unique and preventing replay attacks. The visiting user combines the random challenge value with their own secret key, calculates and returns an encrypted response value. This process ensures that only users with the correct secret key can generate a valid response value. By generating the encrypted response value, it is ensured that the user's identity information is not 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, improving the 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 is consistent with the encrypted proof value, the verification node determines that the node verification is successful, the timer stops timing, and the corresponding verification time is counted. This step ensures that each successful verification process has a clear time record, facilitating subsequent analysis and evaluation. When the encrypted response value is inconsistent with the encrypted proof value, the verification node determines that the node verification fails, and the timer invalidates the verification time. This step ensures that only successful verification processes are recorded, avoiding interference from the time data of failed verifications in subsequent analysis.

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

[0045] Furthermore, by comparing the time value with the time threshold, abnormal behaviors during the verification process can be detected in a timely manner, preventing data leakage or tampering. When an abnormality is detected, a data lockdown instruction is immediately triggered to ensure that potential leaked data is not further spread or used. By analyzing the changing features of the verification time, the verification process can be optimized, unnecessary verification steps can be reduced, and the overall efficiency can be improved. Brief Description of the Drawings

[0046] Figure 1Flowchart of the big data information security verification and filling method based on blockchain according to the present invention;

[0047] Figure 2 Flowchart of generating node certificates corresponding to the data to be verified in the embodiment of the present invention;

[0048] Figure 3 Flowchart of randomly allocating the data to be verified and the corresponding nodes to each verification node in the blockchain network in the embodiment of the present invention;

[0049] Figure 4 Flowchart of the verification node using the node certificate to verify the authenticity of the visiting user in the embodiment of the present invention. Detailed implementation manners

[0050] In order to make the purpose and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

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

[0052] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for 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, and therefore cannot be understood as a limitation of the present invention.

[0053] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0054] Please refer to Figure 1 As shown, it is a flowchart of the big data information security verification and filling method based on blockchain according to 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 at the user terminal;

[0056] Step S2, the client 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 verify 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, preprocess the verification time, and generate corresponding pre-verification data. Use the verification time evaluation model to learn the pre-verification data to obtain the corresponding verification time change map;

[0059] Step S5, extract the time values in the verification time change map, compare the time values with the time threshold, and trigger a data lock instruction when the time value exceeds the time threshold, where

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

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

[0062] The time threshold is the minimum standard for the verification time when the visiting user completes the node verification.

[0063] By generating a node certificate corresponding to the data to be verified at the client, uploading the data to be verified and the corresponding node certificate to the blockchain network, and randomly distributing them to each verification node, the verification node uses the node certificate to verify the authenticity of the visiting user, collects the verification time when the visiting user completes the node verification, preprocesses the verification time, generates corresponding pre-verification data, uses the verification time evaluation model to learn the pre-verification data, obtains the corresponding verification time change map, and triggers a data lock instruction when the time value exceeds the time threshold. When generating the node certificate, the specific content of the data to be verified will not be leaked, which can not only verify the authenticity of the visiting user but also cannot obtain the information of the data itself, thus effectively protecting data privacy and ensuring the security of data during transmission.

[0064] Please refer to Figure 2 As shown in the figure, it is a flowchart for generating a node certificate corresponding to the data to be verified in an embodiment of the present invention, including:

[0065] Step S11, the client encrypts the data to be verified and selects a node certificate algorithm;

[0066] Step S12, the client constructs a corresponding polynomial representation according to the data to be verified;

[0067] Step S13, the client calculates the relevant node proof according to the polynomial representation.

[0068] In a specific implementation, through encryption, the data to be verified will not be leaked during the transmission and storage processes. Even if the data is intercepted or accessed, unauthorized users cannot obtain the actual content of the data. The choice of encryption algorithm can be adjusted according to the sensitivity of the data and the application scenario to further enhance the security of the data.

[0069] Users can select different node proof algorithms according to specific requirements, 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 their own advantages in terms of performance, security, and privacy protection. Users can select the most suitable algorithm according to actual needs.

[0070] The polynomial representation can simplify complex data structures into polynomial forms, thereby reducing the storage and transmission costs of the data.

[0071] By calculating the node proof according to the polynomial representation, the authenticity of the data can be proven without revealing the data content, that is, the verifier can be convinced of the authenticity of the data without knowing the specific content of the data. The generation of the node proof is based on the polynomial representation, and the calculation process is efficient and easy to verify, reducing the consumption of verification time and computing resources. The generation process of the node proof can be adjusted according to the data volume and complexity, and is applicable to the processing and verification of large-scale data.

[0072] Please refer to Figure 3 as shown, which is a flowchart of the embodiment of the present invention for randomly allocating 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 proof to the blockchain network;

[0074] Step St2, use a hash function to calculate the verification node corresponding to the randomly allocated data to be verified;

[0075] Step St3, associate the node proof with the data to be verified and upload the node proof to the corresponding verification node.

[0076] In a specific implementation, the 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 output of the hash function is random, which randomly distributes the data to be verified to different verification nodes, increasing the unpredictability of data distribution and enhancing the security of the system. The hash function can evenly distribute the data to each verification node, avoiding data concentration on a few nodes, thereby improving the load balancing and efficiency of the system. The irreversibility of the hash function ensures the unidirectionality of the distribution process. Once the data is distributed to a certain node, it is impossible to reverse the original data through the hash value, further enhancing the security of the data.

[0078] The entire process ensures the security and privacy of data during the upload, distribution, and verification processes. The data is encrypted and uploaded to the blockchain network, and randomly distributed to verification nodes through the hash function. The nodes prove the verification without revealing the data content.

[0079] Through the random distribution of the hash function and the efficient verification of node proofs, the verification time and consumption of computing resources are reduced, improving the overall efficiency of the system.

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

[0081] The immutable property of the blockchain and the transparency of the data make the behaviors of all verification nodes recorded on the chain, enhancing the credibility and transparency of the system.

[0082] This method is applicable to the processing and verification of large-scale data, can effectively meet the information security requirements in the big data environment, and can be adjusted and extended according to actual needs.

[0083] By corresponding the node proof with the data to be verified, it ensures that the verification node can accurately identify and verify the authenticity of the data during the verification process. The node proof is uploaded to the corresponding verification node, making the verification process more efficient. The verification node can directly use the node proof for verification without recalculating or obtaining additional information. The node proof can prove the authenticity of the data without revealing the data content, further protecting the privacy of the data.

[0084] Please refer to Figure 4 as shown, which is a flowchart of the verification node of the embodiment of the present invention using the node proof to verify the authenticity of the visiting user, including:

[0085] Step Sp1, when the visiting user accesses the data to be verified, the verification node generates an encrypted proof value according to 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 their own secret key, calculates and returns the encrypted response value.

[0088] Step Sp4: Compare the encrypted response value with the encrypted proof value to determine whether the node verification is successful.

[0089] In a specific implementation, the verification node generates the encrypted proof value based on the node proof, which is the basis of the verification process. The encrypted proof value is the key data for the verification node to verify the identity of the visiting user. By generating the encrypted proof value, the security of the verification process is ensured. Even if the verification node is attacked, the attacker cannot directly obtain the real information of the visiting user.

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

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

[0092] By sending the random challenge value, the unpredictability of the verification process is increased, making each verification process unique and preventing replay attacks. The visiting user combines the random challenge value with their own secret key, calculates and returns the encrypted response value. This process ensures that only users with the correct secret key can generate a valid response value. By generating the encrypted response value, it is ensured that the user's identity information will not 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 real, improving the 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, it is determined that the node verification is successful; when the encrypted response value is inconsistent with the encrypted proof value, it is determined that the node verification fails.

[0094] Specifically, a timer is set 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 is successful, the timer stops timing and counts the corresponding verification time. When the node verification fails, the timer invalidates the verification time.

[0095] In specific implementations, by recording the verification time, the efficiency of the verification process can be evaluated. For example, the performance differences of different verification nodes can be analyzed, or the verification algorithm can be optimized to reduce the verification time. The recording of the verification time can also be used for security monitoring. If the verification time of a certain verification node is abnormally short or long, it may imply potential security issues, such as attack behaviors or hardware failures.

[0096] Invalidating the time data of failed verifications can improve the accuracy and reliability of statistical analysis. For example, when calculating the average verification time or evaluating the verification efficiency, excluding the time data of failed verifications can avoid biases in the results. Invalidating the time data of failed verifications can also prevent attackers from obtaining information about the system performance through multiple attempts. If the time data of failed verifications is retained, attackers may use this data to analyze the weaknesses of the system. The recording of the verification time can help quickly locate system failures. If the verification time of a certain verification node suddenly becomes longer, it may be a hardware failure or a network problem. Invalidating the time data of failed verifications 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 revealing the user's secret key and data content, ensuring the privacy of the user.

[0098] When the encrypted response value is consistent with the encrypted proof value, the verification node determines that the node verification is successful, the timer stops timing, and the corresponding verification time is statistically recorded. This step ensures that each successful verification process has a clear time record for subsequent analysis and evaluation. When the encrypted response value is inconsistent with the encrypted proof value, the verification node determines that the node verification fails, and the timer invalidates the verification time. This step ensures that only successful verification processes are recorded, avoiding interference from the time data of failed verifications in subsequent analysis.

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

[0100] Selecting several change characteristics of the verification time;

[0101] Dividing the verification time 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 verification time evaluation model can recognize, and for a single learning, its corresponding standard sampling rate is a single learning rate.

[0103] In specific implementation, calculate the rate of change of the verification time, that is, the difference between adjacent verification times. This can help identify fluctuations in the verification time, such as whether there are abnormal accelerations or decelerations. Analyze the periodic changes in the verification time, such as whether there are regular peaks or valleys. This can help identify periodic behaviors 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 according to the requirements of the verification time evaluation model. An overly high sampling rate may result in overly dense data, increasing the computational cost; an overly low sampling rate may cause the loss of important information in the data.

[0105] Divide the verification time according to the standard sampling rate to form multiple sampling points. Each sampling point contains a segment of the verification time, and these segments together constitute the verification pre-data. Format the segmented verification time segments into a format that the verification time evaluation model can accept, such as converting the time data into a numerical vector or matrix.

[0106] By sampling and feature extraction of the verification time to generate verification pre-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 the standard sampling rate ensures that the verification time evaluation model can effectively identify and learn the change characteristics of the verification time, thereby improving the performance of the model. The generation of verification pre-data ensures the integrity and consistency of the verification time data, providing a reliable data basis for subsequent security analysis.

[0107] Specifically, input the verification pre-data and change characteristics into the verification time evaluation model. The verification time evaluation model learns from the verification pre-data to generate a corresponding verification time change map, annotate and verify the verification time change map according to the change characteristics, and obtain the corresponding time value.

[0108] Specifically, compare the time value with the time threshold. When the time value is less than the time threshold, mark the data to be verified as safe data and fill it in.

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

[0110] In specific implementation, input the verification pre-data that has undergone sampling and feature extraction into the verification time evaluation model. These data contain the change characteristics of the verification time, such as the rate of change, periodicity, etc. At the same time, input the change characteristics of the verification time into the model to help the model better understand and learn the laws of the verification time.

[0111] The verification time evaluation model generates a corresponding verification time change map by learning from the verification pre-data. This map reflects the change trends and patterns of the verification time. The verification time change map is labeled according to the change characteristics to ensure the accuracy and reliability of the map. Through the verification step, abnormal points 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 safety requirements.

[0113] The time value is less than the time threshold: When the time value is less than the time threshold, it indicates 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 security risks.

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

[0116] When the time value is greater than the time threshold, the user side triggers a data lockdown instruction, marks the data to be verified as leaked data and locks it down. This step can prevent potential leaked data from being further spread or used. The lockdown measures can include restricting data access, notifying the system administrator, recording abnormal events, etc. to ensure the security of the data.

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

[0118] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0119] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for verifying and filling in big data information security based on blockchain, characterized in that, Including: The client encrypts the data to be verified and generates a node certificate corresponding to the data to be verified at the client; The client 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 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; Select several change characteristics of the verification time, preprocess the verification time, and generate corresponding verification pre-data. Use the verification time evaluation model to learn the verification pre-data to obtain a corresponding verification time change map; Extract the time value in the verification time change map, compare the time value with a time threshold, and trigger a data lockdown instruction when the time value exceeds the time threshold, where 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 for the verification time when the visiting user completes the node verification.

2. The method for verifying and filling big data information security based on blockchain according to claim 1, characterized in that, The steps of generating a node certificate corresponding to the data to be verified include: The client encrypts the data to be verified and selects a node certificate algorithm; The client constructs a corresponding polynomial representation according to the data to be verified; The client calculates the relevant node certificate according to the polynomial representation.

3. The method for verifying and filling big data information security based on blockchain according to claim 2, wherein, The steps of randomly distributing the data to be verified and the corresponding node to each verification node in the blockchain network include: Upload the encrypted data to be verified and the corresponding node certificate to the blockchain network; Use a hash function to calculate the verification node randomly assigned to the data to be verified; Correspond the node certificate with the data to be verified and upload the node certificate to the corresponding verification node.

4. The method for verifying and filling in big data information security based on blockchain according to claim 3, characterized in that, The steps for the verification node to use the node certificate to perform node verification on the authenticity of the visiting user include: When the visiting user accesses the data to be verified, the verification node generates an encrypted proof value according to the node certificate; The verification node sends a random challenge value to the visiting user; The visiting user combines the random challenge value with its own secret key, calculates and returns an encrypted response value; Compare the encrypted response value with the encrypted proof value to determine whether the node verification is successful.

5. The method for verifying and filling in big data information security based on blockchain according to claim 4, characterized in that When the encrypted response value is consistent with the encrypted proof value, it is determined that the node verification is successful. When the encrypted response value is inconsistent with the encrypted proof value, it is determined that the node verification fails.

6. The method for verifying and filling big data information security based on blockchain according to claim 5, wherein, A timer is provided in the verification node. When the verification node monitors 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 statistics the corresponding verification time. When the node verification fails, the timer invalidates the verification time.

7. The method for verifying and filling in big data information security based on blockchain according to claim 6, characterized in that, The steps of generating verification pre-data include: Select several change features of the verification time; Divide the verification time according to the standard sampling rate to form corresponding verification pre-data, where The standard sampling rate is the learning rate that the verification time evaluation model can recognize, and for a single learning, its corresponding standard sampling rate is a single learning rate.

8. The method for verifying and filling big data information security based on blockchain according to claim 7, characterized in that Input the verification pre-data and the change features into the verification time evaluation model. The verification time evaluation model learns the verification pre-data to generate a corresponding verification time change map, annotate and verify the verification time change map according to the change features, and obtain the corresponding time value.

9. The method for verifying and filling big data information security based on blockchain according to claim 8, characterized in that, Compare the time value with the time threshold. When the time value is less than the time threshold, mark the data to be verified as safe data and fill it in.

10. The method for verifying and filling big data information security based on blockchain according to claim 9, characterized in that, When the time value is greater than the time threshold, the client triggers the data lockdown instruction, marks the data to be verified as leaked data and locks it.

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