Block chain-based information credibility evaluation method and system

Abstract

The invention discloses an information credibility evaluation method and system based on a block chain, and relates to the technical field of information credibility evaluation, and the method comprises the steps: distributing target information to Q uploading users and P downloading users; generating Q pieces of uploading protocol behavior data and P pieces of downloading protocol behavior data; generating a first evaluation coefficient; generating Q uploading risk coefficients and P downloading risk coefficients according to the confidentiality risk; performing security analysis according to the Q uploading risk coefficients and the P downloading risk coefficients to generate a second evaluation coefficient; and comprehensively analyzing the first evaluation coefficient and the second evaluation coefficient to generate a credible evaluation result. According to the method and the device, the technical problems that in the prior art, the security degree of each user is concerned in a one-sided manner during information credibility assessment, and the analysis of the overall security degree of multiple users participating in information flow transmission is lacked are solved, and the technical effects of performing overall dimension risk analysis on the multiple users by utilizing the block chain and improving the information assessment accuracy are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of information credibility assessment, and in particular to a blockchain-based information credibility assessment method and system. Background Art

[0002] As people gradually realize the importance of information security, information credibility assessment has become a focus of attention. At present, information assessment mainly relies on quantitative scoring values, which is too single in data analysis and cannot conduct in-depth information mining on data, resulting in the evaluation results failing to meet the use requirements. The existing technology has the technical problem of focusing on the security of all parties when evaluating information credibility, and lacks the analysis of the overall security level of multiple users involved in the transmission of information flow. Summary of the invention

[0003] The present application provides a blockchain-based information credibility assessment method and system, which is used to solve the technical problems in the prior art of one-sidedly focusing on the security of each user during information credibility assessment and lacking analysis of the overall security level of multiple users involved in information flow transmission.

[0004] In view of the above problems, this application provides a blockchain-based information credibility assessment method and system.

[0005] In a first aspect of the present application, a blockchain-based information trust evaluation method is provided, wherein the method is applied to an information trust evaluation platform, the information trust evaluation platform is communicatively connected to an upload port matrix and a download port matrix, and the method comprises:

[0006] Distribute target information to Q upload users and P download users, wherein the Q upload users and P download users upload and download distribution data through an upload port matrix and a download port matrix respectively, the target information is flow information that is trusted by the information trust evaluation platform, and the upload users and download users have independent information transmission ports;

[0007] Based on the flow record of the distribution data distributed to the Q upload users and P download users in the security protocol, the key-encrypted protocol data is downloaded from the public cloud, decrypted in the trusted execution environment, and verification data is generated. After being encrypted with the public key, it is sent to the Q upload users and P download users for decryption using the Q upload private keys and the P download private keys. The decryption result is matched with the verification data. If the match is successful, a protocol behavior data set is obtained, wherein the protocol behavior data set includes Q upload protocol behavior data and P download protocol behavior data;

[0008] Performing parallel conflict analysis based on the Q upload protocol behavior data and the P download protocol behavior data to generate a first evaluation coefficient;

[0009] Conduct confidentiality risk analysis on Q uploading users and P downloading users respectively, and generate Q uploading risk coefficients and P downloading risk coefficients;

[0010] Perform security analysis based on the Q upload risk coefficients and the P download risk coefficients to generate a second assessment coefficient;

[0011] The first evaluation coefficient and the second evaluation coefficient are comprehensively analyzed to generate a credible evaluation result.

[0012] The second aspect of the present application provides a blockchain-based information trust evaluation system, the system comprising:

[0013] A target information distribution module is used to distribute the target information to Q upload users and P download users, wherein the Q upload users and P download users upload and download the distribution data through the upload port matrix and the download port matrix respectively, the target information is the flow information that is trusted by the information trust evaluation platform, and the upload user and the download user have independent information transmission ports;

[0014] A protocol behavior data generation module, for downloading key-encrypted protocol data from a public cloud based on the flow record of the distribution data distributed to the Q upload users and P download users in the security protocol, decrypting the data in a trusted execution environment, and generating verification data, encrypting the data with a public key and sending the data to the Q upload users and P download users for decryption using the Q upload private keys and the P download private keys, matching the decryption result with the verification data, and obtaining a protocol behavior data set if the match is successful, wherein the protocol behavior data set includes Q upload protocol behavior data and P download protocol behavior data;

[0015] An evaluation coefficient generation module, used to perform parallel conflict analysis based on the Q upload protocol behavior data and the P download protocol behavior data to generate a first evaluation coefficient;

[0016] A risk coefficient generation module is used to perform confidentiality risk analysis on the Q uploading users and the P downloading users respectively, and generate Q uploading risk coefficients and P downloading risk coefficients;

[0017] A second evaluation coefficient generating module, used for performing a security analysis based on the Q upload risk coefficients and the P download risk coefficients to generate a second evaluation coefficient;

[0018] The evaluation result generating module is used to comprehensively analyze the first evaluation coefficient and the second evaluation coefficient to generate a credible evaluation result.

[0019] According to a third aspect of the present application, an electronic device is provided, including:

[0020] A memory for storing executable instructions;

[0021] A processor, when executing the executable instructions stored in the memory, implements the blockchain-based information trust evaluation method provided by the embodiments of the present application.

[0022] In the fourth aspect of the present application, a computer-readable storage medium is provided, storing a computer program, which when executed by a processor, implements the blockchain-based information trust evaluation method provided by the embodiments of the present application.

[0023] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0024] In the present application, Q uploading users and P downloading users of the target information are extracted based on the uploading port matrix and the downloading port matrix. Among them, the target information is the transfer information for trust evaluation using the information trust evaluation platform. The uploading users and the downloading users have independent information transmission ports. Then, based on the transfer records of the distributed data to the Q uploading users and P downloading users in the security protocol, the protocol data encrypted with a key is downloaded from the public cloud, decrypted in the trusted execution environment, and verification data is generated. After being encrypted with the public key, it is sent to the Q uploading users and P downloading users for decryption using the Q uploading private keys and P downloading private keys. The decryption results are matched with the verification data. If the match is passed, a protocol behavior data set is obtained, where the protocol behavior data set includes Q uploading protocol behavior data and P downloading protocol behavior data. Furthermore, parallel conflict analysis is performed based on the Q uploading protocol behavior data and P downloading protocol behavior data to generate a first evaluation coefficient, and confidentiality risk analysis is respectively performed on the Q uploading users and P downloading users to generate Q uploading risk coefficients and P downloading risk coefficients. Then, security analysis is performed according to the Q uploading risk coefficients and P downloading risk coefficients to generate a second evaluation coefficient. By comprehensively analyzing the first evaluation coefficient and the second evaluation coefficient, a trust evaluation result is generated. It achieves the technical effect of improving the reliability of the information trust evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flowchart of the blockchain-based information trust evaluation method provided by the embodiments of the present application;

[0027] Figure 2A schematic diagram of the structure of a blockchain-based information trust evaluation system provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of a system electronic device for a blockchain-based information trust evaluation method provided in an embodiment of the present invention.

[0029] Explanation of the accompanying drawings: target information distribution module 11, protocol behavior data generation module 12, evaluation coefficient generation module 13, risk coefficient generation module 14, second evaluation coefficient generation module 15, evaluation result generation module 16, processor 31, memory 32, input device 33, output device 34. DETAILED DESCRIPTION

[0030] This application provides a blockchain-based information credibility assessment method and system to solve the technical problems in the prior art of one-sidedly focusing on the security of each user during information credibility assessment and lacking analysis of the overall security level of multiple users involved in information flow transmission.

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] like Figure 1 As shown, the present application provides a blockchain-based information trust evaluation method, wherein the method is applied to an information trust evaluation platform, the information trust evaluation platform is communicatively connected with an upload port matrix and a download port matrix, and the method includes:

[0035] Distribute target information to Q upload users and P download users, wherein the Q upload users and P download users upload and download distribution data through an upload port matrix and a download port matrix respectively, the target information is flow information that is trusted by the information trust evaluation platform, and the upload users and download users have independent information transmission ports;

[0036] In a possible embodiment, the information trust evaluation platform is a platform for intelligently evaluating the trust level of transferred information in a trusted execution environment. The information trust evaluation platform is connected to the upload port matrix and the download port matrix to receive and output data of users on all sides of information transfer. The target information is the transferred information for which trust evaluation is performed using the information trust evaluation platform. The upload user is any user who uploads information, and the download user is any user who needs to download information to the local area. That is to say, the target information is the content that can be uploaded and downloaded in the information trust evaluation platform and is distributed to Q upload users and P download users.

[0037] Exemplarily, when contract bidding needs to be carried out, the bidding information contract is used as the target information. First, the target information is split into different types of information, such as information on the type of bidding project, bidding location, bidding time, hard indicators of the second party in the bidding, bid deadline, bid email, etc., and distributed to multiple participants of Party A and multiple parties of Party B. Then, multiple participants of Party A (Q upload users) respectively fill in the type of information in their hands and upload it to the information trust evaluation platform through their respective upload ports, while multiple parties of Party B (P download users) download the information of Party A from the information trust evaluation platform through their respective download ports. Furthermore, a transfer record of the distributed data in the security protocol is obtained based on the upload records of multiple participants of Party A and the download records of multiple parties of Party B, that is, information describing how the distributed data is transferred in the information trust evaluation platform. The transfer record is generated in the information trust evaluation platform. When it is necessary to call the transfer record for analysis, first, the security of the call environment needs to be verified. Through protocol data encrypted with a key, verification data is generated, and the decryption results of multiple participants of Party A and multiple parties of Party B are respectively verified and matched. If the match is passed, it indicates that the call environment is secure. At this time, the data upload situation of multiple participants of Party A and the download situation of multiple parties of Party B can be obtained. Furthermore, through parallel conflict analysis and security analysis of the behaviors of multiple participants of Party A and multiple parties of Party B during upload and download, calculations are performed based on the analysis results to generate a trust evaluation result of the bidding information. Thus, the technical effect of improving the reliability of information trust evaluation is achieved.

[0038] Download the protocol data encrypted with a key from the public cloud based on the transfer record of the distribution data distributed to the Q uploading users and P downloading users in the security protocol, decrypt it in the trusted execution environment, and generate verification data. After encrypting it with the public key, send it to the Q uploading users and P downloading users for decryption using the Q uploading private keys and P downloading private keys, and match the decryption result with the verification data. If the match passes, obtain the protocol behavior data set, where the protocol behavior data set includes Q uploading protocol behavior data and P downloading protocol behavior data;

[0039] Further, the embodiment of the present application further includes:

[0040] Collect the upload record data and upload time of the Q uploading users within the preset monitoring window respectively to generate Q uploading protocol behavior data;

[0041] Collect the download record data and download time of the P downloading users within the preset monitoring window respectively to generate P downloading protocol behavior data.

[0042] In an embodiment of the present application, a request for blockchain is received, and a transfer record of the target information in a security protocol is made. Multiple participating parties are obtained, where the multiple participating parties are Q uploading users and P downloading users who perform data transfer. The transfer record is record data generated when the distributed data is uploaded and downloaded based on the security protocol in the information credibility evaluation platform, including data such as upload time, upload port, port where the uploaded data is downloaded, and download time. According to the one-to-one correspondence between the port and the user, the corresponding Q uploading users and P downloading users are determined. A trusted execution environment is obtained based on the information credibility evaluation platform, and according to the transfer record of the target information in the security protocol, the protocol data encrypted with a key that needs to be downloaded from the public cloud is determined, decrypted in the trusted execution environment, and verification data is randomly generated. The verification data is encrypted with a public key and then sent to the Q uploading users and P downloading users, decrypted with Q uploading private keys and P downloading private keys, and then the decryption result is matched with the verification data to determine whether the transfer record of the distributed data in the security protocol can be obtained. That is to say, the protocol data is used to protect the transfer record and prevent the transferred data from being stolen. If the matching passes, it indicates that the data is secure. At this time, the transfer record of the distributed data in the security protocol is obtained from the trusted execution environment, and the protocol behavior data set is generated. The protocol behavior data set is record data of the Q uploading users and P downloading users operating on the distributed data obtained by distribution, that is, the transfer record. By collecting the protocol behavior information of the users, the security and credibility of the users' information upload or download are analyzed. Preferably, the upload record data and upload time of the Q uploading users within a preset monitoring window are respectively collected to generate Q upload protocol behavior data, and the download record data and download time of the P downloading users within a preset monitoring window are respectively collected to generate P download protocol behavior data. The technical effect of providing a basis for subsequent security and credibility evaluation is achieved.

[0043] Based on the Q upload protocol behavior data and the P download protocol behavior data, parallel conflict analysis is performed to generate a first evaluation coefficient;

[0044] Further, the embodiment of the present application further includes:

[0045] Indexing by the upload time, the Q upload protocol behavior data is retrieved to obtain Q upload time sets;

[0046] Indexing by the download time, the P download protocol behavior data is retrieved to obtain P download time sets;

[0047] Dense analysis is respectively performed on the Q upload time sets and the P download time sets to obtain Q upload time periods and P download time periods;

[0048] Calculate the reciprocal of the overlap rate of Q upload time periods and P download time periods to generate a first evaluation coefficient.

[0049] Furthermore, the embodiments of the present application further include:

[0050] Select one upload time set from the Q upload time sets as the first upload time set;

[0051] Construct a first dense analysis space based on the first upload time set, where the first dense analysis space includes multiple first particles, and each first particle corresponds to an upload time in the first upload time set;

[0052] Obtain the central particle of the first dense analysis space, where the coordinates of the central particle are the average values of the coordinates of the particles in the first dense analysis space;

[0053] Calculate the density of the central particle as the first central density.

[0054] Furthermore, the embodiments of the present application further include:

[0055] Starting from the central particle, move according to a preset step length to obtain a first iterative particle;

[0056] Calculate the density of the first iterative particle to generate a first iterative density;

[0057] Judge whether the first central density is greater than the first iterative density. If so, use the central particle as the stage central particle;

[0058] If not, update the first iterative particle as the stage central particle;

[0059] Starting from the stage central particle, perform multiple iterative updates, and use the stage central particle corresponding to the maximum density value during the iteration process as the target central particle.

[0060] Furthermore, the embodiments of the present application further include:

[0061] Calculate the ratio of the distances from multiple first particles in the first dense analysis space to the target central particle to the sum of the distances from multiple first particles to the target central particle respectively, and use the reciprocal of the calculated ratio as multiple weight coefficients;

[0062] Perform weighted calculation on multiple first particles according to the multiple weight coefficients to generate a first upload time period;

[0063] Perform dense analysis on Q upload time sets and P download time sets respectively to obtain Q upload time periods and P download time periods.

[0064] In a possible embodiment, the first evaluation coefficient is obtained by performing parallel conflict analysis based on Q upload protocol behavior data and P download protocol behavior data, that is, performing conflict analysis based on the overlap degree of upload time and download time of the upload user and the download user. The first evaluation coefficient reflects the information security level after the information conflicts due to network occupation. The higher the evaluation coefficient, the greater the security level.

[0065] Then, using the upload time as an index, the Q upload protocol behavior data are retrieved to obtain Q upload time sets, which reflect the distribution of upload time nodes. Using the download time as an index, the P download protocol behavior data are retrieved to obtain P download time sets. The Q upload time sets and the P download time sets are densely analyzed respectively, that is, the time periods densely distributed in the upload time set and the download time set are analyzed respectively to obtain Q upload time periods and P download time periods, and then the inverse of the overlap rate of the Q upload time periods and the P download time periods is calculated, that is, the inverse of the ratio of the overlap period of the Q upload time periods and the P download time periods to the total period is calculated to obtain the first evaluation coefficient.

[0066] Preferably, an upload time set is selected from the Q upload time sets as the first upload time set, and then a first dense analysis space is constructed based on the first upload time set, wherein the first dense analysis space includes multiple first particles, each first particle corresponds to an upload time in the first upload time set, and then the coordinates of multiple particles in the first dense analysis space are averaged to obtain the central particle of the first dense analysis space, that is, the coordinates of the central particle are the average of the coordinates of each particle in the first dense analysis space. The density of the central particle is obtained as the first central density by calculating the number of particles whose distance to the central particle is a preset threshold divided by the area of a circle with the central particle as the center and the preset threshold as the radius. The preset threshold is a value set by a technician in this field. The central density reflects the density of particles gathered around the central particle.

[0067] Then, the center particle is taken as the starting point, and the particle is moved according to the preset step length to obtain the first iteration particle, that is, the particle obtained after the center particle moves the preset step length. Then, the density of the first iteration particle is calculated to generate the first iteration density, and it is determined whether the first center density is greater than the first iteration density. If so, the center particle is used as the stage center particle. If not, the first iteration particle is updated to the stage center particle, and then, the stage center particle corresponding to the maximum density in the iteration process is used as the target center particle.

[0068] In a possible embodiment, calculate the ratio of the distances from multiple first particles in the first intensive analysis space to the target central particle to the sum of the distances from the multiple first particles to the target central particle respectively, take the reciprocal of the calculated ratio as multiple weight coefficients, perform weighted calculation on the multiple first particles according to the multiple weight coefficients to generate a first upload time period. Based on the same method, perform intensive analysis on Q upload time sets and P download time sets respectively to obtain Q upload time periods and P download time periods.

[0069] Perform confidentiality risk analysis on Q upload users and P download users respectively to generate Q upload risk coefficients and P download risk coefficients;

[0070] Perform security analysis based on the Q upload risk coefficients and P download risk coefficients to generate a second evaluation coefficient;

[0071] Perform comprehensive analysis on the first evaluation coefficient and the second evaluation coefficient to generate a credible evaluation result.

[0072] Furthermore, the embodiment of the present application further includes:

[0073] Collect the number of times the user terminals of Q upload users and P download users are attacked respectively to generate M first security coefficients, where M = Q + P;

[0074] Collect the service lives of the user terminals of Q upload users and P download users respectively to generate M second security coefficients;

[0075] Perform weighted calculation on the M first security coefficients and the M second security coefficients to generate a second evaluation coefficient.

[0076] In an embodiment, by analyzing the usage risks of the user terminals of Q upload users and P download users respectively, Q upload risk coefficients and P download risk coefficients are obtained. By collecting the number of times the user terminals of Q upload users and P download users are attacked respectively, taking the reciprocal of the ratio of the number of times each user terminal is attacked to the total number of times all user terminals are attacked as the first security coefficient, thus obtaining M first security coefficients, where M = Q + P;

[0077] Furthermore, collect the service lives of the user terminals of Q upload users and P download users respectively, take the reciprocal of the ratio of the service life of the user terminal to the designed life as the second security coefficient, thus obtaining M second security coefficients. It reflects the reliability of the user terminal. Furthermore, according to the weight ratio preset by those skilled in the art, perform weighted calculation on the M first security coefficients and the M second security coefficients to generate a second evaluation coefficient.

[0078] Preferably, the first evaluation coefficient and the second evaluation coefficient are averaged, and the average calculation result is used as the credible evaluation result.

[0079] In summary, the embodiments of the present application have at least the following technical effects:

[0080] The present application extracts users by acquiring, obtains Q upload users and P download users of the target information, and then obtains a protocol behavior data set after data verification, wherein the protocol behavior data set includes Q upload protocol behavior data and P download protocol behavior data, and then generates a first evaluation coefficient, and then respectively performs confidentiality risk analysis on the Q upload users and P download users, generates Q upload risk coefficients and P download risk coefficients, performs security analysis based on the Q upload risk coefficients and P download risk coefficients, generates a second evaluation coefficient, and then performs a comprehensive analysis on the first evaluation coefficient and the second evaluation coefficient to generate a credible evaluation result. The technical effect of improving the reliability of the information credible evaluation result is achieved.

[0081] Embodiment 2

[0082] Based on the same inventive concept as the information trustworthiness assessment method based on blockchain in the aforementioned embodiment, Figure 2 As shown, the present application provides a blockchain-based information trust evaluation system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0083] The target information distribution module 11 is used to distribute the target information to Q upload users and P download users, wherein the Q upload users and P download users upload and download the distribution data respectively through the upload port matrix and the download port matrix, distribute the target information to Q upload users and P download users, wherein the Q upload users and P download users upload and download the distribution data respectively through the upload port matrix and the download port matrix, the target information is the flow information that is trusted by the information trust evaluation platform, and the upload user and the download user have independent information transmission ports;

[0084] The protocol behavior data generating module 12 is used to download the key-encrypted protocol data from the public cloud based on the flow record of the distribution data distributed to the Q upload users and the P download users in the security protocol, decrypt it in the trusted execution environment, and generate verification data, encrypt it with the public key and send it to the Q upload users and the P download users to decrypt it with the Q upload private keys and the P download private keys, match the decryption result with the verification data, and if the match is successful, obtain the protocol behavior data set, wherein the protocol behavior data set includes the Q upload protocol behavior data and the P download protocol behavior data;

[0085] The evaluation coefficient generation module 13 is configured to perform parallel conflict analysis based on Q upload protocol behavior data and P download protocol behavior data, and generate a first evaluation coefficient;

[0086] The risk coefficient generation module 14 is configured to perform confidentiality risk analysis on Q upload users and P download users respectively, and generate Q upload risk coefficients and P download risk coefficients;

[0087] The second evaluation coefficient generation module 15 is configured to perform security analysis based on Q upload risk coefficients and P download risk coefficients, and generate a second evaluation coefficient;

[0088] The evaluation result generation module 16 is configured to perform comprehensive analysis on the first evaluation coefficient and the second evaluation coefficient, and generate a credible evaluation result.

[0089] Furthermore, the system further includes:

[0090] Collect the upload record data and upload time of Q upload users within a preset monitoring window respectively, and generate Q upload protocol behavior data;

[0091] Collect the download record data and download time of P download users within a preset monitoring window respectively, and generate P download protocol behavior data.

[0092] Furthermore, the system further includes:

[0093] Retrieve the Q upload protocol behavior data by using the upload time as an index, so as to obtain Q upload time sets;

[0094] Retrieve the P download protocol behavior data by using the download time as an index, so as to obtain P download time sets;

[0095] Perform intensive analysis on the Q upload time sets and the P download time sets respectively, and obtain Q upload time periods and P download time periods;

[0096] Calculate the reciprocal of the overlap rate of the Q upload time periods and the P download time periods, and generate a first evaluation coefficient.

[0097] Furthermore, the system further includes:

[0098] Select one upload time set from the Q upload time sets as the first upload time set;

[0099] Construct a first intensive analysis space based on the first upload time set, where the first intensive analysis space includes a plurality of first particles, and each first particle corresponds to an upload time in the first upload time set;

[0100] Obtain the central particle of the first dense analysis space, where the coordinates of the central particle are the average of the coordinates of each particle in the first dense analysis space;

[0101] Calculate the density of the central particle as the first central density.

[0102] Furthermore, the system further includes:

[0103] Starting from the central particle, move according to a preset step length to obtain the first iterative particle;

[0104] Calculate the density of the first iterative particle to generate the first iterative density;

[0105] Judge whether the first central density is greater than the first iterative density. If so, use the central particle as the stage central particle;

[0106] If not, update the first iterative particle to the stage central particle;

[0107] Starting from the stage central particle, perform multiple iterative updates, and use the stage central particle corresponding to the maximum density value in the iterative process as the target central particle.

[0108] Furthermore, the system further includes:

[0109] Calculate the ratio of the distances from multiple first particles in the first dense analysis space to the target central particle to the sum of the distances from the multiple first particles to the target central particle respectively, and use the reciprocal of the calculated ratio as multiple weight coefficients;

[0110] Perform weighted calculation on the multiple first particles according to the multiple weight coefficients to generate the first upload time period;

[0111] Perform dense analysis on Q upload time sets and P download time sets respectively to obtain Q upload time periods and P download time periods.

[0112] Furthermore, the system further includes:

[0113] Collect the number of times the user terminals of Q upload users and P download users are attacked respectively to generate M first security coefficients, where M = Q + P;

[0114] Collect the service lives of the user terminals of Q upload users and P download users respectively to generate M second security coefficients;

[0115] Perform weighted calculation on the M first security coefficients and the M second security coefficients to generate the second evaluation coefficient.

[0116] Embodiment III

[0117] Figure 3 FIG. Figure 3 is a schematic structural diagram of an electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present invention. As Figure 3 shown, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more. Figure 3 Taking one processor 31 as an example, the processor 31, the memory 32, the input device 33, and the output device 34 in the electronic device may be connected through a bus or other means. Figure 3 Taking connection through a bus as an example.

[0118] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the information trust evaluation based on blockchain in the embodiments of the present invention. The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 32, that is, to implement the above-mentioned information trust evaluation based on blockchain.

[0119] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims may be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0121] This specification and the drawings are merely exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An information credibility evaluation method based on blockchain, characterized in that The method is applied to an information credibility evaluation system constructed by a blockchain. The information credibility evaluation system is communicatively connected to an upload port matrix and a download port matrix. The method includes: Invoking target information through a smart contract and distributing the target information to Q upload users and P download users. Among them, the Q upload users and P download users upload and download distributed data through the upload port matrix and the download port matrix respectively. The target information is transfer information for credibility evaluation using the information credibility evaluation platform. The upload users and download users have independent information transmission ports; Based on the transfer records of the distributed data distributed to the Q upload users and P download users in the security protocol, downloading protocol data encrypted with a key from a public cloud, decrypting it in a trusted execution environment, and generating verification data. After encrypting with a public key, sending it to the Q upload users and P download users to decrypt using Q upload private keys and P download private keys, and matching the decryption results with the verification data. If the match passes, a protocol behavior data set is obtained, where the protocol behavior data set includes Q upload protocol behavior data and P download protocol behavior data; Performing parallel conflict analysis based on the Q upload protocol behavior data and the P download protocol behavior data to generate a first evaluation coefficient; Respectively performing confidentiality risk analysis on the Q upload users and P download users to generate Q upload risk coefficients and P download risk coefficients; Performing security analysis based on the Q upload risk coefficients and the P download risk coefficients to generate a second evaluation coefficient; Performing comprehensive analysis on the first evaluation coefficient and the second evaluation coefficient to generate a credibility evaluation result.

2. The method according to claim 1, characterized in that, The method further includes: Respectively collecting the upload record data and upload time of the Q upload users within a preset monitoring window to generate Q upload protocol behavior data; Respectively collecting the download record data and download time of the P download users within a preset monitoring window to generate P download protocol behavior data.

3. The method according to claim 2, wherein The method further includes: Retrieving the Q upload protocol behavior data with the upload time as an index to obtain Q upload time sets; Retrieving the P download protocol behavior data with the download time as an index to obtain P download time sets; Respectively performing intensive analysis on the Q upload time sets and the P download time sets to obtain Q upload time periods and P download time periods; Calculating the reciprocal of the overlap rate of the Q upload time periods and the P download time periods to generate a first evaluation coefficient.

4. The method according to claim 3, wherein The method further includes: Selecting one upload time set from the Q upload time sets as the first upload time set; Constructing a first intensive analysis space based on the first upload time set, where the first intensive analysis space includes multiple first particles, and each first particle corresponds to an upload time in the first upload time set; Obtaining the central particle of the first intensive analysis space, where the coordinates of the central particle are the average values of the coordinates of each particle in the first intensive analysis space; Calculating the density of the central particle as the first central density.

5. The method according to claim 4, characterized in that, The method further includes: Starting from the central particle, move according to a preset step size to obtain the first iterative particle; Calculate the density of the first iterative particle to generate the first iterative density; Determine whether the first central density is greater than the first iterative density. If so, use the central particle as the stage central particle; If not, update the first iterative particle as the stage central particle; Starting from the stage central particle, perform multiple iterative updates, and use the stage central particle corresponding to the maximum density value during the iteration process as the target central particle.

6. The method according to claim 5, wherein The method further includes: Calculate the ratio of the distances from multiple first particles in the first dense analysis space to the target central particle to the sum of the distances from multiple first particles to the target central particle respectively, and use the reciprocal of the calculated ratio as multiple weight coefficients; Perform weighted calculation on multiple first particles according to the multiple weight coefficients to generate the first upload time period; Perform dense analysis on Q upload time sets and P download time sets respectively to obtain Q upload time periods and P download time periods.

7. The method according to claim 1, characterized in that The method further includes: Collect the number of times the user terminals of Q upload users and P download users are attacked respectively to generate M first security coefficients, where M = Q + P; Collect the service lives of the user terminals of Q upload users and P download users respectively to generate M second security coefficients; Perform weighted calculation on the M first security coefficients and M second security coefficients to generate the second evaluation coefficient.

8. The information credibility evaluation system based on blockchain is characterized in that, The system includes: A target information distribution module, configured to call target information through a smart contract and distribute the target information to Q upload users and P download users. Among them, the Q upload users and P download users upload and download distributed data through an upload port matrix and a download port matrix respectively. The target information is the transfer information for trusted evaluation using the information trusted evaluation platform. The upload users and download users have independent information transmission ports; A protocol behavior data generation module, configured to, based on the transfer records of the distributed data distributed to the Q upload users and P download users in the security protocol, download the protocol data encrypted by a key from the public cloud, decrypt it in a trusted execution environment, and generate verification data. After encrypting it with a public key, send it to the Q upload users and P download users for decryption using Q upload private keys and P download private keys. Match the decryption result with the verification data. If the match passes, obtain a protocol behavior data set, where the protocol behavior data set includes Q upload protocol behavior data and P download protocol behavior data; An evaluation coefficient generation module, configured to perform parallel conflict analysis based on Q upload protocol behavior data and P download protocol behavior data to generate the first evaluation coefficient; A risk coefficient generation module, configured to perform confidentiality risk analysis on Q upload users and P download users respectively to generate Q upload risk coefficients and P download risk coefficients; A second evaluation coefficient generation module, configured to perform security analysis according to Q upload risk coefficients and P download risk coefficients to generate the second evaluation coefficient; An evaluation result generation module, configured to comprehensively analyze the first evaluation coefficient and the second evaluation coefficient to generate a credible evaluation result.

9. An electronic device, characterized in that, The electronic device includes: a memory, configured to store executable instructions; a processor, configured to implement the blockchain-based information credibility evaluation method according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the blockchain-based information credibility evaluation method according to any one of claims 1-7.

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