Informatization management system based on block chain

Abstract

The invention relates to the technical field of block chains, in particular to an informatization management system based on a block chain. The blockchain-based informatization management system disclosed by the invention comprises a data acquisition module, a student work management module, a financial management module, a protection module, an efficiency inspection module and a verification storage module. According to the method, the blockchain network is established, the informatization management system based on the blockchain is constructed by using distributed computing, and the original data protection capability of the university informatization management system is enhanced in combination with privacy computing, so that the privacy and security of the original data are ensured, and the security of the university informatization management system is improved. The technical problems of data synchronization delay and data conflict caused by network delay and bandwidth limitation when the colleges and universities expand and call different-place campus areas or different-place branch mechanisms are avoided, the safety of the data in the transmission and storage process is ensured, and the protection capability of the original data is improved.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to an information management system based on blockchain. Background Art

[0002] Blockchain-based information management is a solution that uses blockchain technology to improve the management efficiency, transparency and security of colleges and universities. It is very suitable for information management in colleges and universities. The core feature of blockchain technology is that data cannot be tampered with. Once information is recorded on the blockchain, it is difficult to be modified or deleted, which is very beneficial to ensure the authenticity and integrity of data. The existing information management system is usually based on a centralized integrated portal architecture, which can achieve information interconnection and one-stop service to meet the needs of smart campus construction. When colleges and universities establish remote campuses or branches, geographical obstacles will inevitably affect the efficiency of data synchronization and maintenance. At the same time, the data transmission process also poses challenges to data security and risk control. With the increasing number of remote campuses or remote branches, the difficulty of maintaining and operating the information management system will also increase. On the other hand, since all information in the information management system based on blockchain technology is recorded on the blockchain, all nodes can verify and query, which poses new challenges to the privacy of college information.

[0003] In view of this, it is necessary to improve the blockchain-based information management system in the existing technology to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to disclose an information management system based on blockchain, which is used to solve the above-mentioned technical problems existing in the prior art, and is particularly intended to avoid the use of a central server, avoid data synchronization delays and data conflicts caused by network delays and bandwidth limitations when colleges and universities expand enrollment in remote campuses or remote branches, so as to improve the privacy and security of original data.

[0005] To achieve the above objectives, the present invention provides an information management system based on blockchain, comprising:

[0006] Data collection module, student work management module, financial management module, protection module, efficiency inspection module and verification storage module;

[0007] The data collection module is used to collect student affairs information, operation information, and scientific research information, and pre-process the student affairs information, operation information, and scientific research information and send them to the student affairs management module, financial management module, and protection module respectively;

[0008] The student work management module is used to process student work information based on homomorphic encryption technology, analyze students' personal information, and evaluate the protection efficiency of student work information;

[0009] The financial management module is used to process operational information based on secure multi-party computing technology, analyze financial status, and evaluate the protection efficiency of operational information;

[0010] The protection module is used to process scientific research information according to the federated learning algorithm, conduct cooperative management of scientific research raw data, and evaluate the protection efficiency of scientific research information;

[0011] The efficiency test module is used to evaluate the privacy protection capability of the blockchain-based information management system by combining the protection efficiency of student information, operational information, and scientific research information;

[0012] The verification storage module is used to perform reliability analysis on data based on privacy protection capabilities and to store verified data in a chained manner.

[0013] As a further improvement of the present invention, the student information includes the student's identity information, educational experience, physical examination data, course schedule, tuition data, grade information, and attendance information;

[0014] Operational information includes asset data, meeting schedules, fund usage, and salary information;

[0015] Scientific research information includes project approval, scientific research reports, scientific research papers, R&D patents, and scientific research awards.

[0016] As a further improvement of the present invention, the student-worker information is processed according to the homomorphic encryption technology as follows:

[0017] Generate a public key for encryption and a private key for decryption, and send the private key to the teacher;

[0018] Teachers use public keys to encrypt student information and store the encrypted student information on the blockchain;

[0019] The encrypted student information is subjected to homomorphic operations to analyze and process the information, and the analysis and processing results are stored in encrypted form on the blockchain.

[0020] As a further improvement of the present invention, the protection efficiency of student information is evaluated as follows:

[0021] Calibration encryption performance coefficient Ep, encryption performance coefficient In the formula, Et is the encryption time, Dt is the decryption time, and Ho is the batch homomorphic operation time;

[0022] Select a set of student information data, record the current time T1 before starting the encryption operation, perform homomorphic encryption on the student information data, and after the encryption operation is completed, record the current time T2, and the encryption time Et = T2-T1;

[0023] Using the homomorphically encrypted student information data, before starting the decryption operation, record the current time T3 and decrypt the encrypted data. After the decryption operation is completed, record the current time T4, and the decryption time Dt = T4-T3;

[0024] Select multiple groups of encrypted student information data, record the current time T5 before starting batch homomorphic operations, perform a batch of homomorphic operations, and after the batch homomorphic operations are completed, record the current time T6, and the batch homomorphic operation time Ho = T6-T5.

[0025] As a further improvement of the present invention, the operation information is processed according to the secure multi-party computing technology as follows:

[0026] Deploy secure multi-party computing nodes based on the operational information independently stored by each remote campus or branch;

[0027] Each remote campus or branch uses different encryption algorithms to encrypt the operational information, generate a hash value for the encrypted data, and record the hash value on the blockchain;

[0028] Using threshold encryption technology, key fragments are shared by different remote campuses or branches;

[0029] Each remote campus or branch jointly sets financial audit objectives, determines calculation functions and required data;

[0030] The secure multi-party computing nodes of each remote campus or remote branch participate in the distributed computing in an encrypted state and transmit the computing results between the secure multi-party computing nodes;

[0031] Each remote campus or branch verifies the hash value of the calculation result to check whether the calculation result has been tampered with;

[0032] The calculation results are decrypted jointly by the remote campuses or remote branches according to the threshold encryption scheme.

[0033] As a further improvement of the present invention, the protection efficiency of the operational information is evaluated as follows:

[0034] The protection efficiency of operational information is evaluated by cross-validating secure multi-party computations. The specific method is as follows:

[0035] The test coefficient of the independent sample t is calibrated as tc, and the operation data of each remote campus or remote branch is divided into multiple subsets. An independent secure multi-party computation is performed on each subset to generate the audit results of each subset.

[0036] Conduct independent sample t-test on the audit results of each subset, select two subset samples, and set two hypotheses: hypothesis one is that the means of the two independent samples are equal, and hypothesis two is that the means of the two independent samples are not equal;

[0037] According to the formula Calculate the t value, where and are the means of two independent samples, and are the variances of two independent samples, n1 and n2 are the sample sizes of two independent samples;

[0038] According to the formula Calculate the degrees of freedom df for independent samples;

[0039] Set the significance level and degrees of freedom, and find the critical value from the t distribution table;

[0040] Calculate the p-value corresponding to the t-value and compare the p-value with the set significance level;

[0041] When the p-value is less than the set significance level, hypothesis one is rejected, indicating that the means of the two independent samples are different. When the p-value is greater than or equal to the significance level, hypothesis one is not rejected, indicating that the means of the two independent samples are the same.

[0042] When hypothesis one is rejected and the means of the two independent samples are different, the label test coefficient tc is 1. When hypothesis one is not rejected and the means of the two independent samples are the same, the label test coefficient tc is 0.

[0043] As a further improvement of the present invention, the scientific research information is processed according to the federated learning algorithm as follows:

[0044] Deploy federated learning nodes based on the scientific research information independently stored in each remote campus or branch, and the federated learning nodes together form a federated learning network;

[0045] A federated learning node serves as a central coordination server to initialize a global model and broadcast the model parameters to the federated learning nodes of each remote campus or branch.

[0046] Each remote campus or branch uses local scientific research information data to train the global model based on the initial model parameters;

[0047] Each remote campus or branch encrypts the model parameters updated after local training and transmits them to the central coordination server through the blockchain network;

[0048] The central coordination server collects the encrypted model parameters uploaded by each remote campus or branch, aggregates and updates them, and broadcasts the updated global model parameters to each remote campus or branch again to enter the next training round;

[0049] Repeat the process of local model training, parameter encryption and transmission, and global model update until the global model converges or reaches the preset training rounds;

[0050] After the training is completed, the central coordination server will publish the final global model to the blockchain network, and researchers in different campuses or branches can use the updated global model for data analysis and scientific research applications.

[0051] As a further improvement of the present invention, the protection efficiency of scientific research information is evaluated as follows:

[0052] Evaluate the efficiency of protecting scientific research information using federated learning methods, which can be expressed through the performance of the training model and the calculation of the scientific research privacy coefficient Re, the scientific research privacy coefficient Where F1 is the harmonic mean of precision and recall, Auc is the area under the ROC curve, and Log is the logarithmic loss.

[0053] As a further improvement of the present invention, the privacy protection capability of the information management system based on blockchain is evaluated in combination with the protection efficiency of student information, operation information, and scientific research information. Specifically:

[0054] The privacy protection capability is evaluated by calculating the protection efficiency index Pe of the information management system based on blockchain. In the formula, α, β, and γ are the weight coefficients of the encryption performance coefficient, the verification coefficient tc, and the scientific research privacy coefficient, respectively, and α, β, and γ are all positive numbers.

[0055] As a further improvement of the present invention, the reliability of the data is analyzed according to the privacy protection capability, and the verified data is chain-stored as follows:

[0056] A protection efficiency threshold is preset. When the calculated protection efficiency index Pe is greater than or equal to the protection efficiency threshold, it is marked that the data protection capability of the blockchain-based information management system is reliable;

[0057] When the calculated protection efficiency index Pe is less than the protection efficiency threshold, the data protection capability of the blockchain-based information management system is unreliable;

[0058] The data marked as having reliable data protection capabilities of the information management system will be packaged into blocks and connected into chains in chronological order, while the data marked as having unreliable data protection capabilities of the blockchain-based information management system will be eliminated.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] This application establishes a blockchain network, uses distributed computing to build an information management system based on blockchain, and combines privacy computing to strengthen the original data protection capabilities of the university information management system, thereby ensuring the privacy and security of the original data. It can not only cancel the central server of the centralized university information management system, but also avoid the technical problems of data synchronization delays and data conflicts caused by network delays and bandwidth limitations when universities expand enrollment in remote campuses or remote branches; at the same time, this application can also ensure the security of data during transmission and storage in terms of encryption mechanisms and decentralized authority management, while ensuring data transparency and security, it also improves the protection capabilities of original data. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is the overall topology diagram of the blockchain-based information management system of the present invention. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in the field based on these embodiments are all within the scope of protection of the present invention.

[0063] See also Figure 1 As shown, the present invention is an information management system 100 based on blockchain, including: a data collection module 10, a student work management module 20, a financial management module 30, a protection module 40, an efficiency verification module 50 and a verification storage module 60. Exemplarily, the information management system 100 based on blockchain disclosed in this specific embodiment can be used to manage university information, and can also be used in enterprise scenarios with multiple branches.

[0064] The data collection module 10 is used to collect student information, operation information, and scientific research information, and pre-process the student information, operation information, and scientific research information and send them to the student work management module 20, the financial management module 30, and the protection module 40 respectively. The student work management module 20 is used to process the student information according to the homomorphic encryption technology, analyze the personal information of the students, and evaluate the protection efficiency of the student information. The financial management module 30 is used to process the operation information according to the secure multi-party computing technology, analyze the financial status, and evaluate the protection efficiency of the operation information. The protection module 40 is used to process the scientific research information according to the federated learning algorithm, cooperatively manage the original scientific research data, and evaluate the protection efficiency of the scientific research information. The efficiency verification module 50 is used to evaluate the privacy protection capability of the information management system 100 based on the blockchain in combination with the protection efficiency of the student information, operation information, and scientific research information. The verification storage module 60 is used to analyze the reliability of the data according to the privacy protection capability, and chain the verified data. It should be noted that the preprocessing performed by the aforementioned data acquisition module 10 can adopt the common data processing methods in the prior art, for example, the blockchain data collected by the student work management module 20, the financial management module 30 and the protection module 40 (that is, the original data formed by the collection of student work information, operation information, and scientific research information) is cleaned, sorted, summarized, etc. for subsequent data analysis. This process includes removing invalid data and erroneous data, normalizing and standardizing the data, and classifying and grouping. Through these operations, the quality and applicability of the data can be improved, providing support for in-depth data analysis.

[0065] Through distributed ledger technology, blockchain makes data storage and management independent of a single central institution, which can reduce the risk of single point failure and information leakage. All participants can view transaction records on the blockchain, which improves information transparency and helps prevent corruption and fraud. Smart contracts in the blockchain can automatically execute preset rules and clauses, improving the automation and efficiency of management processes. In the field of student management, the blockchain can record students' admission, graduation, grades and other information data to ensure the authenticity and reliability of the data and reduce the risk of information forgery and data loss. In terms of academic certification, students' academic certificates are stored on the blockchain, and employers and other institutions can verify the authenticity of academic qualifications through the blockchain, which can simplify the certification process. In the field of financial management, the use of blockchain for fund management and financial transparency can ensure the compliance and transparency of fund use and reduce financial risks and corruption. In the field of scientific research, the application, review and publication process of scientific research results is recorded on the blockchain to ensure that information is open and transparent at each link and prevent theft and infringement of results.

[0066] Although blockchain has the characteristics of transparency, it is necessary to balance data transparency and personal privacy protection in university management, and protect the privacy of personal data while ensuring data openness and transparency. In the existing information management system based on centralized fusion portal architecture, data is usually stored in centralized servers or databases, maintained by the IT department of the university or a specific management system. Data update and maintenance are performed by centralized systems, which have the risk of single point failure. Since data is stored in a centralized manner, it is easy to become a target of attack, and there is a high risk of leakage and tampering. In terms of access control, it relies on traditional access control mechanisms and firewalls and other security measures. The access and modification permissions of data are usually controlled by administrators, which are difficult to audit externally. Trust dependence is completely dependent on the integrity of the management system and administrators. Although centralized management is usually more efficient, when universities expand remote campuses or remote branch structures, performance bottlenecks may occur in the process of processing a large number of requests. Any system failure will affect the normal operation of the entire management system. The traditional centralized management system is relatively mature, and the complexity of implementation and maintenance is relatively low, but it must be regularly maintained and upgraded, which increases the complexity and maintenance cost of the system. The initial deployment cost is relatively low, but the long-term maintenance and security protection costs are high, and resources need to be continuously invested in system management and maintenance.

[0067] In this regard, in the present application, in the blockchain-based information management system 100, data is distributed and stored on multiple nodes in the blockchain network, without a single control center. Once the data is written to the blockchain, it cannot be easily tampered with, which can ensure the authenticity and integrity of the data. In terms of security, since the data is distributed on multiple nodes, it is difficult for attackers to attack all nodes at the same time, which effectively improves data security. In the process of data transmission and storage, the blockchain can use effective encryption technology to ensure data security. The writing and updating of data are recorded on the blockchain, and all nodes can verify and view it, which improves the transparency of the system. The blockchain network does not rely on specific managers, but realizes data verification and trust through a consensus mechanism. Since the data is distributed on multiple nodes, even if individual nodes fail, the system can still operate normally. The automated execution of management rules and processes through smart contracts can improve process efficiency. Although the initial system design, development and deployment costs of the blockchain network are high, due to the decentralization and high security of the blockchain, the long-term maintenance and management costs can establish advantages over traditional centralized information management systems.

[0068] After a university opens a remote campus or branch, the data of each remote campus needs to be synchronized in real time to ensure information consistency. If a centralized management system is used, network latency and bandwidth limitations may cause data synchronization delays, affecting management efficiency. Maintaining data consistency across all campuses when data is frequently updated is a challenge, and conflicts may occur during data synchronization, resulting in inconsistent data.

[0069] The data transmission and storage process of multiple remote campuses requires a high degree of security. The centralized management system is easily targeted by attacks, increasing the risk of data leakage and tampering. At the same time, the access rights of different campuses need to be strictly managed to prevent unauthorized access and data leakage. Managing the access rights of users in multiple locations increases the complexity of the system. Different regions may have different data protection regulations. The centralized management system needs to ensure compliance with all relevant regulations, which increases the difficulty of compliance management.

[0070] The establishment of multiple remote campuses or branches has put forward higher requirements for centralized university information management systems. In order to meet these challenges, universities may need to comprehensively optimize and improve data synchronization, data security, operation and maintenance, and compliance. Therefore, the introduction of distributed technology to establish a blockchain-based information management system 100, and the evaluation of the privacy protection capabilities of the blockchain-based information management system 100 in combination with privacy computing can effectively improve the flexibility and response capabilities of the blockchain-based information management system 100. In the following specific implementation, the blockchain information management system 100 can be simply expressed as an information management system.

[0071] Specifically, in this specific implementation, the data collection module 10 is used to collect student information, operational information, and scientific research information, and pre-process the student information, operational information, and scientific research information and send them to the student work management module 20, the financial management module 30, and the protection module 40 respectively. Student information includes students' identity information, educational experience, physical examination data, course schedule, tuition data, grade information, and attendance information. Operational information includes asset data, meeting schedule, fund use, and salary information. Scientific research information includes project approval, scientific research reports, scientific research papers, R&D patents, and scientific research awards.

[0072] The student work management module 20 is used to process student work information based on homomorphic encryption technology, analyze students' personal information, and evaluate the protection efficiency of student work information.

[0073] According to homomorphic encryption technology, the specific processing of student information is as follows:

[0074] The information management system 100 based on blockchain generates a public key for encryption and a private key for decryption, and transmits the private key to the teacher. The specific implementation of encryption and decryption involved in this application can adopt any encryption and decryption algorithm in the prior art, such as symmetric encryption, asymmetric encryption or hash algorithm, etc. Therefore, this application will not elaborate on them one by one.

[0075] Teachers use public keys to encrypt student information and store the encrypted student information on the blockchain.

[0076] The blockchain-based information management system 100 directly performs homomorphic operations on the encrypted student information to analyze and process the student information, and stores the analysis and processing results in encrypted form on the blockchain.

[0077] The protection efficiency of student information is evaluated by calculating the performance of homomorphic encryption. The specific method is as follows:

[0078] Calibration encryption performance coefficient Ep, encryption performance coefficient In the formula, Et is the encryption time, Dt is the decryption time, and Ho is the batch homomorphic operation time;

[0079] Select a set of student information data, record the current time T1 before starting the encryption operation, perform homomorphic encryption on the student information data (for example, grade information), and after the encryption operation is completed, record the current time T2, and the encryption time Et = T2-T1;

[0080] Using the homomorphically encrypted student information data, before starting the decryption operation, record the current time T3 and decrypt the encrypted data. After the decryption operation is completed, record the current time T4, and the decryption time Dt = T4-T3;

[0081] Select multiple sets of encrypted student information data, record the current time T5 before starting batch homomorphic operations, perform a batch of homomorphic operations, and after the batch homomorphic operations are completed, record the current time T6, and the batch homomorphic operation time Ho = T6-T5;

[0082] Commonly used homomorphic operations are addition and multiplication.

[0083] The financial management module 30 is used to process the operation information according to the secure multi-party computing technology, analyze the financial status, and evaluate the protection efficiency of the operation information. The operation information is processed according to the secure multi-party computing technology as follows:

[0084] Deploy secure multi-party computing nodes based on the operational information independently stored in each remote campus or branch.

[0085] Each remote campus or branch uses different encryption algorithms to encrypt operational information, generate a hash value for the encrypted data, and record the hash value on the blockchain.

[0086] Threshold encryption technology is used to share key fragments among remote campuses or branches.

[0087] The remote campuses or branches jointly set financial audit objectives and determine the calculation functions and required data.

[0088] The secure multi-party computing nodes of each remote campus or remote branch participate in the distributed computing in an encrypted state and transmit the computing results between the secure multi-party computing nodes.

[0089] Each remote campus or branch verifies the hash value of the calculation result to check whether the calculation result has been tampered with.

[0090] The calculation results are decrypted jointly by the remote campuses or remote branches according to the threshold encryption scheme.

[0091] A hash value is a unique code of fixed length calculated by a hash function. It is a one-way mathematical function that can convert an input of any length into an output of fixed length, which is usually expressed as an alphanumeric string. An input of any length may be expressed as a piece of text, a number, or a file. In secure multi-party computing, commonly used encryption algorithms include secret sharing, obfuscated circuits, oblivious transfer, and zero-knowledge proof. Threshold encryption is an encryption technology that can split a secret into multiple shares and distribute these shares to different participants. Threshold encryption is usually used to ensure that the original data can only be decrypted when a certain threshold of participants are involved.

[0092] The protection efficiency of operational information is evaluated by cross-validating secure multi-party computations. The specific method is as follows:

[0093] The test coefficient of the calibrated independent sample t is tc, and the operational data of each remote campus or remote branch is divided into multiple subsets. Independent secure multi-party computing is performed on each subset to generate the audit results of each subset.

[0094] An independent sample t-test is performed on the audit results of each subset. Two subset samples are selected and two hypotheses are set. Hypothesis one is that the means of the two independent samples are equal, and hypothesis two is that the means of the two independent samples are not equal.

[0095] According to the formula Calculate the t value, where and are the means of two independent samples, and are the variances of two independent samples, and n1 and n2 are the sample sizes of two independent samples.

[0096] According to the formula Calculate the degrees of freedom df for independent samples.

[0097] Set the significance level and degrees of freedom, and find the critical value from the t distribution table.

[0098] Calculate the p-value corresponding to the t-value, and compare the p-value with the set significance level. When the p-value is less than the set significance level, hypothesis one is rejected, and the means of the two independent samples are different. When the p-value is greater than or equal to the significance level, hypothesis one is not rejected, and it indicates that the means of the two independent samples are the same. When hypothesis one is rejected and it indicates that the means of the two independent samples are different, the test coefficient tc is marked as 1. When hypothesis one is not rejected and the means of the two independent samples are the same, the test coefficient tc is marked as 0.

[0099] It should be noted that the p-value represents the probability of observing a test statistic equal to or more extreme if the null hypothesis is true.

[0100] The protection module 40 is used to process scientific research information according to the federated learning algorithm, to collaboratively manage the original scientific research data, and to evaluate the protection efficiency of scientific research information. The processing of scientific research information according to the federated learning algorithm is specifically as follows:

[0101] Federated learning nodes are deployed based on the scientific research information independently stored in each remote campus or branch, and the federated learning nodes together form a federated learning network (Federated Learning).

[0102] A federated learning node acts as a central coordination server to initialize a global model and broadcast the model parameters to the federated learning nodes of each remote campus or branch.

[0103] Each remote campus or branch uses local scientific research information data to train the global model based on the initial model parameters.

[0104] Each remote campus or remote branch encrypts the model parameters updated after local training and transmits them to the central coordination server through the blockchain network. Since this application is not an invention of the blockchain network, this specific implementation method omits the specific topological architecture of the blockchain network and can adopt a mature blockchain network in the prior art.

[0105] The central coordination server collects the encrypted model parameters uploaded by each remote campus or branch, performs aggregate updates, and broadcasts the updated global model parameters to each remote campus or branch again to enter the next training round.

[0106] Repeat the process of local model training, parameter encryption and transmission, and global model update until the global model converges or reaches the preset training rounds.

[0107] After the training is completed, the central coordination server will publish the final global model to the blockchain network, and researchers in different campuses or branches can use the updated global model for data analysis and scientific research applications.

[0108] Each remote campus or branch will encrypt the model parameters updated after local training. Commonly used encryption algorithms include secret sharing, RSA encryption, differential privacy, etc.

[0109] Evaluate the efficiency of protecting scientific research information using federated learning methods, which can be expressed through the performance of the training model, and calculate the scientific research privacy coefficient Re. Where F1 is the harmonic mean of precision and recall, Auc is the area under the ROC curve, and Log is the logarithmic loss.

[0110] F1 can comprehensively consider precision and recall rate. The ROC curve represents the performance of the model under all classification thresholds through the relationship between the true positive rate and the false positive rate. The true positive rate represents the proportion of positive categories correctly predicted by the training model, and the false positive rate represents the proportion of negative categories incorrectly predicted by the training model as positive categories. Log measures the accuracy of the classifier by penalizing incorrect classifications, and can evaluate the probability output of the classifier.

[0111] The efficiency verification module 50 is used to evaluate the privacy protection capability of the information management system (i.e., the blockchain-based information management system 100) based on the protection efficiency of student information, operational information, and scientific research information, and specifically includes the following sub-steps.

[0112] The privacy protection capability is evaluated by calculating the protection efficiency index Pe of the information management system. In the formula, α, β, and γ are the weight coefficients of the encryption performance coefficient, the t-test coefficient tc, and the scientific research privacy coefficient, respectively, and α, β, and γ are all positive numbers.

[0113] The verification storage module is used to perform reliability analysis on data based on privacy protection capabilities and to store verified data in a chain, with the following sub-steps in detail.

[0114] A protection efficiency threshold is preset. When the calculated protection efficiency index Pe is greater than or equal to the protection efficiency threshold, it is marked that the data protection capability of the blockchain-based information management system 100 is reliable.

[0115] When the calculated protection efficiency index Pe is less than the protection efficiency threshold, it is marked that the data protection capability of the blockchain-based information management system 100 is unreliable.

[0116] The data marked as having reliable data protection capabilities of the blockchain-based information management system 100 are packaged into blocks and connected into chains in chronological order, while the data marked as having unreliable data protection capabilities of the blockchain-based information management system 100 are eliminated.

[0117] This application establishes a blockchain network, uses distributed computing to build a blockchain-based information management system 100, and combines privacy computing to strengthen the original data protection capability of the blockchain-based information management system 100 to ensure the privacy and security of the original data. It can not only cancel the central server of the centralized information management system and avoid data synchronization delays and data conflicts caused by network delays and bandwidth limitations when colleges and universities expand enrollment to remote campuses or remote branches, but also ensure the security of data during transmission and storage in terms of encryption mechanisms and decentralized authority management, while ensuring data transparency and security, and improving the protection of original data.

[0118] The above-mentioned formulas contained in this specific implementation manner are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0119] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

[0120] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. The information management system based on blockchain is characterized by: include: Data collection module, student work management module, financial management module, protection module, efficiency inspection module and verification storage module; The data collection module is used to collect student affairs information, operation information, and scientific research information, and pre-process the student affairs information, operation information, and scientific research information and send them to the student affairs management module, financial management module, and protection module respectively; The student work management module is used to process student work information based on homomorphic encryption technology, analyze students' personal information, and evaluate the protection efficiency of student work information; The financial management module is used to process operational information based on secure multi-party computing technology, analyze financial status, and evaluate the protection efficiency of operational information; The protection module is used to process scientific research information according to the federated learning algorithm, conduct cooperative management of scientific research raw data, and evaluate the protection efficiency of scientific research information; The efficiency test module is used to evaluate the privacy protection capability of the blockchain-based information management system by combining the protection efficiency of student information, operational information, and scientific research information; The verification storage module is used to perform reliability analysis on data based on privacy protection capabilities and to store verified data in a chained manner.

2. The information management system based on blockchain according to claim 1 is characterized in that: Student information includes students’ identity information, educational experience, physical examination data, course schedule, tuition data, grade information, and attendance information; Operational information includes asset data, meeting schedules, fund usage, and salary information; Scientific research information includes project approval, scientific research reports, scientific research papers, R&D patents, and scientific research awards.

3. The information management system based on blockchain according to claim 2 is characterized in that: The specific processing of student information based on homomorphic encryption technology is as follows: Generate a public key for encryption and a private key for decryption, and send the private key to the teacher; Teachers use public keys to encrypt student information and store the encrypted student information on the blockchain; The encrypted student information is subjected to homomorphic operations to analyze and process the information, and the analysis and processing results are stored in encrypted form on the blockchain.

4. The information management system based on blockchain according to claim 3 is characterized in that: The specific evaluation of the protection efficiency of student information is as follows: Calibration encryption performance coefficient Ep, encryption performance coefficient In the formula, Et is the encryption time, Dt is the decryption time, and Ho is the batch homomorphic operation time; Select a set of student information data, record the current time T1 before starting the encryption operation, perform homomorphic encryption on the student information data, and after the encryption operation is completed, record the current time T2, and the encryption time Et = T2-T1; Using the homomorphically encrypted student information data, before starting the decryption operation, record the current time T3 and decrypt the encrypted data. After the decryption operation is completed, record the current time T4, and the decryption time Dt = T4-T3; Select multiple groups of encrypted student information data, record the current time T5 before starting batch homomorphic operations, perform a batch of homomorphic operations, and after the batch homomorphic operations are completed, record the current time T6, and the batch homomorphic operation time Ho = T6-T5.

5. The information management system based on blockchain according to claim 2 is characterized in that: The specific processing of operational information based on secure multi-party computing technology is as follows: Deploy secure multi-party computing nodes based on the operational information independently stored by each remote campus or branch; Each remote campus or branch uses different encryption algorithms to encrypt the operational information, generate a hash value for the encrypted data, and record the hash value on the blockchain; Using threshold encryption technology, key fragments are shared by different remote campuses or branches; Each remote campus or branch jointly sets financial audit objectives, determines calculation functions and required data; The secure multi-party computing nodes of each remote campus or remote branch participate in the distributed computing in an encrypted state and transmit the computing results between the secure multi-party computing nodes; Each remote campus or branch verifies the hash value of the calculation result to check whether the calculation result has been tampered with; The calculation results are decrypted jointly by the remote campuses or remote branches according to the threshold encryption scheme.

6. The information management system based on blockchain according to claim 5 is characterized in that: The specific evaluation of the protection efficiency of operational information is as follows: The protection efficiency of operational information is evaluated by cross-validating secure multi-party computations. The specific method is as follows: The test coefficient of the independent sample t is calibrated as tc, and the operation data of each remote campus or remote branch is divided into multiple subsets. An independent secure multi-party computation is performed on each subset to generate the audit results of each subset. Conduct independent sample t-test on the audit results of each subset, select two subset samples, and set two hypotheses: hypothesis one is that the means of the two independent samples are equal, and hypothesis two is that the means of the two independent samples are not equal; According to the formula Calculate the t value, where and are the means of two independent samples, and are the variances of two independent samples, n1 and n2 are the sample sizes of two independent samples; According to the formula Calculate the degrees of freedom df for independent samples; Set the significance level and degrees of freedom, and find the critical value from the t distribution table; Calculate the p-value corresponding to the t-value and compare the p-value with the set significance level; When the p-value is less than the set significance level, hypothesis one is rejected, indicating that the means of the two independent samples are different. When the p-value is greater than or equal to the significance level, hypothesis one is not rejected, indicating that the means of the two independent samples are the same. When hypothesis one is rejected and the means of the two independent samples are different, the label test coefficient tc is 1. When hypothesis one is not rejected and the means of the two independent samples are the same, the label test coefficient tc is 0.

7. The information management system based on blockchain according to claim 2 is characterized in that: The specific processing of scientific research information according to the federated learning algorithm is as follows: Deploy federated learning nodes based on the scientific research information independently stored in each remote campus or branch, and the federated learning nodes together form a federated learning network; A federated learning node serves as a central coordination server to initialize a global model and broadcast the model parameters to the federated learning nodes of each remote campus or branch. Each remote campus or branch uses local scientific research information data to train the global model based on the initial model parameters; Each remote campus or branch encrypts the model parameters updated after local training and transmits them to the central coordination server through the blockchain network; The central coordination server collects the encrypted model parameters uploaded by each remote campus or branch, aggregates and updates them, and broadcasts the updated global model parameters to each remote campus or branch again to enter the next training round; Repeat the process of local model training, parameter encryption and transmission, and global model update until the global model converges or reaches the preset training rounds; After the training is completed, the central coordination server will publish the final global model to the blockchain network, and researchers in different campuses or branches can use the updated global model for data analysis and scientific research applications.

8. The information management system based on blockchain according to claim 7 is characterized in that: The specific evaluation of the protection efficiency of scientific research information is as follows: Evaluate the efficiency of protecting scientific research information using federated learning methods, which can be expressed through the performance of the training model and the calculation of the scientific research privacy coefficient Re, the scientific research privacy coefficient Where F1 is the harmonic mean of precision and recall, Auc is the area under the ROC curve, and Log is the logarithmic loss.

9. The information management system based on blockchain according to claim 8 is characterized in that: The privacy protection capability of the blockchain-based information management system is evaluated based on the protection efficiency of student information, operational information, and scientific research information. The privacy protection capability is evaluated by calculating the protection efficiency index Pe of the information management system based on blockchain. In the formula, α, β, and γ are the weight coefficients of the encryption performance coefficient, the verification coefficient tc, and the scientific research privacy coefficient, respectively, and α, β, and γ are all positive numbers.

10. The information management system based on blockchain according to claim 9 is characterized in that: The reliability of the data is analyzed based on the privacy protection capability, and the verified data is stored in a chain as follows: A protection efficiency threshold is preset. When the calculated protection efficiency index Pe is greater than or equal to the protection efficiency threshold, it is marked that the data protection capability of the blockchain-based information management system is reliable; When the calculated protection efficiency index Pe is less than the protection efficiency threshold, the data protection capability of the blockchain-based information management system is unreliable; The data marked as having reliable data protection capabilities of the information management system will be packaged into blocks and connected into chains in chronological order, while the data marked as having unreliable data protection capabilities of the blockchain-based information management system will be eliminated.

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