Integrity verification processing method and system for trusted data transmission

Abstract

The invention provides an integrity verification processing method and system for credible data transmission, and the method comprises the steps: obtaining a credible transmission data record of a transmission node, and carrying out the credibility analysis; acquiring a transmission efficiency requirement parameter and a data same parameter of the current transmission node and the receiving node for transmitting the plurality of target data blocks; in the transmission node, performing hash tree construction according to the plurality of target data blocks and the plurality of pieces of basic data position information, performing position random adjustment frequency configuration, and obtaining N position adjustment strategies; and according to N position adjustment strategies, performing random position adjustment on the plurality of target data blocks to obtain N adjustment data position information sets, calculating to obtain a malicious rate according to the verification hash tree and the plurality of adjustment verification hash trees, and calculating to obtain a verification score in combination with the credibility of the transmission node to serve as an integrity verification result. The technical problem that in the prior art, when the same data exists in the multiple data blocks, the data transmission safety is not high is solved.

Description

Technical Field

[0001] The present invention relates to the field of data transmission, and in particular to a method and system for integrity verification processing for trusted data transmission. Background Art

[0002] In existing technologies, data transmission verification involves generating a hash tree based on multiple data blocks before transmission. These blocks are then transmitted to the receiving node. Upon receiving the blocks, the receiving node generates a hash tree and verifies whether the hash trees are consistent. If they are, the output data integrity verification succeeds. If they are inconsistent, there is a risk of data tampering, and the output data integrity verification fails. However, if multiple data blocks contain identical data, a second preimage attack may occur, compromising data transmission security. Summary of the Invention

[0003] The present invention addresses the technical problem in the prior art that data transmission security is low when identical data exists in multiple data blocks, 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 technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for integrity verification processing for trusted data transmission, comprising:

[0006] Obtaining the trusted transmission data records of the transmission node in the historical time, performing node credibility analysis on the transmission node, and obtaining the credibility of the transmission node;

[0007] Obtaining transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, and analyzing data identical parameters of the multiple target data blocks;

[0008] In the transmission node, a hash tree is constructed based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, and a number of random position adjustments is configured based on the transmission efficiency requirement parameter, the transmission node credibility, and the data same parameter to obtain a number of random position adjustments N, and N position adjustment strategies are generated, where N is a positive integer;

[0009] According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain N adjusted data position information sets. Multiple adjustment hash trees are constructed in combination with the multiple target data blocks, and data transmission is performed. In the receiving node, verification hash trees 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 a malicious rate. Combined with the credibility of the transmission node, a verification score is calculated as the integrity verification result.

[0010] In a second aspect, the present invention provides an integrity verification processing system for trusted data transmission, comprising:

[0011] A credibility analysis module is used to obtain the trusted transmission data records of the transmission node in the historical time, perform node credibility analysis on the transmission node, and obtain the credibility of the transmission node;

[0012] A data acquisition module, configured to obtain transmission efficiency requirement parameters for the current transmitting node and the receiving node in transmitting multiple target data blocks, and analyze data identical parameters of the multiple target data blocks;

[0013] a hash tree construction module, configured to construct a hash tree in the transmission node based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, configure a number of random position adjustments based on the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter to obtain a number of random position adjustments N, and generate N position adjustment strategies, where N is a positive integer;

[0014] The optimization output module is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies, obtain N adjusted data position information sets, construct multiple adjusted hash trees in combination with the multiple target data blocks, and perform data transmission. In 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, and verification is performed to obtain a malicious rate. In combination with the credibility of the transmission node, a verification score is calculated as the integrity verification result.

[0015] The beneficial effects of the present invention are:

[0016] Compared with the existing technology, the present application first obtains the trusted transmission data record of the transmission node in the historical time, performs node credibility analysis of the transmission node, obtains the credibility of the transmission node, quantifies the credibility of the node, and provides a reliable basis for the integrity verification of subsequent data transmission. Secondly, the transmission efficiency requirement parameters for the current transmission node and the receiving node to transmit multiple target data blocks are obtained, and the data identical parameters of the multiple target data blocks are analyzed to obtain the efficiency requirements and data identical parameters of the data transmission, providing the necessary data support for the optimization of the subsequent verification strategy. Thirdly, within the transmission node, a hash tree is constructed based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree, and the number of random position adjustments is configured based on the transmission efficiency requirement parameters, the transmission node credibility and the data identical parameters to obtain the number of random position adjustments N, and generate N position adjustment strategies, which not only ensures the credibility and security of data transmission, but also takes into account the efficiency of data transmission. Finally, according to N position adjustment strategies, multiple target data blocks are randomly adjusted to obtain N adjusted data position information sets. Multiple adjusted hash trees are constructed in combination with multiple target data blocks, and data transmission is performed. In the receiving node, verification hash trees and multiple adjusted verification hash trees are generated according to multiple target data blocks and N position adjustment strategies. Verification is performed to obtain the malicious rate. Combined with the credibility of the transmission node, the verification score is calculated as the integrity verification result, which improves the security of data transmission.

[0017] Through the above technical solution, the present application quantifies the credibility 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for integrity verification of trusted data transmission provided by the present invention;

[0019] Figure 2 This is a structural diagram of a system for integrity verification and processing of trusted data transmission provided by the present invention.

[0020] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0021] Credibility analysis module 11, data collection module 12, hash tree construction module 13, optimization output module 14. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0024] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0025] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a method for integrity verification processing for trusted data transmission, including:

[0026] S10: Obtaining the trusted transmission data records of the transmission node in the historical period, performing node credibility analysis on the transmission node, and obtaining the credibility of the transmission node;

[0027] In a dynamically changing network environment, the reliability of node data transmission may change over time. Traditional static trust models cannot quantitatively evaluate 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.

[0028] To address the above issues, this application obtains the trusted transmission data records of the transmission node in the historical time, performs node credibility analysis on the transmission node, obtains the transmission node credibility, and thereby evaluates the transmission credibility of the node.

[0029] Specifically, step S10 in the method includes:

[0030] Obtaining a trusted transmission data record of the transmission node within a historical period, wherein the trusted transmission data record includes an integrity verification result of the trusted data transmission within the historical period;

[0031] Extracting the proportion of negative integrity verification results in the trusted transmission data record to obtain a historical integrity verification coefficient;

[0032] Obtain the average complete verification coefficient of trusted data transmission from multiple nodes;

[0033] The ratio of the historical complete verification coefficient to the average complete verification coefficient is calculated as the transmission node credibility.

[0034] In an embodiment of the present application, the trusted transmission data record of the transmission node in the historical time is first obtained, wherein the trusted transmission data record includes the integrity verification result of the trusted data transmission in the historical time, and the integrity verification result includes "yes" or "no". "Yes" indicates that the integrity verification is successful, and "no" indicates that the integrity verification is unsuccessful.

[0035] Next, the percentage of integrity verification results that are negative within the trusted transmission data records is extracted to obtain the historical integrity verification coefficient. The historical integrity verification coefficient is calculated as: number of integrity verification results that are negative / 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 integrity verification results that are negative is 50, then the historical integrity verification coefficient = 50 / 1000 = 0.05.

[0036] Next, obtain the average complete verification coefficient of trusted data transmissions from multiple nodes. The average complete verification coefficient is calculated as follows: the total number of times the multiple nodes' verification results were negative / the total number of data transmissions from the multiple nodes. The average complete verification coefficient reflects the average failure rate of multiple nodes and can be used as a benchmark for assessing node trustworthiness. For example, if the total number of data transmissions from 30 nodes is 20,000, and the total number of times the verification results were negative is 3,000, then the average complete verification coefficient = 3,000 / 20,000 = 0.15.

[0037] Finally, the ratio of the historical integrity verification coefficient to the average integrity verification coefficient is calculated as the transmission node credibility, where transmission node credibility = average integrity verification coefficient / historical integrity verification coefficient. A higher transmission node credibility reflects a lower historical transmission failure rate for the node, i.e., a more reliable node. For example, if the average integrity verification coefficient is 0.15, the historical integrity verification coefficient of node 1 is 0.05, and the historical integrity verification coefficient of node 2 is 0.04, then the transmission node credibility of node 1 = 0.15 / 0.05 = 3, and the transmission node credibility of node 2 = 0.15 / 0.04 = 3.75. Node 2 has a lower historical transmission failure rate, and the calculated transmission node credibility is higher, indicating that node 2 has higher transmission reliability than node 1.

[0038] In summary, compared to existing technologies, this application obtains the node credibility by acquiring the trusted transmission data records of the transmission node over a period of time, performing node credibility analysis on the transmission node, and obtaining the node credibility. By comparing the node's historical transmission performance with the average transmission benchmark, the node credibility can be quantified, providing a reliable basis for subsequent data transmission integrity verification, and thus identifying and constraining nodes with low credibility during the transmission process.

[0039] S20: Obtain transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, and analyze data identical parameters of the multiple target data blocks;

[0040] Fixed verification strategies cannot adaptively adjust verification strategies according to actual conditions, resulting in an imbalance between security and efficiency. For example, in scenarios with high real-time requirements (such as industrial control data transmission), delays are caused due to excessive verification time, or unnecessary resource waste occurs in scenarios with high data redundancy (such as repeated file transfers).

[0041] In response to the above problem, the present application obtains transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, and analyzes data identical parameters of the multiple target data blocks.

[0042] Specifically, step S20 in the method includes:

[0043] Acquire transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time;

[0044] Data feature information of the plurality of target data blocks is collected, and data identical parameters of the plurality of target data blocks are analyzed and acquired based on the data feature information.

[0045] In this embodiment of the present application, the transmission efficiency requirement parameters for the transmission of multiple target data blocks between the current transmitting node and the receiving node are first obtained. The transmission efficiency requirement parameters include the transmission time (such as the maximum allowable transmission time). The transmission time limits the computational overhead and the number of data exchanges during the verification process. For example, the transmission time must be controlled within a threshold (such as 100ms). By constraining the transmission time, excessive transmission delays caused by excessive verification can be avoided.

[0046] 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, data identity parameters for these multiple target data blocks are analyzed and obtained. Specifically, multiple historical data block sets with identical data feature information are indexed within the transmission data within the historical time period. The number of identical data blocks appearing within each of these historical data block sets is then extracted and the average is calculated to obtain the data identity parameter. Furthermore, the data identity parameter reflects the commonality of data blocks in historical transmissions. The larger the data identity parameter, the higher the data redundancy and the lower the likelihood that an attacker will exploit repeated patterns to forge data.

[0047] Furthermore, the step of “analyzing and obtaining the data common parameters of the plurality of target data blocks according to the data feature information” includes:

[0048] According to the data characteristic information, indexing multiple historical data block sets having the same data characteristic information in the transmitted data within the historical time;

[0049] The number of identical data blocks in the plurality of historical data block sets is extracted respectively, and the mean is calculated to obtain the data identical parameter.

[0050] In the embodiment of the present application, first, based on data feature information (such as data type, size, hash value, etc.), multiple historical data block sets with the same data feature information are indexed in the transmitted data within the historical time period. The historical data block sets reflect the transmission records with similar features to the current data during the historical transmission process. For example, based on the data feature information (JPG, 2MB), multiple historical data block sets of JPG and 2MB in the historical transmission data are retrieved.

[0051] Next, the number of identical data blocks within each set of historical data blocks is extracted and the mean is calculated to obtain a data identity parameter. For example, the number of data blocks identical to the currently transmitted data block features (JPG and 2MB) extracted from the historical records is 15 and 25, respectively. The mean is calculated as 20, which serves as the data identity parameter. This data identity parameter reflects the probability of data block duplication. A higher value for the data identity parameter indicates a higher frequency of recurrence of this feature data in historical transmissions, indicating greater data redundancy.

[0052] In summary, compared to existing technologies, this application obtains transmission efficiency requirement parameters for multiple target data blocks transmitted by the current transmitting and receiving nodes, and analyzes the data identity parameters of these multiple target data blocks. This provides the necessary data support for optimizing subsequent verification strategies by obtaining the required data transmission efficiency and data identity parameters.

[0053] S30: In the transmission node, a hash tree is constructed based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, and a number of random position adjustments is configured based on the transmission efficiency requirement parameter, the transmission node credibility, and the data sameness parameter to obtain a number of random position adjustments N, and N position adjustment strategies are generated, where N is a positive integer;

[0054] In scenarios where data is transmitted in blocks, a traditional single hash value is difficult to verify the integrity and order of multiple data blocks and is vulnerable to second preimage attacks. Therefore, integrity verification requires a layered verification structure that can bind data content and location. Furthermore, simply adding location information to generate a new hash value may still result in insufficient verification. Verification requires randomly adjusting the positions of data blocks to generate more hash trees. The more times the position is randomly adjusted, the more hash trees are generated, and the more accurate the verification. However, this results in longer verification times and lower verification efficiency.

[0055] In response to the above problems, the present application constructs a hash tree within the transmission node based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree, and configures the number of random position adjustments based on the transmission efficiency requirement parameters, transmission node credibility and data identity parameters to obtain the number of random position adjustments N and generate N position adjustment strategies.

[0056] Specifically, step S30 in the method includes:

[0057] In the transmission node, obtaining a plurality of basic data location information of the plurality of target data blocks;

[0058] generating a plurality of first-layer basic hash values ​​according to the plurality of target data blocks and the plurality of basic data location information;

[0059] A second-level basic hash value is generated according to every two adjacent first-level basic hash values ​​in the plurality of first-level basic hash values, and the generation is continued until a root basic hash tree is obtained, thereby obtaining a basic hash tree.

[0060] In the embodiment of the present application, first, within the transmission node, multiple basic data location information of multiple target data blocks is obtained. Specifically, the basic data location information reflects the index or arrangement order of the data blocks in the original transmission sequence. For example, the data blocks are arranged as Block 1, Block 2, Block 3 in the transmission order, and 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 factor for the subsequent generation of hash values. It ensures the binding relationship between data content and location, and prevents attackers from circumventing verification by rearranging data blocks.

[0061] Secondly, based on multiple target data blocks and multiple basic data location information, multiple first-layer basic hash values ​​are generated. Specifically, for each data block, its content is concatenated with the corresponding location information and then a hash operation is performed (such as using the SHA-256 algorithm) to obtain the underlying hash value. For example, the content of Block1 is "Data" and the location information is "0". After the SHA-256 algorithm is calculated, the first-layer hash value obtained is Hash("Data" + "0"). In this way, it is ensured that the hash value of each data block contains both content information and location information, forming a basic verification unit for binding data and location.

[0062] Finally, based on every two adjacent first-layer basic hash values ​​within the multiple first-layer basic hash values, a second-layer basic hash value is generated, and the generation continues until a root basic hash tree is obtained, thereby obtaining a basic hash tree. Specifically, every two adjacent hash values ​​within the first-layer basic hash value are concatenated and hashed again to generate a second-layer basic hash value, which is then repeatedly concatenated and generated layer by layer until a unique root basic hash value is generated, thereby obtaining a basic hash tree. For example, if the first-layer hash values ​​are H1, H2, H3, and H4, then the second-layer hash values ​​are Hash(H1+H2) and Hash(H3+H4), and the third-layer root hash value is Hash(second-layer hash value 1+second-layer hash value 2). In this way, a unique root basic hash value is generated, thereby obtaining a basic hash tree. Furthermore, the upward-layer hash tree structure ensures that any tampering or position change of any data block will cause the hash value on the entire path from that location to the root hash to change, so that the receiver only needs to compare the root hash value to quickly determine whether the data set has been tampered with, 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.

[0063] Furthermore, the “configuring the number of random position adjustments according to the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter, obtaining the number of random position adjustments N, and generating N position adjustment strategies” includes:

[0064] According to the number of the plurality of target data blocks, setting a space for random position adjustment times;

[0065] Randomly generating a first position adjustment number within the random position adjustment number space;

[0066] Calculating the ratio of the first position adjustment number to the transmission node credibility and the data identical parameter to obtain a first credibility score and a first security score;

[0067] Acquire a time for performing position adjustment of the data block according to the first position adjustment number as a first position adjustment time;

[0068] Calculating a ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain a first time score;

[0069] Performing weighted calculation on the first credibility score, the first security score, and the first time score to obtain a first position adjustment score;

[0070] Continue to randomly generate the number of position adjustments for optimization and calculate the position adjustment score until convergence, and output the number of random position adjustments N with the largest position adjustment score, where N is a positive integer;

[0071] According to the number of random position adjustments N, basic data position information of multiple target data blocks is randomly selected and randomly adjusted to obtain N position adjustment strategies.

[0072] In an embodiment of the present application, first, a space for the number of random position adjustments is set based on the number of target data blocks. Specifically, based on the total number of target data blocks (e.g., M), a feasible range of the number of random position adjustments is set (e.g., set to [1, M / 2]) to avoid excessive computational overload or insufficient security due to too few adjustments. For example, if there are 8 data blocks, the space for the number of random position adjustments can be set to [2, 5], limiting the number of random position adjustments to between 2 and 5.

[0073] Next, a first position adjustment number is randomly generated within the position random adjustment number space. For example, a first position adjustment number 4 is randomly generated within the position random adjustment number space [2, 5].

[0074] Next, the ratio of the first position adjustment times to the transmission node credibility and the data identical parameter is calculated to obtain a first credibility score and a first security score. Specifically:

[0075] First Trust Score: First Trust Score = Number of First Position Adjustments / Transmitting Node Trust. The greater the number of first position adjustments, the greater the first trust score. For example, if a node's transmitting node trust is 3 and the number of first position adjustments is 4, the first trust score is 4 / 3 = 1.3. If the number of first position adjustments is 5, the first trust score is 5 / 3 = 1.7.

[0076] First Security Score: First Security Score = Number of First Position Adjustments / Data Identification Parameter. The greater the number of first position adjustments, the greater the first security score. For example, if the node's data identification parameter is 20 and the number of first position adjustments is 4, the first security score is 4 / 20 = 0.2. If the number of first position adjustments is 5, the first trust score is 5 / 20 = 0.25.

[0077] Furthermore, a first time score is calculated, where the first time score = transmission efficiency requirement parameter / first repositioning time. Specifically, the time required to reposition the data block according to the first number of repositioning adjustments is obtained as the first repositioning time. The ratio of the transmission efficiency requirement parameter to the first repositioning time is then calculated to obtain the first time score. This is because a greater number of first repositioning adjustments results in higher first credibility scores and first security scores, but also in a longer first repositioning time. Therefore, the first time score is needed to balance efficiency. For example, the time required to reposition the data block according to the first number of repositioning adjustments is first obtained. For example, four repositioning adjustments take 80ms, while five repositioning adjustments take 90ms. This is used as the first repositioning time. The transmission efficiency requirement parameter (e.g., a maximum allowable transmission time of 100ms) is used. For four repositioning adjustments, the first time score is 100 / 80 = 1.25, and for five repositioning adjustments, the first time score is 100 / 90 = 1.1. Thus, a greater number of repositioning adjustments results in a lower first time score, and the first time score can be used to ensure data transmission efficiency.

[0078] Furthermore, a weighted calculation is performed on the first trust score, the first security score, and the first time score to obtain a first position adjustment score, where the first position adjustment score = w1 (first trust score) + w2 (first security score) + w3 (first time score), where w1, w2, and w3 are preset weights of the first trust score, first security score, and first time score, for example, w1 = 40%, w2 = 30%, and w3 = 30%. For example, if w1 = 40%, w2 = 30%, and w3 = 30%, and 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. In this way, by weighting the first trust score, first security score, and first time score, the credibility and security of data transmission are guaranteed while also taking into account the efficiency of data transmission.

[0079] Furthermore, the number of position adjustments is continuously randomly generated for optimization, and the position adjustment scores are calculated until convergence, and the number of random position adjustments N with the largest position adjustment score is output, where N is a positive integer. Specifically, new position adjustments are continuously randomly generated through iterative optimization, and the above score calculation process is repeated until convergence (such as the score change for multiple consecutive iterations is less than the threshold), and finally the highest score N is selected as the optimal adjustment number. Exemplarily, the number of position adjustments is continuously randomly generated in the space of random position adjustments [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 largest, and the number of random position adjustments 3 is output.

[0080] Finally, according to the number of random position adjustments N, the basic data position information of multiple target data blocks is randomly selected for random adjustment to obtain N position adjustment strategies. Specifically, according to the number of random position adjustments N (such as 3), the basic position information of the data blocks is randomly selected for rearrangement. For example, the original order is [D1, D2, D3, D4], which becomes [D2, D1, D4, D3] after 3 adjustments, forming different data block sequence combinations. 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 to calculate the malicious rate, the accuracy of data integrity verification can be improved.

[0081] In summary, compared to the prior art, the present application constructs a hash tree within the transmission node based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree. The application also configures the number of random position adjustments based on the transmission efficiency requirement parameters, the transmission node credibility, and the data identity parameters to obtain the number of random position adjustments N and generate N position adjustment strategies. In this way, the optimal number of adjustments N is determined through multi-parameter dynamic optimization, and N data block position adjustment strategies are generated, which not only ensures the credibility and security of data transmission, but also takes into account the efficiency of data transmission. Furthermore, the hash tree binds the location information to the data content, and any order adjustment or content tampering will cause the root hash to change, effectively resisting malicious attacks.

[0082] S40: According to the N position adjustment strategies, the multiple target data blocks are randomly adjusted to obtain N adjusted data position information sets, multiple adjustment hash trees are constructed in combination with the multiple target data blocks, and data transmission is performed. In the receiving node, verification hash trees and multiple adjustment verification hash trees are generated according to the multiple target data blocks and the N position adjustment strategies, and verification is performed to obtain a malicious rate. In combination with the credibility of the transmission node, a verification score is calculated as the integrity verification result.

[0083] In the process of integrity verification 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 node credibility and data characteristics, resulting in resource waste in high-trust scenarios or insufficient verification in low-trust scenarios.

[0084] To address the above problems, the present application performs random position adjustments on multiple target data blocks according to N position adjustment strategies, obtains N adjusted data position information sets, constructs multiple adjusted hash trees in combination with multiple target data blocks, and performs data transmission. In the receiving node, verification hash trees and multiple adjusted verification hash trees are generated based on multiple target data blocks and N position adjustment strategies, and verification is performed to obtain the malicious rate. Combined with the credibility of the transmission node, the verification score is calculated as the integrity verification result.

[0085] Specifically, step S40 in the method includes:

[0086] According to the N position adjustment strategies, randomly adjust the positions of the multiple target data blocks to obtain multiple adjusted data position information sets;

[0087] According to the multiple target data blocks, respectively combining the N adjustment data position information sets, based on the hash tree, constructing N adjustment hash trees;

[0088] Transmitting the multiple target data blocks, the basic hash tree, and the N adjusted hash trees to the receiving node through the transmitting node;

[0089] In the receiving node, generating a verification hash tree and N adjusted verification hash trees according to the multiple target data blocks, the multiple basic data location information, and the N location adjustment strategies;

[0090] Verify the basic hash tree and the N adjusted hash trees based on the verification hash tree and the N adjusted verification hash trees, and obtain a percentage of inconsistent hash trees as a malicious rate;

[0091] Based on the malicious rate, the verification security rate is calculated, and combined with the transmission node credibility, the verification score is calculated to obtain a judgment whether it is greater than the verification score threshold, and a yes or no integrity verification result is obtained.

[0092] In this embodiment of the present application, the multiple target data blocks are first randomly repositioned according to the N position adjustment strategies to obtain multiple adjusted data position information sets, where each new adjusted data position information set corresponds to a unique arrangement. For example, the original data blocks [D1, D2, D3, D4] are randomly repositioned three times to obtain multiple adjusted data position information sets: [D2, D1, D4, D3], [D3, D4, D1, D2], and so on.

[0093] Next, based on the multiple target data blocks, N adjusted data position information sets are combined and N adjusted hash trees are constructed based on 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 position arrangement.

[0094] Then, the transmitting node transmits the multiple target data blocks, the basic hash tree, and the N adjusted hash trees to the receiving node. Specifically, the sender transmits the multiple target data blocks, the basic hash tree, and the N adjusted hash trees to the receiving node.

[0095] Furthermore, within the receiving node, a verification hash tree and N adjusted verification hash trees are generated based on the multiple target data blocks, the multiple basic data location information, and the N location adjustment strategies. Specifically, after receiving the data, the receiver independently reconstructs the verification hash tree and N adjusted verification hash trees based on the same location adjustment strategy and data blocks to ensure consistency with the construction process of the sender.

[0096] Furthermore, the basic hash tree and N adjusted hash trees are verified based on the receiving node and the verification hash tree and the N adjusted verification hash trees, and the proportion of inconsistent hash trees is obtained as the malicious rate. Specifically, by comparing the received hash tree with the locally reconstructed hash tree one by one, the proportion of inconsistent hash trees is counted as the malicious rate, where malicious rate = inconsistent number / (1 + N), 1 is the basic 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.

[0097] Finally, based on the malicious rate, the verification safety rate is calculated. Combined with the transmission node credibility, a verification score is calculated. A determination is made as to whether the score is greater than a verification score threshold, resulting in a yes or no integrity verification result. The verification score threshold is a value pre-set by a person skilled in the art based on actual circumstances, such as 0.9. Here, verification safety rate = 1 - malicious rate, verification score = α * verification safety rate + (1 - α) * transmission node credibility, where α is a weight (for example, α is 0.7). For example, if α is 0.7, the malicious rate is 0.25, and the transmission node credibility is 3, then the verification score = 0.7 * 0.25 + 0.3 * 3 = 1.01. Furthermore, a determination is made as to whether the verification score is greater than the verification score threshold. If so, verification passes, and a "yes" integrity verification result is output; otherwise, a "no" integrity verification result is output. For example, if the calculated verification score is 1.01 and the verification score threshold is 0.9, verification passes, and a "yes" integrity verification result is output.

[0098] In summary, compared to the prior art, this application randomly adjusts the positions of multiple target data blocks according to N position adjustment strategies, obtains N adjusted data position information sets, constructs multiple adjusted hash trees based on multiple target data blocks, and performs data transmission. In the receiving node, based on multiple target data blocks and N position adjustment strategies, a verification hash tree and multiple adjusted verification hash trees are generated for verification, and the malicious rate is obtained. Combined with the credibility of the transmission node, a verification score is calculated as the integrity verification result. In this way, the integrity of data transmission is verified by N randomly adjusted hash trees, and the integrity is verified by the node's historical credibility and the current N randomly adjusted hash trees, thereby improving the accuracy of verification.

[0099] In summary, the embodiments of the present application have at least the following technical effects:

[0100] Compared to existing technologies, this application analyzes the node's credibility by acquiring historical records of trusted transmission data from transmission nodes. This allows for node credibility analysis, comparing the node's historical transmission performance with the average transmission benchmark. This quantifies the node's credibility, providing a reliable basis for verifying the integrity of subsequent data transmissions and, based on this, identifying and constraining nodes with low credibility during the transmission process.

[0101] Secondly, the present application obtains the transmission efficiency requirement parameters for the current transmitting node and the receiving node when transmitting multiple target data blocks, and analyzes the data identity parameters of these multiple target data blocks. In this way, the data transmission efficiency requirements and data identity parameters are obtained, providing the necessary data support for the subsequent optimization of the verification strategy.

[0102] Again, within the transmission node, this application constructs a hash tree based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree. The application also configures the number of random position adjustments based on the transmission efficiency requirement parameters, the transmission node credibility, and the data identity parameters to obtain the number of random position adjustments N and generate N position adjustment strategies. In this way, the optimal number of adjustments N is determined through multi-parameter dynamic optimization, and N data block position adjustment strategies are generated. This ensures the credibility and security of data transmission while taking into account the efficiency of data transmission. Furthermore, the hash tree binds the location information to the data content, and any order adjustment or content tampering will cause the root hash to change, effectively resisting malicious attacks.

[0103] Finally, this application randomly adjusts the positions of multiple target data blocks according to N position adjustment strategies, obtains N adjusted data position information sets, constructs multiple adjusted hash trees based on multiple target data blocks, and performs data transmission. In the receiving node, based on multiple target data blocks and N position adjustment strategies, a verification hash tree and multiple adjusted verification hash trees are generated for verification, and the malicious rate is obtained. Combined with the credibility of the transmission node, a verification score is calculated as the integrity verification result. In this way, the integrity of data transmission is verified by N randomly adjusted hash trees, and the integrity is verified by the node's historical credibility and the current N randomly adjusted hash trees, thereby improving the accuracy of verification.

[0104] Through the above technical solution, the present application quantifies the credibility 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.

[0105] 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 the first embodiment, the embodiment of the present invention further provides an integrity verification processing system for trusted data transmission, including:

[0106] Credibility analysis module 11, used to obtain the trusted transmission data records of the transmission node in the historical time, perform node credibility analysis on the transmission node, and obtain the credibility of the transmission node;

[0107] The data acquisition module 12 is used to obtain transmission efficiency requirement parameters of the current transmitting node and the receiving node for transmitting multiple target data blocks, and analyze data identical parameters of the multiple target data blocks;

[0108] A hash tree construction module 13 is configured to construct a hash tree in the transmission node based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, configure the number of random position adjustments based on the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter to obtain the number of random position adjustments N, and generate N position adjustment strategies, where N is a positive integer;

[0109] The optimization output module 14 is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies, obtain N adjusted data position information sets, construct multiple adjusted hash trees in combination with the multiple target data blocks, and perform data transmission. In 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, and verification is performed to obtain a malicious rate. In combination with the credibility of the transmission node, a verification score is calculated as the integrity verification result.

[0110] The credibility analysis module 11 is specifically used for:

[0111] Obtaining a trusted transmission data record of the transmission node within a historical period, wherein the trusted transmission data record includes an integrity verification result of the trusted data transmission within the historical period;

[0112] Extracting the proportion of negative integrity verification results in the trusted transmission data record to obtain a historical integrity verification coefficient;

[0113] Obtain the average complete verification coefficient of trusted data transmission from multiple nodes;

[0114] The ratio of the historical complete verification coefficient to the average complete verification coefficient is calculated as the transmission node credibility.

[0115] The data acquisition module 12 is specifically used for:

[0116] Acquire transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time;

[0117] Data feature information of the plurality of target data blocks is collected, and data identical parameters of the plurality of target data blocks are analyzed and acquired based on the data feature information.

[0118] Furthermore, the step of “analyzing and obtaining the data common parameters of the plurality of target data blocks according to the data feature information” includes:

[0119] According to the data characteristic information, indexing multiple historical data block sets having the same data characteristic information in the transmitted data within the historical time;

[0120] The number of identical data blocks in the plurality of historical data block sets is extracted respectively, and the mean is calculated to obtain the data identical parameter.

[0121] The hash tree construction module 13 is specifically used to:

[0122] In the transmission node, obtaining a plurality of basic data location information of the plurality of target data blocks;

[0123] generating a plurality of first-layer basic hash values ​​according to the plurality of target data blocks and the plurality of basic data location information;

[0124] A second-level basic hash value is generated according to every two adjacent first-level basic hash values ​​in the plurality of first-level basic hash values, and the generation is continued until a root basic hash tree is obtained, thereby obtaining a basic hash tree.

[0125] Furthermore, the “configuring the number of random position adjustments according to the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter, obtaining the number of random position adjustments N, and generating N position adjustment strategies” includes:

[0126] According to the number of the plurality of target data blocks, setting a space for random position adjustment times;

[0127] Randomly generating a first position adjustment number within the random position adjustment number space;

[0128] Calculating the ratio of the first position adjustment number to the transmission node credibility and the data identical parameter to obtain a first credibility score and a first security score;

[0129] Acquire a time for performing position adjustment of the data block according to the first position adjustment number as a first position adjustment time;

[0130] Calculating a ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain a first time score;

[0131] Performing weighted calculation on the first credibility score, the first security score, and the first time score to obtain a first position adjustment score;

[0132] Continue to randomly generate the number of position adjustments for optimization and calculate the position adjustment score until convergence, and output the number of random position adjustments N with the largest position adjustment score, where N is a positive integer;

[0133] According to the number of random position adjustments N, basic data position information of multiple target data blocks is randomly selected and randomly adjusted to obtain N position adjustment strategies.

[0134] The optimization output module 14 is specifically configured to:

[0135] According to the N position adjustment strategies, randomly adjust the positions of the multiple target data blocks to obtain multiple adjusted data position information sets;

[0136] According to the multiple target data blocks, respectively combining the N adjustment data position information sets, based on the hash tree, constructing N adjustment hash trees;

[0137] Transmitting the multiple target data blocks, the basic hash tree, and the N adjusted hash trees to the receiving node through the transmitting node;

[0138] In the receiving node, generating a verification hash tree and N adjusted verification hash trees according to the multiple target data blocks, the multiple basic data location information, and the N location adjustment strategies;

[0139] Verify the basic hash tree and the N adjusted hash trees based on the verification hash tree and the N adjusted verification hash trees, and obtain a percentage of inconsistent hash trees as a malicious rate;

[0140] Based on the malicious rate, the verification security rate is calculated, and combined with the transmission node credibility, the verification score is calculated to obtain a judgment whether it is greater than the verification score threshold, and a yes or no integrity verification result is obtained.

[0141] In summary, the embodiments of the present application have at least the following technical effects:

[0142] Compared with the existing technology, this application first obtains the trusted transmission data records of the transmission node in the historical time through the credibility analysis module, performs node credibility analysis of the transmission node, obtains the credibility of the transmission node, quantifies the credibility of the node, and provides a reliable basis for the integrity verification of subsequent data transmission. Secondly, through the data acquisition module, the transmission efficiency requirement parameters for the current transmission node and the receiving node to transmit multiple target data blocks are obtained, and the data consistency parameters of the multiple target data blocks are analyzed to obtain the efficiency requirements and data consistency parameters of the data transmission, providing the necessary data support for the optimization of the subsequent verification strategy. Thirdly, through the hash tree construction module, within the transmission node, a hash tree is constructed based on multiple target data blocks and multiple basic data location information to obtain a basic hash tree, and the number of random position adjustments is configured based on the transmission efficiency requirement parameters, the transmission node credibility and the data consistency parameters to obtain the number of random position adjustments N, and generate N position adjustment strategies, which not only ensures the credibility and security of data transmission, but also takes into account the efficiency of data transmission. Finally, by optimizing the output module, the multiple target data blocks are randomly repositioned according to N position adjustment strategies, obtaining N adjusted data position information sets. Multiple adjusted hash trees are constructed based on the multiple target data blocks, and data is transmitted. At the receiving node, verification hash trees 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 a malicious rate. Combined with the transmission node's credibility, a verification score is calculated as the integrity verification result, improving the security of data transmission. This improves the security of data transmission when the same data exists in multiple data blocks.

[0143] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0144] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for integrity verification of trusted data transmission, characterized in that: The method comprises: Obtaining the trusted transmission data records of the transmission node in the historical time, performing node credibility analysis on the transmission node, and obtaining the credibility of the transmission node; Obtaining transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, and analyzing data identical parameters of the multiple target data blocks; In the transmission node, a hash tree is constructed based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, and a number of random position adjustments is configured based on the transmission efficiency requirement parameter, the transmission node credibility, and the data same parameter to obtain a number of random position adjustments N, and N position 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 adjusted data position information sets. Multiple adjustment hash trees are constructed in combination with the multiple target data blocks, and data transmission is performed. In the receiving node, verification hash trees 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 a malicious rate. Combined with the credibility 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: Acquiring a trusted transmission data record of a transmission node in a historical period, performing a node credibility analysis on the transmission node, and obtaining the credibility of the transmission node includes: Obtaining a trusted transmission data record of the transmission node within a historical period, wherein the trusted transmission data record includes an integrity verification result of the trusted data transmission within the historical period; Extracting the proportion of negative integrity verification results in the trusted transmission data record to obtain a historical integrity verification coefficient; Obtain the average complete verification coefficient of trusted data transmission from multiple nodes; The ratio of the historical complete verification coefficient to the average complete verification coefficient is calculated as the transmission node credibility.

3. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that: Obtaining transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, and analyzing data identical parameters of the multiple target data blocks, including: Acquire transmission efficiency requirement parameters for the current transmitting node and the receiving node to transmit multiple target data blocks, wherein the transmission efficiency requirement parameters include transmission time; Data feature information of the plurality of target data blocks is collected, and data identical parameters of the plurality of target data blocks are analyzed and acquired based on the data feature information.

4. The integrity verification processing method for trusted data transmission according to claim 3, characterized in that: Analyzing and obtaining data common parameters of the plurality of target data blocks according to the data feature information includes: According to the data characteristic information, indexing multiple historical data block sets having the same data characteristic information in the transmitted data within the historical time; The number of identical data blocks in the plurality of historical data block sets is extracted respectively, and the mean is calculated to obtain the data identical parameter.

5. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that: In the transmission node, a hash tree is constructed according to the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, including: In the transmission node, obtaining a plurality of basic data location information of the plurality of target data blocks; generating a plurality of first-layer basic hash values ​​according to the plurality of target data blocks and the plurality of basic data location information; A second-level basic hash value is generated according to every two adjacent first-level basic hash values ​​in the plurality of first-level basic hash values, and the generation is continued until a root basic hash tree is obtained, thereby obtaining a basic hash tree.

6. The integrity verification processing method for trusted data transmission according to claim 1, characterized in that: The number of random position adjustments is configured according to the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter to obtain the number of random position adjustments N, and generate N position adjustment strategies, including: According to the number of the plurality of target data blocks, setting a space for random position adjustment times; Randomly generating a first position adjustment number within the random position adjustment number space; Calculating the ratio of the first position adjustment number to the transmission node credibility and the data identical parameter to obtain a first credibility score and a first security score; Acquire a time for performing position adjustment of the data block according to the first position adjustment number as a first position adjustment time; Calculating a ratio of the transmission efficiency requirement parameter to the first position adjustment time to obtain a first time score; Performing weighted calculation on the first credibility score, the first security score, and the first time score to obtain a first position adjustment score; Continue to randomly generate the number of position adjustments for optimization and calculate the position adjustment score until convergence, and output the number of random position adjustments N with the largest position adjustment score, where N is a positive integer; According to the number of random position adjustments N, basic data position information of multiple target data blocks is randomly selected and randomly 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 adjusted to obtain N adjusted data position information sets. Multiple adjusted hash trees are constructed in combination with the multiple target data blocks, and data is transmitted. In the receiving node, verification hash trees 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 a malicious rate. Combined with the credibility of the transmission node, a verification score is calculated as the integrity verification result, including: According to the N position adjustment strategies, randomly adjust the positions of the multiple target data blocks to obtain multiple adjusted data position information sets; According to the multiple target data blocks, respectively combining the N adjustment data position information sets, based on the hash tree, constructing N adjustment hash trees; Transmitting the multiple target data blocks, the basic hash tree, and the N adjusted hash trees to the receiving node through the transmitting node; In the receiving node, generating a verification hash tree and N adjusted verification hash trees according to the multiple target data blocks, the multiple basic data location information, and the N location adjustment strategies; Verify the basic hash tree and the N adjusted hash trees based on the verification hash tree and the N adjusted verification hash trees, and obtain a percentage of inconsistent hash trees as a malicious rate; Based on the malicious rate, the verification security rate is calculated, and combined with the transmission node credibility, the verification score is calculated to obtain a judgment whether it is greater than the verification score threshold, and a yes or no integrity verification result is obtained.

8. A system for integrity verification and processing of trusted data transmission, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: A credibility analysis module is used to obtain the trusted transmission data records of the transmission node in the historical time, perform node credibility analysis on the transmission node, and obtain the credibility of the transmission node; A data acquisition module, configured to obtain transmission efficiency requirement parameters for the current transmitting node and the receiving node in transmitting multiple target data blocks, and analyze data identical parameters of the multiple target data blocks; a hash tree construction module, configured to construct a hash tree in the transmission node based on the multiple target data blocks and the multiple basic data location information to obtain a basic hash tree, configure a number of random position adjustments based on the transmission efficiency requirement parameter, the transmission node credibility, and the data identity parameter to obtain a number of random position adjustments N, and generate N position adjustment strategies, where N is a positive integer; The optimization output module is used to randomly adjust the positions of the multiple target data blocks according to the N position adjustment strategies, obtain N adjusted data position information sets, construct multiple adjusted hash trees in combination with the multiple target data blocks, and perform data transmission. In 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, and verification is performed to obtain a malicious rate. In combination with the credibility of the transmission node, a verification score is calculated as the integrity verification result.

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