An integrity verification processing method and system for data trusted transmission

Abstract

This application provides a method and system for integrity verification of trusted data transmission. The method includes: acquiring trusted transmission data records of a transmission node and performing trustworthiness analysis; acquiring transmission efficiency requirements and data similarity parameters for the current transmission node and receiving node to transmit multiple target data blocks; within the transmission node, constructing a hash tree based on the location information of multiple target data blocks and multiple basic data blocks, configuring the number of random position adjustments, and obtaining N position adjustment strategies; randomly adjusting the locations of multiple target data blocks according to the N position adjustment strategies, obtaining N sets of adjusted data location information; calculating the malicious rate based on the verification hash tree and multiple adjusted verification hash trees; and combining this with the trustworthiness of the transmission node to calculate a verification score, which serves as the integrity verification result. This solves the technical problem in the prior art where data transmission security is low when multiple data blocks contain the same data.

Description

Technical Field

[0001] This invention relates to the field of data transmission, and in particular to an integrity verification processing method and system for trusted data transmission. Background Technology

[0002] In existing technologies, during data transmission verification, a hash tree is generated based on multiple data blocks before transmission. All hash trees are then transmitted to the receiver. The receiving node generates its own hash tree and verifies its consistency. If the hash trees match, the data integrity verification is successful; otherwise, there is a risk of data tampering, and the data integrity verification fails. However, when multiple data blocks contain identical data, a second preimage attack can occur, compromising data transmission security. Summary of the Invention

[0003] This invention addresses the technical problem of low data transmission security when multiple data blocks contain the same data in the prior art, and provides an integrity verification processing method and system for trusted data transmission.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides an integrity verification processing method for trusted data transmission, comprising: Acquire reliable transmission data records of the transmission node within a historical time period, perform node reliability analysis on the transmission node, and obtain the reliability of the transmission node. Obtain the transmission efficiency requirements parameters for transmitting multiple target data blocks at the current transmission node and receiving node, and analyze the common parameters of the multiple target data blocks; Within the transmission node, a hash tree is constructed based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree. The number of times the location is randomly adjusted is configured according to the transmission efficiency requirement parameter, the trustworthiness of the transmission node, and the data sameness parameter to obtain the number of times the location is randomly adjusted N. N location adjustment strategies are generated, where N is a positive integer. According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain N sets of adjusted data position information. Multiple adjustment hash trees are constructed by combining the multiple target data blocks, and data is transmitted. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

[0005] Secondly, the present invention provides an integrity verification processing system for trusted data transmission, comprising: The credibility analysis module is used to obtain the reliable transmission data records of the transmission node in the historical time period, perform node credibility analysis of the transmission node, and obtain the credibility of the transmission node. The data acquisition module is used to obtain the transmission efficiency requirements parameters for the transmission of multiple target data blocks by the current transmission node and receiving node, and to analyze the common parameters of the multiple target data blocks. The hash tree construction module is used to construct a hash tree within the transmission node based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree. It also configures the number of random position adjustments based on the transmission efficiency requirement parameters, transmission node credibility, and data sameness parameters to obtain the number of random position adjustments N, and generates N position adjustment strategies, where N is a positive integer. The optimized output module is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies to obtain N sets of adjusted data position information. It then constructs multiple adjustment hash trees by combining the multiple target data blocks and transmits the data. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

[0006] The beneficial effects of this invention are: Compared to existing technologies, this application first obtains trusted transmission data records of transmission nodes over a historical period, performs node trustworthiness analysis to obtain and quantify node trustworthiness, providing a reliable basis for subsequent data transmission integrity verification. Second, it obtains transmission efficiency requirements parameters for the current transmission and receiving nodes to transmit multiple target data blocks, and analyzes the data similarity parameters of multiple target data blocks, obtaining the data transmission efficiency requirements and data similarity parameters, providing necessary data support for subsequent verification strategy optimization. Third, within the transmission node, a hash tree is constructed based on the location information of multiple target data blocks and multiple basic data blocks to obtain a basic hash tree. Then, based on the transmission efficiency requirements parameters, transmission node trustworthiness, and data similarity parameters, the number of random position adjustments is configured to obtain the number of random position adjustments N, and N position adjustment strategies are generated. This ensures both the trustworthiness and security of data transmission while also considering data transmission efficiency. Finally, according to N position adjustment strategies, multiple target data blocks are randomly adjusted to obtain N sets of adjusted data position information. Multiple adjustment hash trees are constructed by combining multiple target data blocks and transmitted to the receiving node. Based on the multiple target data blocks and N position adjustment strategies, a verification hash tree and multiple adjustment verification hash trees are generated and verified to obtain the malfunction rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated as the integrity verification result, which improves the security of data transmission.

[0007] Through the above technical solution, this application quantifies the trustworthiness of nodes based on historical data and verifies the integrity of data transmission by dynamically constructing a hash tree. In this way, the security of data transmission is improved when the same data exists in multiple data blocks. Attached Figure Description

[0008] Figure 1 A flowchart illustrating an integrity verification process for trusted data transmission provided by this invention; Figure 2 This is a schematic diagram of the structure of an integrity verification processing system for trusted data transmission provided by the present invention.

[0009] In the attached diagram, the components represented by each number are as follows: Credibility analysis module 11, data acquisition module 12, hash tree construction module 13, and output optimization module 14. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0012] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0013] Example 1, as Figure 1 As shown, this embodiment of the invention provides an integrity verification processing method for trusted data transmission, including: S10: Obtain trusted transmission data records of the transmission node within a historical time period, perform node trustworthiness analysis on the transmission node, and obtain the trustworthiness of the transmission node. In dynamically changing network environments, the reliability of node data transmission may vary over time, and traditional static trust models cannot quantitatively assess a node's historical transmission performance. To improve the quality of data transmission integrity verification, it is necessary to evaluate the reliability of node data transmission based on the node's trustworthiness in historical data.

[0014] To address the aforementioned issues, this application obtains trusted transmission data records of transmission nodes over a historical period, performs node trustworthiness analysis on the transmission nodes, and obtains the trustworthiness of the transmission nodes, thereby assessing the transmission trustworthiness of the nodes.

[0015] Specifically, step S10 in the method includes: Obtain trusted transmission data records of transmission nodes within a historical time period, wherein the trusted transmission data records include the integrity verification results of trusted data transmission within the historical time period; Extract the proportion of integrity verification results that are negative within the trusted transmission data record to obtain the historical integrity verification coefficient; Obtain the average complete verification coefficient for trusted data transmission across multiple nodes; The ratio of the average complete verification coefficient to the historical complete verification coefficient is calculated as the reliability of the transmission node.

[0016] In this embodiment of the application, trusted transmission data records of the transmission node within a historical time period are first obtained. The trusted transmission data records include the integrity verification results of trusted data transmission within the historical time period. The integrity verification results include two types: "yes" and "no". "Yes" indicates that the integrity verification was successful, and "no" indicates that the integrity verification was unsuccessful.

[0017] Secondly, the proportion of "no" integrity verification results within trusted transmission data records is extracted to obtain the historical integrity verification coefficient. The historical integrity verification coefficient = number of "no" integrity verification results / total number of data transmissions. The historical integrity verification coefficient reflects the node's historical transmission failure rate. For example, if the total number of trusted data transmissions is 1000, and the number of "no" integrity verification results is 50, then the historical integrity verification coefficient = 50 / 1000 = 0.05.

[0018] Next, obtain the average complete verification coefficient for trusted data transmission across multiple nodes. The average complete verification coefficient = total number of times the verification result is negative across multiple nodes / total number of data transmissions across multiple nodes. The average complete verification coefficient reflects the average failure rate of multiple nodes and can be used as a benchmark for evaluating node trustworthiness. For example, if the total number of data transmissions across 30 nodes is 20,000, and the total number of times the verification result is negative is 3,000, then the average complete verification coefficient = 3,000 / 20,000 = 0.15.

[0019] Finally, the ratio of the average complete verification coefficient to the historical complete verification coefficient is calculated as the transmission node reliability. Transmission node reliability = average complete verification coefficient / historical complete verification coefficient. A higher transmission node reliability reflects a lower historical transmission failure rate, meaning the node is more reliable. For example, if the average complete verification coefficient is 0.15, the historical complete verification coefficient of node 1 is 0.05, and the historical complete verification coefficient of node 2 is 0.04, then the transmission node reliability of node 1 = 0.15 / 0.05 = 3, and the transmission node reliability of node 2 = 0.15 / 0.04 = 3.75. Since node 2 has a lower historical transmission failure rate, its calculated transmission node reliability is higher, indicating that node 2 has higher transmission reliability than node 1.

[0020] In summary, compared to existing technologies, this application obtains the trustworthiness of transmission nodes by acquiring trusted transmission data records of transmission nodes over a historical period and performing node trustworthiness analysis. Thus, by comparing the historical transmission performance of a node with the average transmission benchmark, the trustworthiness of the node can be quantified, providing a reliable basis for subsequent data transmission integrity verification, and thereby identifying and constraining nodes with low trustworthiness during transmission.

[0021] S20: Obtain the transmission efficiency requirement parameters for the current transmission node and receiving node to transmit multiple target data blocks, and analyze the data common parameters of the multiple target data blocks; Fixed verification strategies cannot adaptively adjust to actual conditions, leading to an imbalance between security and efficiency. For example, in scenarios with high real-time requirements (such as industrial control data transmission), excessive verification time can cause delays, or in scenarios with high data redundancy (such as repeated file transfers), unnecessary resource waste may occur.

[0022] To address the aforementioned issues, this application obtains the transmission efficiency requirements parameters for transmitting multiple target data blocks at the current transmission node and receiving node, and analyzes the data similarities of the multiple target data blocks.

[0023] Specifically, step S20 in the method includes: Obtain the transmission efficiency requirement parameters for the current transmission node and receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time; Collect data feature information of the multiple target data blocks, and analyze and obtain the common parameters of the multiple target data blocks based on the data feature information.

[0024] In this embodiment, the transmission efficiency requirement parameters for transmitting multiple target data blocks between the current transmitting node and the receiving node are first obtained. These parameters include transmission time (e.g., maximum allowed transmission time), which limits the computational overhead and number of data interactions during the verification process. For example, the transmission time must be controlled within a threshold (e.g., 100ms). Constraining the transmission time avoids exceeding the transmission delay limit due to excessive verification.

[0025] Secondly, data feature information (such as data type, size, hash value, etc.) of multiple target data blocks is collected. Based on this data feature information, the common parameters of the data in multiple target data blocks are analyzed and obtained. Specifically, multiple sets of historical data blocks with the same data feature information are indexed in the transmitted data over a historical period. Then, the number of identical data blocks appearing in each of the multiple historical data block sets is extracted, and the average value is calculated to obtain the data similarity parameter. Furthermore, the data similarity parameter reflects the prevalence of data blocks in historical transmission. The larger the data similarity parameter, the higher the data redundancy, and the lower the possibility that attackers will use repetitive patterns to forge data.

[0026] Furthermore, the step of "analyzing and obtaining the same data parameters of the multiple target data blocks based on the data feature information" includes: Based on the data feature information, in the transmitted data within a historical time period, a set of multiple historical data blocks with the same data feature information is indexed; Extract the number of identical data blocks in each of the multiple historical data block sets, calculate the average, and obtain the data identical parameters.

[0027] In this embodiment, firstly, based on data characteristic information (such as data type, size, hash value, etc.), multiple sets of historical data blocks with the same data characteristic information are indexed in the transmitted data within a historical time period. The sets of historical data blocks reflect transmission records with similar characteristics to the current data during historical transmission. For example, multiple sets of historical data blocks of JPG and 2MB are retrieved from the historical transmitted data based on data characteristic information (JPG, 2MB).

[0028] Secondly, the number of identical data blocks appearing in multiple historical data block sets is extracted, and the average value is calculated to obtain the data similarity parameter. For example, the number of data blocks with the same characteristics (JPG and 2MB) as the currently transmitted data blocks extracted from historical records is 15 and 25 respectively, with an average value of 20, which is used as the data similarity parameter. Thus, the data similarity parameter reflects the probability of data block repetition; a higher value indicates a higher frequency of recurrence of this type of characteristic data in historical transmissions, and stronger data redundancy.

[0029] In summary, compared to existing technologies, this application obtains the transmission efficiency requirements parameters for transmitting multiple target data blocks between the current transmitting and receiving nodes, and analyzes the data similarity parameters of the multiple target data blocks. Thus, it obtains the data transmission efficiency requirements and data similarity parameters, providing necessary data support for the subsequent optimization of verification strategies.

[0030] S30: Within the transmission node, a hash tree is constructed based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree. The number of times the location is randomly adjusted is configured based on the transmission efficiency requirement parameter, the reliability of the transmission node, and the data sameness parameter to obtain the number of times the location is randomly adjusted N. N location adjustment strategies are generated, where N is a positive integer. In scenarios involving data chunked transmission, traditional single hash values ​​are insufficient for verifying the integrity and order of multiple data blocks and are vulnerable to second preimage attacks. Therefore, a hierarchical verification structure that binds data content and location is needed for integrity verification. Furthermore, simply adding location information to generate new hash values ​​may still result in insufficient verification data. More hash trees need to be generated for verification by randomly adjusting the positions of data blocks. The more times the positions are randomly adjusted, the more hash trees are generated, leading to more accurate verification. However, this results in longer processing time and lower verification efficiency.

[0031] To address the aforementioned issues, this application constructs a basic hash tree within the transmission node based on the location information of multiple target data blocks and multiple basic data blocks. It then configures the number of random position adjustments based on parameters such as transmission efficiency requirements, transmission node trustworthiness, and data similarity, thereby obtaining the number of random position adjustments N and generating N position adjustment strategies.

[0032] Specifically, step S30 in the method includes: Within the transmission node, acquire multiple basic data location information of the multiple target data blocks; Based on the multiple target data blocks and multiple basic data location information, multiple first-level basic hash values ​​are generated; Based on each pair of adjacent first-level basic hash values ​​within the plurality of first-level basic hash values, a second-level basic hash value is generated, and this process continues until the root basic hash tree is obtained, thus obtaining the basic hash tree.

[0033] In this embodiment, the basic data location information of multiple target data blocks is first obtained within the transmission node. Specifically, the basic data location information reflects the index or arrangement order of the data blocks in the original transmission sequence. For example, if the data blocks are arranged in transmission order as Block1, Block2, and Block3, the corresponding location information is [0, 1, 2]. Furthermore, the basic data location information reflects the original order of the data blocks and is an important input element for subsequent hash value generation, ensuring the binding relationship between data content and location and preventing attackers from circumventing verification by rearranging data blocks.

[0034] Secondly, based on multiple target data blocks and multiple basic data location information, multiple layer-one basic hash values ​​are generated. Specifically, for each data block, its content is concatenated with its corresponding location information and then hashed (e.g., using the SHA-256 algorithm) to obtain the underlying hash value. For example, if the content of Block1 is "Data" and the location information is "0", the underlying hash value obtained after SHA-256 calculation is Hash("Data" + "0"). In this way, it is ensured that the hash value of each data block simultaneously contains both content information and location information, forming a basic verification unit for binding data and location.

[0035] Finally, based on every two adjacent first-level basic hash values ​​within multiple first-level basic hash values, second-level basic hash values ​​are generated, and this process continues until a root basic hash tree is obtained, thus obtaining the basic hash tree. Specifically, every two adjacent hash values ​​within a first-level basic hash value are concatenated and hashed again to generate second-level basic hash values. This concatenation and generation is repeated layer by layer upwards until a unique root basic hash value is generated, thus obtaining the basic hash tree. For example, if the first-level hash values ​​are H1, H2, H3, and H4, then the second-level hash values ​​are Hash (H1+H2) and Hash (H3+H4), and the third-level root hash value is Hash (second-level hash value 1 + second-level hash value 2). In this way, a unique root basic hash value is generated, and the basic hash tree is obtained. Furthermore, the layer-by-layer upward hash tree structure ensures that any tampering or relocation of any data block will cause a change in the hash value along the entire path from that point to the root hash value. This allows the receiver to quickly determine whether the data set has been tampered with simply by comparing the root hash value, thereby achieving efficient verification of data integrity. In this way, the final generated basic hash tree can not only reflect the integrity of the original data, but also provide the necessary benchmark framework for subsequent position adjustment strategies, ensuring that all adjusted hash trees can be mutated and verified based on the original structure.

[0036] Furthermore, the step of "configuring the number of random position adjustments based on the transmission efficiency requirement parameter, the reliability of the transmission node, and the data similarity parameter, obtaining the number of random position adjustments N, and generating N position adjustment strategies" includes: Based on the number of the multiple target data blocks, set the space for random position adjustment; Within the space of random position adjustment times, a first number of position adjustments is randomly generated; Calculate the ratio of the number of times the first position was adjusted to the reliability of the transmission node and the same parameter of the data, respectively, to obtain the first reliability score and the first security score; Obtain the time for adjusting the data block position according to the first position adjustment number, and use it as the first position adjustment time; Calculate the ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain the first time score; The first credibility score, the first security score, and the first time score are weighted and calculated to obtain the first position adjustment score; Continue to randomly generate the number of position adjustments for optimization, and calculate the position adjustment score until convergence. Output the number of random position adjustments N with the highest position adjustment score, where N is a positive integer. Based on the number of random position adjustments N, the basic data position information of multiple target data blocks is randomly selected and adjusted to obtain N position adjustment strategies.

[0037] In this embodiment, the number of random position adjustments is first set according to the number of target data blocks. Specifically, based on the total number of target data blocks (e.g., M), a feasible range for the number of random position adjustments is set (e.g., [1, M / 2]) to avoid computational overload due to too many adjustments or insufficient security due to too few adjustments. For example, if there are 8 data blocks, the number of random position adjustments can be set to [2, 5], limiting the number of random position adjustments to between 2 and 5.

[0038] Next, within the space of random position adjustment times, a first position adjustment number is randomly generated. For example, a first position adjustment number of 4 is randomly generated within the space of random position adjustment times [2, 5].

[0039] Next, the ratios of the number of times the first position was adjusted to the reliability of the transmission node and the data with the same parameters are calculated to obtain the first reliability score and the first security score. Specifically: First Trust Score: First Trust Score = Number of First Position Adjustments / Trustworthiness of Transmission Node. The more times the first position is adjusted, the higher the First Trust Score. For example, if the trustworthiness of a node's transmission node is 3, then when the number of first position adjustments is 4, the First Trust Score = 4 / 3 = 1.3; when the number of first position adjustments is 5, the First Trust Score = 5 / 3 = 1.7.

[0040] First Security Score: First Security Score = Number of First Position Adjustments / Number of Data Same Parameters. The more times the first position is adjusted, the higher the First Security Score. For example, if the number of data same parameters for a node is 20, then when the number of first position adjustments is 4, the First Security Score = 4 / 20 = 0.2; when the number of first position adjustments is 5, the First Trust Score = 5 / 20 = 0.25.

[0041] Further, the first-time score is calculated, where the first-time score = transmission efficiency requirement parameter / first position adjustment time. Specifically, the time taken to adjust the data block position according to the number of first position adjustments is obtained as the first position adjustment time. Then, the ratio of the transmission efficiency requirement parameter to the first position adjustment time is calculated to obtain the first-time score. This is because the more times the first position is adjusted, the higher the first trust score and the first security score, but the longer the first position adjustment time. Therefore, the first-time score is needed to balance efficiency. For example, the time taken to adjust the data block position according to the number of first position adjustments is first obtained, for example, 4 adjustments take 80ms, and 5 adjustments take 90ms. This is used as the first position adjustment time. The transmission efficiency requirement parameter (such as the maximum allowed transmission time of 100ms) is used. Then, the first-time score for 4 adjustments = 100 / 80 = 1.25, and the first-time score for 5 adjustments = 100 / 90 = 1.1. Thus, the more adjustments, the lower the first-time score, and the first-time score can be used to ensure the efficiency of data transmission.

[0042] Furthermore, the first trust score, first security score, and first time score are weighted and calculated to obtain the first position adjustment score, where the first position adjustment score = w1 first trust score + w2 first security score + w3 first time score, and w1, w2, and w3 are preset weights for the first trust score, first security score, and first time score, respectively, for example, preset w1=40%, w2=30%, w3=30%. For example, if w1=40%, w2=30%, w3=30%, the first trust score is 0.94, the first security score is 0.16, and the first time score is 1.25, then the first position adjustment score = 40%*0.94 + 30%*0.16 + 30%*1.25 = 0.78. Thus, by weighting the first trust score, first security score, and first time score, both the trustworthiness and security of data transmission are ensured, while also taking into account the efficiency of data transmission.

[0043] Further, the number of random position adjustments is continuously generated for optimization, and the position adjustment score is calculated until convergence. The position adjustment number N with the highest position adjustment score is output, where N is a positive integer. Specifically, new position adjustment numbers are continuously generated randomly through iterative optimization, and the above score calculation process is repeated until convergence (e.g., the score change is less than a threshold after multiple consecutive iterations). Finally, the N with the highest score is selected as the optimal number of adjustments. For example, the number of random position adjustments is continuously generated randomly within the space [2, 5], and the corresponding position adjustment scores are calculated as follows: when the number of position adjustments is 2, the position adjustment score is 0.66; when the number of position adjustments is 3, the position adjustment score is 0.82; when the number of position adjustments is 5, the position adjustment score is 0.76; among them, when the number of position adjustments is 3, the position adjustment score is the highest, so the position adjustment number 3 is output.

[0044] Finally, according to the number of random position adjustments N, the basic data position information of multiple target data blocks is randomly selected and randomly adjusted to obtain N position adjustment strategies. Specifically, according to the number of random position adjustments N (e.g., 3), the basic position information of data blocks is randomly selected and rearranged. For example, the original order [D1, D2, D3, D4] becomes [D2, D1, D4, D3] after 3 adjustments, forming different combinations of data block orders. Furthermore, each position adjustment strategy corresponds to a reconstructed hash tree structure. By performing multiple rounds of verification on all reconstructed hash trees and comparing the consistency of multiple hash trees, the malicious rate is calculated. In this way, the accuracy of data integrity verification can be improved.

[0045] In summary, compared to existing technologies, this application constructs a basic hash tree within the transmission node based on the location information of multiple target data blocks and multiple basic data blocks. It then configures the number of random position adjustments (N) based on parameters such as transmission efficiency requirements, transmission node trustworthiness, and data similarity, generating N position adjustment strategies. By dynamically optimizing multiple parameters to determine the optimal number of adjustments (N) and generating N data block position adjustment strategies, it ensures both the trustworthiness and security of data transmission while also considering its efficiency. Furthermore, by binding the location information to the data content through the hash tree, any order adjustment or content tampering will result in a change to the root hash, effectively resisting malicious attacks.

[0046] S40: According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain N sets of adjusted data position information. Multiple adjustment hash trees are constructed by combining the multiple target data blocks, and data is transmitted. In the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated according to the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

[0047] In the process of verifying the integrity of trusted data transmission, the traditional single hash tree verification mechanism is difficult to cope with complex attacks in dynamic network environments, and the fixed verification strategy cannot dynamically adjust the verification strength according to the trustworthiness of nodes and data characteristics, resulting in resource waste in high-trust scenarios or insufficient verification in low-trust scenarios.

[0048] To address the aforementioned issues, this application employs an N-position adjustment strategy to randomly adjust the positions of multiple target data blocks, obtaining N sets of adjusted data position information. Multiple adjustment hash trees are then constructed using these multiple target data blocks, and data is transmitted. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N-position adjustment strategy. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated and used as the integrity verification result.

[0049] Specifically, step S40 in the method includes: According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain multiple sets of adjusted data position information. Based on the multiple target data blocks, and combining them with N sets of adjusted data position information, N adjusted hash trees are constructed using hash trees. The transmission node transmits the plurality of target data blocks, the basic hash tree, and N adjusted hash trees to the receiving node. Within the receiving node, a verification hash tree and an adjustment verification hash tree are generated based on the multiple target data blocks, multiple basic data location information, and N location adjustment strategies. The base hash tree and N adjusted hash trees are verified based on the verification hash tree and N adjusted verification hash trees. The percentage of inconsistent hash trees is used as the malfunction rate. Based on the malicious rate, the verification security rate is calculated. Combined with the trustworthiness of the transmission node, the verification score is calculated. It is then determined whether the score exceeds the verification score threshold to obtain a yes or no integrity verification result.

[0050] In this embodiment, the multiple target data blocks are first randomly positioned according to the N position adjustment strategies to obtain multiple sets of adjusted data position information, where each new set of adjusted data position information corresponds to a unique arrangement. For example, the original data block [D1, D2, D3, D4] is randomly positioned three times to obtain multiple sets of adjusted data position information: [D2, D1, D4, D3], [D3, D4, D1, D2], etc.

[0051] Secondly, based on the multiple target data blocks, and combining them with N sets of adjusted data position information, N adjusted hash trees are constructed using a hash tree. Specifically, based on each adjusted data position information set, an independent adjusted hash tree is generated according to the aforementioned hash tree construction method. The root hash value of each adjusted hash tree is unique, reflecting the data integrity under the positional arrangement.

[0052] Next, the multiple target data blocks, the basic hash tree, and the N adjusted hash trees are transmitted to the receiving node through the transmission node. Specifically, the sender transmits the multiple target data blocks, the basic hash tree, and the N adjusted hash trees together to the receiving node.

[0053] Furthermore, within the receiving node, a verification hash tree and N adjusted verification hash trees are generated based on the multiple target data blocks, multiple basic data location information, and N location adjustment strategies. Specifically, after receiving data, the receiver independently reconstructs the verification hash tree and N adjusted verification hash trees based on the same location adjustment strategies and data blocks, ensuring consistency with the sender's construction process.

[0054] Furthermore, the base hash tree and N adjusted hash trees are verified based on the receiving node and the verification hash tree and N adjusted verification hash trees. The percentage of inconsistent hash trees is obtained as the malicious rate. Specifically, the received hash tree is compared one by one with the locally reconstructed hash tree, and the percentage of inconsistent hash trees is calculated as the malicious rate, where malicious rate = number of inconsistent hash trees / (1 + N), where 1 is the base hash tree and N is the number of adjusted hash trees. For example, if the number of adjusted verification hash trees is 15 and the number of inconsistent hash trees is 4, then the malicious rate = 4 / (1 + 15) = 0.25.

[0055] Finally, based on the malicious rate, the verification security rate is calculated. Combined with the trustworthiness of the transmission node, a verification score is calculated. It is then determined whether the score exceeds a verification score threshold to obtain a yes or no integrity verification result. The verification score threshold is a value preset by those skilled in the art based on actual conditions, for example, 0.9. Wherein, verification security rate = 1 - malicious rate, verification score = α * verification security rate + (1 - α) * transmission node trustworthiness, and α is the weight (e.g., α is 0.7). For example, if α is 0.7, the malicious rate is 0.25, and the transmission node trustworthiness is 3, then the verification score = 0.7 * (1 - 0.25) + 0.3 * 3 = 1.425. Further, it is determined whether the verification score exceeds the verification score threshold. If it exceeds the threshold, the verification passes, and the integrity verification result "Yes" is output; otherwise, the integrity verification result "No" is output. For example, if the calculated verification score is 1.425 and the verification score threshold is 0.9, the verification passes, and the integrity verification result "Yes" is output.

[0056] In summary, compared to existing technologies, this application employs an N-position adjustment strategy to randomly adjust the positions of multiple target data blocks, obtaining N sets of adjusted data position information. Multiple adjustment hash trees are then constructed using these multiple target data blocks, and data is transmitted. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategy. Verification is then performed to obtain the malicious rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated as the integrity verification result. Thus, by using N randomly adjusted hash trees for data transmission integrity verification, and by combining the node's historical trustworthiness with the current N randomly adjusted hash trees for integrity verification, the accuracy of the verification is improved.

[0057] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application obtains the trustworthiness of transmission nodes by acquiring trusted transmission data records of transmission nodes over a historical period and performing node trustworthiness analysis. By comparing the historical transmission performance of a node with an average transmission benchmark, the trustworthiness of the node can be quantified, providing a reliable basis for subsequent data transmission integrity verification, and thereby identifying and constraining nodes with low trustworthiness during transmission.

[0058] Secondly, this application obtains the transmission efficiency requirements parameters for the current transmitting and receiving nodes to transmit multiple target data blocks, and analyzes the data similarity parameters of the multiple target data blocks. In this way, the efficiency requirements and data similarity parameters for data transmission are obtained, providing necessary data support for the subsequent optimization of the verification strategy.

[0059] Furthermore, within the transmission node, this application constructs a basic hash tree based on the location information of multiple target data blocks and multiple basic data blocks, obtaining a basic hash tree. Then, based on parameters such as transmission efficiency requirements, transmission node trustworthiness, and data similarity, it configures the number of random position adjustments, obtaining the number of random position adjustments N, and generating N position adjustment strategies. Thus, by dynamically optimizing multiple parameters to determine the optimal number of adjustments N and generating N data block position adjustment strategies, it ensures both the trustworthiness and security of data transmission while also considering data transmission efficiency. Moreover, by binding the location information to the data content through the hash tree, any order adjustment or content tampering will cause a change in the root hash, effectively resisting malicious attacks.

[0060] Finally, this application employs N position adjustment strategies to randomly adjust the positions of multiple target data blocks, obtaining N sets of adjusted data position information. These sets are then combined to construct multiple adjusted hash trees, which are then transmitted. Within the receiving node, a verification hash tree and multiple adjusted verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malicious rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated and used as the integrity verification result. Thus, by using N randomly adjusted hash trees for data transmission integrity verification, and by combining the node's historical trustworthiness with the current N randomly adjusted hash trees for integrity verification, the accuracy of the verification is improved.

[0061] Through the above technical solution, this application quantifies the trustworthiness of nodes based on historical data and verifies the integrity of data transmission by dynamically constructing a hash tree. In this way, the security of data transmission is improved when the same data exists in multiple data blocks.

[0062] Example 2, as Figure 2 As shown, based on the same inventive concept as the integrity verification processing method for trusted data transmission provided in Embodiment 1, this embodiment of the invention also provides an integrity verification processing system for trusted data transmission, comprising: The credibility analysis module 11 is used to obtain the reliable transmission data records of the transmission node in the historical time period, perform node credibility analysis of the transmission node, and obtain the credibility of the transmission node. The data acquisition module 12 is used to acquire the transmission efficiency requirement parameters for the current transmission node and receiving node to transmit multiple target data blocks, and to analyze the data common parameters of the multiple target data blocks; The hash tree construction module 13 is used to construct a hash tree within the transmission node based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree, and to configure the number of random position adjustments based on the transmission efficiency requirement parameters, transmission node credibility and data sameness parameters to obtain the number of random position adjustments N, and to generate N position adjustment strategies, where N is a positive integer. The optimized output module 14 is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies to obtain N sets of adjusted data position information, construct multiple adjustment hash trees by combining the multiple target data blocks, and transmit the data. In the receiving node, according to the multiple target data blocks and the N position adjustment strategies, a verification hash tree and multiple adjustment verification hash trees are generated, verified, and the malicious rate is obtained. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

[0063] The credibility analysis module 11 is specifically used for: Obtain trusted transmission data records of transmission nodes within a historical time period, wherein the trusted transmission data records include the integrity verification results of trusted data transmission within the historical time period; Extract the proportion of integrity verification results that are negative within the trusted transmission data record to obtain the historical integrity verification coefficient; Obtain the average complete verification coefficient for trusted data transmission across multiple nodes; The ratio of the average complete verification coefficient to the historical complete verification coefficient is calculated as the reliability of the transmission node.

[0064] Specifically, the data acquisition module 12 is used for: Obtain the transmission efficiency requirement parameters for the current transmission node and receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time; Collect data feature information of the multiple target data blocks, and analyze and obtain the common parameters of the multiple target data blocks based on the data feature information.

[0065] Furthermore, the step of "analyzing and obtaining the same data parameters of the multiple target data blocks based on the data feature information" includes: Based on the data feature information, in the transmitted data within a historical time period, a set of multiple historical data blocks with the same data feature information is indexed; Extract the number of identical data blocks in each of the multiple historical data block sets, calculate the average, and obtain the data identical parameters.

[0066] Specifically, the hash tree construction module 13 is used for: Within the transmission node, acquire multiple basic data location information of the multiple target data blocks; Based on the multiple target data blocks and multiple basic data location information, multiple first-level basic hash values ​​are generated; Based on each pair of adjacent first-level basic hash values ​​within the plurality of first-level basic hash values, a second-level basic hash value is generated, and this process continues until the root basic hash tree is obtained, thus obtaining the basic hash tree.

[0067] Furthermore, the step of "configuring the number of random position adjustments based on the transmission efficiency requirement parameter, the reliability of the transmission node, and the data similarity parameter, obtaining the number of random position adjustments N, and generating N position adjustment strategies" includes: Based on the number of the multiple target data blocks, set the space for random position adjustment; Within the space of random position adjustment times, a first number of position adjustments is randomly generated; Calculate the ratio of the number of times the first position was adjusted to the reliability of the transmission node and the same parameter of the data, respectively, to obtain the first reliability score and the first security score; Obtain the time for adjusting the data block position according to the first position adjustment number, and use it as the first position adjustment time; Calculate the ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain the first time score; The first credibility score, the first security score, and the first time score are weighted and calculated to obtain the first position adjustment score; Continue to randomly generate the number of position adjustments for optimization, and calculate the position adjustment score until convergence. Output the number of random position adjustments N with the highest position adjustment score, where N is a positive integer. Based on the number of random position adjustments N, the basic data position information of multiple target data blocks is randomly selected and adjusted to obtain N position adjustment strategies.

[0068] The optimized output module 14 is specifically used for: According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain multiple sets of adjusted data position information. Based on the multiple target data blocks, and combining them with N sets of adjusted data position information, N adjusted hash trees are constructed using hash trees. The transmission node transmits the plurality of target data blocks, the basic hash tree, and N adjusted hash trees to the receiving node. Within the receiving node, a verification hash tree and an adjustment verification hash tree are generated based on the multiple target data blocks, multiple basic data location information, and N location adjustment strategies. The base hash tree and N adjusted hash trees are verified based on the verification hash tree and N adjusted verification hash trees. The percentage of inconsistent hash trees is used as the malfunction rate. Based on the malicious rate, the verification security rate is calculated. Combined with the trustworthiness of the transmission node, the verification score is calculated. It is then determined whether the score exceeds the verification score threshold to obtain a yes or no integrity verification result.

[0069] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application first uses a credibility analysis module to obtain trusted transmission data records of transmission nodes over a historical period, performing node credibility analysis to obtain and quantify the credibility of the nodes, providing a reliable basis for subsequent data transmission integrity verification. Secondly, through a data acquisition module, it obtains the transmission efficiency requirements parameters for the transmission of multiple target data blocks between the current transmission and receiving nodes, and analyzes the data similarity parameters of multiple target data blocks, obtaining the data transmission efficiency requirements and data similarity parameters, providing necessary data support for the optimization of subsequent verification strategies. Thirdly, through a hash tree construction module, a hash tree is constructed within the transmission node based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree. Then, based on the transmission efficiency requirements parameters, transmission node credibility, and data similarity parameters, the number of random position adjustments is configured to obtain the number of random position adjustments N, generating N position adjustment strategies. This ensures both the credibility and security of data transmission while also considering data transmission efficiency. Finally, by optimizing the output module, multiple target data blocks are randomly repositioned according to N position adjustment strategies, resulting in N sets of adjusted data position information. These sets are then combined with the multiple target data blocks to construct multiple adjustment hash trees, which are then transmitted. At the receiving node, based on the multiple target data blocks and the N position adjustment strategies, a verification hash tree and multiple adjustment verification hash trees are generated and verified to obtain the malicious rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated as the integrity verification result, thus improving the security of data transmission. This enhances the security of data transmission when multiple data blocks contain the same data.

[0070] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for integrity verification processing of trusted data transmission, characterized in that, The method includes: Acquire reliable transmission data records of the transmission node within a historical time period, perform node reliability analysis on the transmission node, and obtain the reliability of the transmission node. Obtain the transmission efficiency requirements parameters for transmitting multiple target data blocks at the current transmission node and receiving node, and analyze the common parameters of the multiple target data blocks; Within the transmission node, a hash tree is constructed based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree. The number of times the location is randomly adjusted is configured according to the transmission efficiency requirement parameter, the trustworthiness of the transmission node, and the data sameness parameter to obtain the number of times the location is randomly adjusted N. N location adjustment strategies are generated, where N is a positive integer. According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain N sets of adjusted data position information. Multiple adjustment hash trees are constructed by combining the multiple target data blocks, and data is transmitted. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

2. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that, Obtain trusted transmission data records of the transmission nodes within a historical time period, perform node trustworthiness analysis on the transmission nodes, and obtain the trustworthiness of the transmission nodes, including: Obtain trusted transmission data records of transmission nodes within a historical time period, wherein the trusted transmission data records include the integrity verification results of trusted data transmission within the historical time period; Extract the proportion of integrity verification results that are negative within the trusted transmission data record to obtain the historical integrity verification coefficient; Obtain the average complete verification coefficient for trusted data transmission across multiple nodes; The ratio of the average complete verification coefficient to the historical complete verification coefficient is calculated as the reliability of the transmission node.

3. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that, Obtain the transmission efficiency requirements parameters for transmitting multiple target data blocks at the current transmitting and receiving nodes, and analyze the common parameters of the multiple target data blocks, including: Obtain the transmission efficiency requirement parameters for the current transmission node and receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time; Collect data feature information of the multiple target data blocks, and analyze and obtain the common parameters of the multiple target data blocks based on the data feature information.

4. The integrity verification processing method for trusted data transmission according to claim 3, characterized in that, Based on the data feature information, analyze and obtain the common parameters of the multiple target data blocks, including: Based on the data feature information, in the transmitted data within a historical time period, a set of multiple historical data blocks with the same data feature information is indexed; Extract the number of identical data blocks in each of the multiple historical data block sets, calculate the average, and obtain the data identical parameters.

5. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that, Within the transmission node, a hash tree is constructed based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree, including: Within the transmission node, acquire multiple basic data location information of the multiple target data blocks; Based on the multiple target data blocks and multiple basic data location information, multiple first-level basic hash values ​​are generated; Based on each pair of adjacent first-level basic hash values ​​within the plurality of first-level basic hash values, a second-level basic hash value is generated, and this process continues until the root basic hash tree is obtained, thus obtaining the basic hash tree.

6. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that, Based on the transmission efficiency requirement parameters, transmission node reliability, and data similarity parameters, the number of random position adjustments is configured to obtain the number of random position adjustments N, and N position adjustment strategies are generated, including: Based on the number of the multiple target data blocks, set the space for random position adjustment; Within the space of random position adjustment times, a first number of position adjustments is randomly generated; Calculate the ratio of the number of times the first position was adjusted to the reliability of the transmission node and the same parameter of the data, respectively, to obtain the first reliability score and the first security score; Obtain the time for adjusting the data block position according to the first position adjustment number, and use it as the first position adjustment time; Calculate the ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain the first time score; The first credibility score, the first security score, and the first time score are weighted and calculated to obtain the first position adjustment score; Continue to randomly generate the number of position adjustments for optimization, and calculate the position adjustment score until convergence. Output the number of random position adjustments N with the highest position adjustment score, where N is a positive integer. Based on the number of random position adjustments N, the basic data position information of multiple target data blocks is randomly selected and adjusted to obtain N position adjustment strategies.

7. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that, According to the N position adjustment strategies, the multiple target data blocks are randomly positioned to obtain N sets of adjusted data position information. Multiple adjustment hash trees are constructed by combining these sets with the multiple target data blocks, and data is transmitted. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malicious rate. Combined with the trustworthiness of the transmitting node, a verification score is calculated as the integrity verification result, including: According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain multiple sets of adjusted data position information. Based on the multiple target data blocks, and combining them with N sets of adjusted data position information, N adjusted hash trees are constructed using hash trees. The transmission node transmits the plurality of target data blocks, the basic hash tree, and N adjusted hash trees to the receiving node. Within the receiving node, a verification hash tree and an adjustment verification hash tree are generated based on the multiple target data blocks, multiple basic data location information, and N location adjustment strategies. The base hash tree and N adjusted hash trees are verified based on the verification hash tree and N adjusted verification hash trees. The percentage of inconsistent hash trees is used as the malfunction rate. Based on the malicious rate, the verification security rate is calculated. Combined with the trustworthiness of the transmission node, the verification score is calculated. It is then determined whether the score exceeds the verification score threshold to obtain a yes or no integrity verification result.

8. An integrity verification processing system for trusted data transmission, characterized in that, For performing the method according to any one of claims 1-7, comprising: The credibility analysis module is used to obtain the reliable transmission data records of the transmission node in the historical time period, perform node credibility analysis of the transmission node, and obtain the credibility of the transmission node. The data acquisition module is used to obtain the transmission efficiency requirements parameters for the transmission of multiple target data blocks by the current transmission node and receiving node, and to analyze the common parameters of the multiple target data blocks. The hash tree construction module is used to construct a hash tree within the transmission node based on the multiple target data blocks and multiple basic data location information to obtain a basic hash tree. It also configures the number of random position adjustments based on the transmission efficiency requirement parameters, transmission node credibility, and data sameness parameters to obtain the number of random position adjustments N, and generates N position adjustment strategies, where N is a positive integer. The optimized output module is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies to obtain N sets of adjusted data position information. It then constructs multiple adjustment hash trees by combining the multiple target data blocks and transmits the data. Within the receiving node, a verification hash tree and multiple adjustment verification hash trees are generated based on the multiple target data blocks and the N position adjustment strategies. Verification is performed to obtain the malfunction rate. Combined with the trustworthiness of the transmission node, a verification score is calculated as the integrity verification result.

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