A blockchain-based laboratory record research data audit security management method

Abstract

The present invention discloses a laboratory record book scientific research data audit security management method based on blockchain, which relates to the field of data processing technology. The method includes: initializing the alliance blockchain; storing the experimental data hash value on the alliance blockchain, reading the laboratory record book scientific research data, and performing confidential data discrimination to obtain data confidentiality labeling information; when it is non-confidential scientific research data, auditing to obtain multiple non-confidential data audit behavior results; when it is confidential scientific research data, calling the range management contract to obtain the parameter value range; according to the parameter value range and the scientific research data parameters, generating a zero-knowledge range proof; verifying the laboratory record book scientific research data, obtaining multiple confidential data audit behavior results, and recording on the chain. It solves the technical problem of the lack of hierarchical audit of different scientific research data in the laboratory record book in the prior art, and achieves the technical effect of improving the security of data management through hierarchical audit of data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a blockchain-based laboratory record and scientific research data audit security management method. Background Art

[0002] As a widely used tool for researchers, electronic laboratory notebooks need to record experimental data and processes in a timely, accurate and complete manner, so they have high requirements for the security and tamper-proofness of the storage environment. Blockchain technology, with its decentralized distributed ledger technology characteristics, ensures the security and credibility of data and the traceability of its source through cryptography and consensus algorithms. Therefore, many existing laboratory notebooks use blockchain as an infrastructure to store scientific research data. Due to the requirements of scientific research institutions and the characteristics of scientific research data themselves, laboratory managers and supervisory audit units need to audit the on-chain data to ensure its integrity and authenticity. Although the existing technology has solved the privacy problem of data auditing well through privacy computing, it has not considered the confidentiality level of on-chain data in actual scientific research application scenarios; in addition, due to the hierarchical characteristics of laboratory notebooks used by multiple institutions and laboratories, hierarchical audits are required for the internal administrator permissions of the institute and the workflow of other auditors. There is currently no audit system for this. Summary of the invention

[0003] This application provides a blockchain-based laboratory notebook scientific research data audit security management method, which solves the technical problem of the lack of hierarchical audit of different scientific research data in laboratory notebooks in the prior art.

[0004] In view of the above problems, this application provides a blockchain-based laboratory record and scientific research data audit security management method.

[0005] This application provides a blockchain-based laboratory record and scientific research data audit security management method, the method comprising:

[0006] Initialize the alliance blockchain, the alliance blockchain includes multiple proof nodes, multiple verification nodes and blockchain account books, wherein the multiple proof nodes and multiple verification nodes include experimental audit contracts, proof contracts, verification contracts, and scope management contracts; store the experimental data hash value of the laboratory record of scientific research data on the alliance blockchain through the laboratory record platform, read the laboratory record of scientific research data based on the multiple proof nodes, and perform confidential data identification on the laboratory record of scientific research data to obtain data confidentiality labeling information; when the data confidentiality labeling information is non-confidential scientific research data, audit the experimental data hash value through the multiple verification nodes to obtain multiple non-confidential data audit behavior results, and audit the multiple non-confidential data based on the alliance blockchain. The audit behavior results are recorded on the chain; when the data confidentiality annotation information is confidential scientific research data, the range management contract is called by the multiple proof nodes to obtain the parameter value range of the laboratory record of the scientific research data; based on the parameter value range, the laboratory record of the scientific research data is pre-processed to obtain the scientific research data parameters of the proof contract, and according to the parameter value range and the scientific research data parameters, a zero-knowledge range proof is generated, and the zero-knowledge range proof is uploaded to the blockchain account book; the verification contract is called by the multiple verification nodes to download the zero-knowledge range proof from the blockchain account book and verify the laboratory record of the scientific research data, to obtain multiple confidential data audit behavior results, and the multiple confidential data audit behavior results are recorded on the chain based on the alliance blockchain.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, the alliance blockchain is initialized. The alliance blockchain includes multiple proof nodes, multiple verification nodes and blockchain ledgers, wherein the multiple proof nodes and multiple verification nodes include experimental audit contracts, proof contracts, verification contracts and scope management contracts. Then, the experimental data hash value of the laboratory record of the scientific research data is stored on the alliance blockchain through the laboratory record platform, the laboratory record of the scientific research data is read based on multiple proof nodes, and the laboratory record of the scientific research data is discriminated as confidential data to obtain data confidentiality labeling information. When the data confidentiality labeling information is non-confidential scientific research data, the experimental audit contract is called by multiple verification nodes to audit the experimental data hash value, and multiple non-confidential data audit behavior results are obtained, and the multiple non-confidential data audit behavior results are recorded on the chain based on the alliance blockchain; when the data confidentiality labeling information is confidential scientific research data, the scope management contract is called by multiple proof nodes to obtain the parameter value range of the laboratory record of the scientific research data. Further, the laboratory record of the scientific research data is pre-processed based on the parameter value range, and the scientific research data parameters of the proof contract are obtained. According to the parameter value range and the scientific research data parameters, a zero-knowledge range proof is generated, and the zero-knowledge range proof is uploaded to the blockchain ledger. Finally, multiple verification nodes call the verification contract to download the zero-knowledge range proof from the blockchain account book and verify the scientific research data in the laboratory notebook, obtain multiple confidential data audit behavior results, and record the multiple confidential data audit behavior results on the chain based on the alliance blockchain. This solves the technical problem of the lack of hierarchical audit of different scientific research data in the laboratory notebook in the existing technology, and achieves the technical effect of improving the security of data management through hierarchical audit of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flowchart of a blockchain-based laboratory record and scientific research data audit security management method provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the process of initializing the alliance blockchain in the blockchain-based laboratory record and scientific research data audit security management method provided in the embodiment of the present application. DETAILED DESCRIPTION

[0012] This application solves the technical problem of the lack of hierarchical auditing of different scientific research data in laboratory notebooks in the prior art by providing a blockchain-based laboratory notebook scientific research data audit security management method.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" 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 are inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the embodiment of the present application provides a blockchain-based laboratory record scientific research data audit security management method, wherein the method includes:

[0016] Initialize a consortium blockchain, wherein the consortium blockchain includes a plurality of proof nodes, a plurality of verification nodes, and a blockchain ledger, wherein the plurality of proof nodes and the plurality of verification nodes include an experimental audit contract, a proof contract, a verification contract, and a scope management contract.

[0017] By initializing the alliance blockchain, multiple proof nodes, multiple verification nodes and blockchain ledgers can be configured on the alliance blockchain, and necessary smart contracts can be deployed to support the audit and verification of scientific research data. Among them, the proof node refers to the experimental recorder who presents his own evidence to other auditors; the verification node refers to the institute review manager, publishing auditor and regulatory auditor who receive the evidence of the proof node and verify it to complete the data audit process; both the proof node and the verification node contain smart contracts, and the smart contract for non-confidential data audit includes the experimental audit contract, and the smart contract for confidential data audit includes the proof contract, verification contract and scope management contract; the blockchain ledger is used to store scientific research data and the verification information of the corresponding personnel.

[0018] Furthermore, if Figure 2 As shown, the initialization of the alliance blockchain includes:

[0019] Establishing a consortium network, the consortium network is composed of the multiple proof nodes and the multiple verification nodes, and setting the experimental audit contract, proof contract, verification contract, and scope management contract on the network nodes of the consortium network; calling the scope management contract through the multiple verification nodes to initialize parameters, determine the scope binary tuple and primary key ID information, and upload the scope binary tuple and primary key ID information to the blockchain account book; initializing and setting the scope identifier and the proof identifier according to the scope binary tuple, and obtaining the consortium blockchain based on the consortium network, the blockchain account book, the scope identifier and the proof identifier.

[0020] The alliance network is formed by configuring multiple verification nodes and multiple validation nodes, and deploying experimental audit contracts, proof contracts, verification contracts and scope management contracts on each node of the alliance network. These contracts will serve as the basis for executing logic and rules on the blockchain. Multiple verification nodes call the scope management contract to initialize parameters, determine the scope binary tuple and primary key ID information, and upload it to the blockchain ledger to ensure the immutability and traceability of this information. Specifically, the verification node calls the scope management contract with a scope binary tuple.<a,b> The upper and lower limits of the range are determined in the form of , where a represents the lower limit of the range and b represents the upper limit of the range, and the binary tuple is uploaded to the blockchain ledger. At the same time, the verification node also uploads the prover's primary key ProverKeyID to the blockchain by setting the primary key SetupKeyID. Based on the range binary tuple and primary key ID information uploaded to the blockchain ledger, a range identifier is generated; similarly, a proof identifier is generated for the zero-knowledge proof, that is, at the time of initialization, the range identifier RangeID and the proof identifier ProofID are set for the range. The verification node can upload and download the range at the specified location according to the range identifier, and can also upload and download the zero-knowledge proof at the specified location according to the proof identifier. By establishing a consortium network, setting the necessary smart contracts and parameters on the consortium network, and setting the range identifier and proof identifier on the range management contract, a consortium blockchain can be finally obtained, thereby laying a solid foundation for subsequent scientific research data auditing and verification work.

[0021] The experimental data hash value of the laboratory record data is stored on the alliance blockchain through the laboratory record platform, the laboratory record data is read based on the multiple proof nodes, and the laboratory record data is identified as confidential data to obtain data confidentiality labeling information.

[0022] After completing the experiment, the experimental recorder uploads the experimental data to the laboratory notebook, and the original data is stored in the local server of each research institute. The laboratory notebook platform will use a hash algorithm to hash the experimental data to generate a unique hash value. This hash value represents the digital fingerprint of the data and is used to verify the integrity and uniqueness of the data. The generated hash value is stored on the alliance blockchain; multiple proof nodes in the alliance blockchain read the laboratory notebook scientific research data corresponding to the hash value from the blockchain ledger, and perform confidentiality data judgment on the laboratory notebook scientific research data, thereby obtaining data confidentiality labeling information. Specifically, multiple proof nodes, that is, multiple experimental recorders, can mark whether the experimental data is confidential data, and upload the marked experimental data back to the alliance blockchain, and then obtain data confidentiality labeling information, that is, whether the data is confidential data or non-confidential data.

[0023] When the data confidentiality label information is non-confidential scientific research data, the experimental data hash value is audited by calling the experimental audit contract through the multiple verification nodes to obtain multiple non-confidential data audit behavior results, and the multiple non-confidential data audit behavior results are recorded on the chain based on the alliance blockchain.

[0024] When the data confidentiality label information indicates that it is non-confidential scientific research data, the experimental audit contract is called through multiple verification nodes in the alliance blockchain to audit the hash values ​​of these non-confidential scientific research data. The experimental audit contract contains predefined audit rules and logic for verifying the authenticity and integrity of the experimental data. The experimental audit contract receives the hash value of the experimental data as input and executes the corresponding audit logic to confirm whether the hash value matches the original hash value stored on the blockchain and whether other predefined audit conditions are met. After the experimental audit contract is executed, multiple non-confidential data audit behavior results will be generated, including audit pass, audit failure, data anomaly and other status information, and multiple non-confidential data audit behavior results will be recorded on the chain.

[0025] Furthermore, the obtaining of multiple non-confidential data audit behavior results includes:

[0026] Calculate and obtain the audit data hash value of the laboratory's record of the scientific research data, and compare and judge the audit data hash value with the experimental data hash value to obtain multiple experimental integrity audit results of the multiple verification nodes; record multiple timestamps of the multiple experimental integrity audit results, and obtain multiple audit verification identifiers based on the multiple experimental integrity audit results and the multiple timestamps; based on the multiple experimental integrity audit results and the multiple audit verification identifiers, obtain the multiple non-confidential data audit behavior results.

[0027] Preferably, scientific research data to be audited is obtained from the alliance blockchain, and the same hash algorithm as the experimental data hash value is used to perform hash calculation on the data involved in the audit process to obtain the audit data hash value, and the calculated audit data hash value is compared with the experimental data hash value originally stored on the blockchain. If the two hash values ​​are the same, it means that the audit data has not been tampered with during the audit process and the experimental data remains intact; if they are different, it may indicate that there is data tampering or an error in the audit process, thereby obtaining multiple experimental integrity audit results of multiple verification nodes; for each audit operation of each verification node, its start time, end time and the time point of any key operation are recorded, and the timestamp information is stored on the blockchain to ensure its non-tamperability and traceability; based on the experimental integrity audit results, timestamps, and identity information of the verification nodes, etc., multiple audit verification identifiers are generated, which are unique and verifiable, so that the audit results can be queried and verified later; the audit verification identifier is associated with the experimental integrity audit results to form multiple non-confidential data audit behavior results.

[0028] When the data confidentiality label information is confidential scientific research data, the range management contract is called through the multiple proof nodes to obtain the parameter value range of the laboratory recording the scientific research data.

[0029] When the data confidentiality label information is confidential scientific research data, the range management contract is called through multiple proof nodes in the alliance blockchain to obtain the parameter value range of the laboratory record of this scientific research data. Among them, the range management contract is a smart contract on the alliance blockchain, which is used to manage and query the parameter value range of the data. Specifically, the range management contract can retrieve and return the corresponding parameter value range according to the query parameters provided by the proof node (such as data name, type, project, etc.), including numerical range, date range, string matching pattern, etc.

[0030] The laboratory records the scientific research data based on the parameter value range, obtains the scientific research data parameters of the proof contract, generates a zero-knowledge range proof based on the parameter value range and the scientific research data parameters, and uploads the zero-knowledge range proof to the blockchain account book.

[0031] Furthermore, the obtaining of the scientific research data parameters of the certification contract includes:

[0032] Determine the parameter value range. When the parameter value range is a standard range, perform a first preprocessing on the laboratory record of the scientific research data to generate a standard aggregation range certificate. When the parameter value range is an arbitrary range, perform a second preprocessing on the laboratory record of the scientific research data to generate an arbitrary aggregation range certificate. According to the standard aggregation range certificate and the arbitrary aggregation range certificate, obtain the scientific research data parameters of the certification contract.

[0033] Specifically, the parameter value range obtained from the range management contract is analyzed to determine whether it is a standard range. For example, if the value range v i ∈[0,2), (i=1,2,...,m), it means that the range of scientific data belongs to the standard range; if the value range is v i ∈[a,b), (i=1,2,...,m), then it means that the range of scientific data belongs to an arbitrary range. When the parameter value range is the standard range, the laboratory record scientific research data is first preprocessed to generate a standard aggregation range proof, which verifies that the parameter value of the data is indeed within the standard range without leaking any specific information about the data. When the parameter value range is an arbitrary range, the laboratory record scientific research data is second preprocessed to generate an arbitrary aggregation range proof, which verifies that the parameter value of the data is within the specified arbitrary range while maintaining the privacy of the data. The standard aggregation range proof and the arbitrary aggregation range proof together constitute the scientific research data parameters of the proof contract. Next, a non-interactive zero-knowledge proof Bulletproofs is used to construct a zero-knowledge range proof based on the parameter value range and the scientific research data parameters. The zero-knowledge range proof will prove that the value of the scientific research data parameter is indeed within the specified parameter value range without leaking any additional information about the data itself, and the zero-knowledge range proof is uploaded to the blockchain ledger to provide any node with appropriate permissions for verification.

[0034] Furthermore, generating a standard aggregate range certificate includes:

[0035] When the value range is the standard range, a first commitment random number is selected as a blinding factor to calculate the first Pedersen commitment of the laboratory record of the scientific research data; the laboratory record of the scientific research data is converted into a binary vector and a complement vector, and a second commitment random number is selected as a blinding factor to calculate the second Pedersen commitment of the binary vector and the complement vector; the first Pedersen commitment and the second Pedersen commitment are packaged to obtain multiple standard range certificates; the multiple standard range certificates are committed to aggregate to generate a standard aggregate value commitment and a standard aggregate vector commitment; the standard aggregate value blinding factor and the standard aggregate vector blinding factor are calculated to generate, and the aggregate range certificate is generated according to the standard aggregate value blinding factor and the standard aggregate vector blinding factor, as well as the standard aggregate value commitment and the standard aggregate vector commitment.

[0036] When the parameter value range is the standard range, the method of generating the standard range proof is as follows: select a commitment random number (first commitment random number) r as the blinding factor, calculate the first Pedersen commitment of the laboratory to record the scientific research data, Com(v) = v·g+r·h, where Com(v) represents the first Pedersen commitment of the laboratory to record the scientific research data, r represents the blinding factor, g and h represent the generators on the elliptic curve, and v represents the value of the laboratory to record the scientific research data; convert v into a binary vector representation l and its complement vector R, where l = (v0, v1,…, v n-1 ), v i is the binary digit of v, R=(1-v0,1-v1,…,1-v n-1 ); Select another vector commitment random number (second commitment random number) r l As a blinding factor, the second Pedersen commitment of the binary vector l and the complement vector R is calculated, Com(l,R) = l·G+R·H+r l h, where Com(l,R) represents the second Pedersen commitment of a binary vector l and a complement vector R, r l represents the blinding factor, G and H represent the generator vector of the elliptic curve; package Com(v) and Com(l,R) and their parameters to obtain the standard range proof. Furthermore, in order to improve the verification efficiency in the data audit process, reduce the storage of on-chain data, and facilitate the verification of multiple data, multiple standard range proofs are aggregated into zero-knowledge proofs. The way to aggregate multiple standard range proofs is as follows: Assume that there are m standard range proofs that need to be aggregated, each proof consists of (Com(v i ),Com(l i ,R i ),r i ,r li ), ri Represents any first commitment random number, r li Represents any second commitment random number, v i Characterizes the value of any laboratory recording scientific research data, l i and R i Respectively represent the binary vector and complement vector of any laboratory recording the research data; aggregate the commitments of m proofs to generate a standard aggregate value commitment Com(v agg ,r agg ) and the standard aggregation vector commitment Com(l agg ,R agg ,r lagg ), where v agg The value that represents the aggregated laboratory record of this research data, r agg Characterizes the blinding factor of the standard aggregate value, l agg Represents the aggregated binary vector, R agg Represents the complement vector after aggregation, r lagg Characterizes the standard aggregation vector blinding factor.

[0037] The standard aggregate value commitment formula and the standard aggregate vector commitment formula are as follows:

[0038]

[0039] Standard aggregate value blinding factor formula: Standard aggregation vector blinding factor formula: Generate an aggregate range proof based on the standard aggregate value blinding factor and the standard aggregate vector blinding factor, as well as the standard aggregate value commitment and the standard aggregate vector commitment.

[0040] Furthermore, generating an arbitrary aggregate range proof includes:

[0041] When the value range is an arbitrary range, the laboratory record of the scientific research data is range converted to generate first scientific research data and second scientific research data; the first random number and the second random number are selected as blinding factors, and the first data Pedersen commitment and the second data Pedersen commitment of the first scientific research data and the second scientific research data are respectively calculated to obtain the first data Pedersen commitment and the second data Pedersen commitment of the first scientific research data and the second scientific research data; the first scientific research data and the second scientific research data are converted into a binary vector and a complement vector to obtain a first vector representation and a second vector representation; the first vector commitment random number and the second vector commitment random number are selected as blinding factors, and the first vector Pedersen commitment of the first vector representation and the second vector Pedersen commitment of the second vector representation are respectively calculated to obtain; the first data Pedersen commitment and the second data Pedersen commitment, as well as the first vector Pedersen commitment and the second vector Pedersen commitment, are packaged to generate multiple arbitrary range certificates; the multiple arbitrary range certificates are committed to aggregate to generate arbitrary aggregate value commitment and arbitrary aggregate vector commitment; arbitrary aggregate value blinding factor and arbitrary aggregate vector blinding factor are respectively calculated to generate, and the arbitrary aggregate range certificate is generated according to the arbitrary aggregate value blinding factor and arbitrary aggregate vector blinding factor, as well as the aggregate value commitment and aggregate vector commitment.

[0042] When the parameter value range is a random range v∈[a,b], the method to generate an arbitrary range proof is: perform range conversion on the laboratory record of the scientific research data v, v′1=va, v′2=v-b+2 n , so that v′1∈[0,2 n ],v′2∈[0,2 n ], v′1 represents the first scientific research data, v′2 represents the second scientific research data; select the first random number r1 and the second random number r2 as blinding factors, and calculate the first data Pedersen commitment Com(v′1) and the second data Pedersen commitment Com(v′2) of the first scientific research data and the second scientific research data respectively, Com(v′1)=v′1·g+r1·h, Com(v′2)=v′2·g+r2·h, where g and h represent the generators on the elliptic curve. Convert the first scientific research data v′1 and the second scientific research data v′2 into binary vectors l1, l2 and complement vectors R1, R2, where: l1=(v′ 1,0 ,v′ 1,1 ,…,v′ 1,n-1 ), l2=(v′ 2,0 ,v′ 2,1 ,…,v′ 2,n-1 ), R1=(1-v′ 1,0 ,1-v′ 1,1 ,…,1-v′ 1,n-1), R2=(1-v′ 2,0 ,1-v′ 2,1 ,…,1-v′ 2,n-1 );Select the first vector commitment random number r l1 and the second vector commitment random number r l2 As blinding factors, a first vector Pedersen commitment Com(l1, R1) represented by the first vector and a second vector Pedersen commitment Com(l2, R2) represented by the second vector are respectively calculated to obtain.

[0043] The first vector Pedersen commitment formula and the second vector Pedersen commitment formula are as follows:

[0044]

[0045] Among them, G i and H i is the generator vector of the elliptic curve; the first data Pedersen commitment Com(v′1) and the second data Pedersen commitment Com(v′2), as well as the first vector Pedersen commitment Com(l1,R1) and the second vector Pedersen commitment Com(l2,R2) are packaged to generate multiple arbitrary range proofs; multiple arbitrary range proofs are committed and aggregated to generate arbitrary aggregate value commitments and arbitrary aggregate vector commitments. Specifically, assuming that there are m arbitrary range proofs that need to be aggregated, each proof consists of

[0046] (Com(v′ 1,i ),Com(l 1,i ,R 1,i ),Com(v′ 2,i ),Com(l 2,i ,R 2,i ),r 1,i ,r 2,i ,r l1,i ,r l2,i ), aggregate the commitments of m proofs to generate an arbitrary aggregate value commitment Com(v′ 1,agg ,r 1,agg )、Com(v′ 2,agg ,r 2,agg ) and any aggregate vector commitment Com(l 1,agg ,R 1,agg ,r l1,agg ) and Com(l 2,agg ,R 2,agg ,r l2,agg ).

[0047] The formula for any aggregate value commitment is as follows:

[0048]

[0049] The arbitrary aggregation vector commitment formula is as follows:

[0050]

[0051] Calculate and generate the blinding factor of any aggregate value separately and any aggregate vector blinding factor Generate an arbitrary aggregate range proof based on an arbitrary aggregate value blinding factor and an arbitrary aggregate vector blinding factor, as well as an aggregate value commitment and an aggregate vector commitment.

[0052] The verification contract is called by the multiple verification nodes to download the zero-knowledge range proof from the blockchain account book and verify the laboratory's recorded scientific research data, obtain multiple confidential data audit behavior results, and record the multiple confidential data audit behavior results on the chain based on the alliance blockchain.

[0053] Specifically, the verification contract is called through multiple verification nodes to download the zero-knowledge range proof from the blockchain ledger and verify the laboratory record scientific research data. The verification contract is a smart contract deployed on the blockchain, which is used to call the zero-knowledge range proof to verify the laboratory record scientific research data stored in the blockchain ledger. After verification, multiple confidential data audit behavior results can be obtained and recorded on the chain. The confidential data audit behavior results are used to indicate whether the data has passed the verification without leaking any sensitive information about the data.

[0054] Furthermore, the obtaining of multiple confidential data audit behavior results includes:

[0055] When the data confidentiality label information is confidential scientific research data, the roles of the multiple verification nodes are identified to obtain the verification role information; if the verification role information is the reviewer of the research institute, the experimental audit contract is called to audit the hash value of the experimental data to obtain the multiple confidential data audit behavior results; if the verification role information is the publishing auditor and the regulatory auditor, the zero-knowledge range proof is downloaded from the blockchain account book and the laboratory record of the scientific research data is verified to obtain the multiple confidential data audit behavior results.

[0056] When the data confidentiality label information is confidential scientific research data, the verification role information is obtained by performing role identification on multiple verification nodes; if the verification role information is the reviewer of the research institute, the experimental audit contract is directly called to audit the hash value of the experimental data to obtain multiple confidential data audit behavior results; if the verification role information is the publishing auditor and the regulatory auditor, the zero-knowledge range proof is downloaded from the blockchain ledger and the laboratory record of the scientific research data is verified to obtain multiple confidential data audit behavior results.

[0057] Furthermore, the obtaining of multiple confidential data audit behavior results includes:

[0058] Check whether the experimental audit contract, proof contract, verification contract, scope management contract, and the primary key ID information and the parameter value range have been modified. If modified, the verification fails; if not modified, the verification contract is called by the multiple verification nodes to download the zero-knowledge range proof from the blockchain account book to verify the laboratory's scientific research data, and obtain the multiple confidential data audit behavior results.

[0059] Download the zero-knowledge range proof from the blockchain account book and verify the laboratory's scientific research data. Specifically, it is necessary to check whether the experimental audit contract, proof contract, verification contract, range management contract, and the primary key ID information and the parameter value range have been modified; if any modification or inconsistency is found in any of the above checks, the verification is deemed to have failed; if all checks pass, that is, no modification or inconsistency is found, the verification process will continue to be executed, and the verification contract will be called through multiple verification nodes. The zero-knowledge range proof is downloaded from the blockchain account book to verify the laboratory's scientific research data. Based on the verification results, multiple confidential data audit behavior results are generated, which will record in detail the findings during the verification process, including whether the data has passed the verification, any potential anomalies or violations, etc.

[0060] Furthermore, the method further comprises:

[0061] The trusted factor information of the verifier is obtained, wherein the trusted factor information of the verifier includes professional ability, historical experience and verification security; based on the trusted factor information of the verifier, the multiple verification nodes are assigned credibility assessment to obtain multiple verifier credibility; based on the multiple verifier credibility, the multiple confidential data audit behavior results are corrected for credibility.

[0062] Preferably, multiple verification nodes are evaluated for credibility by obtaining the verifier's credible factor information, and multiple verification party credibility is obtained. Specifically, the verifier's credible factor information includes professional ability (verifier's knowledge, skills and qualifications in related fields), historical experience (verifier's performance in similar projects or tasks in the past) and verification security (security standards and measures followed by the verifier in data processing, storage and transmission, including data encryption, access control, firewall settings, etc.); by assigning weights to each verifier's credible factor information, and giving scores according to the verifier's performance on these factors, these scores are weighted and summed to obtain the total credibility score of each verification node, that is, the verifier's credibility. According to the credibility of multiple verifiers, the credibility of multiple confidential data audit behavior results is corrected, and the audit behavior results provided by each verification node can be weighted averaged according to the credibility value of each verification node. Verification nodes with higher credibility will have a greater impact on their results. Through the above process, the credibility of the confidential data audit behavior results can be more accurately reflected, thereby providing a more reliable reference basis.

[0063] In summary, the embodiments of the present application have at least the following technical effects:

[0064] First, the alliance blockchain is initialized. The alliance blockchain includes multiple proof nodes, multiple verification nodes and blockchain ledgers, wherein the multiple proof nodes and multiple verification nodes include experimental audit contracts, proof contracts, verification contracts and scope management contracts. Then, the experimental data hash value of the laboratory record of the scientific research data is stored on the alliance blockchain through the laboratory record platform, the laboratory record of the scientific research data is read based on multiple proof nodes, and the laboratory record of the scientific research data is discriminated as confidential data to obtain data confidentiality labeling information. When the data confidentiality labeling information is non-confidential scientific research data, the experimental audit contract is called by multiple verification nodes to audit the experimental data hash value, and multiple non-confidential data audit behavior results are obtained, and the multiple non-confidential data audit behavior results are recorded on the chain based on the alliance blockchain; when the data confidentiality labeling information is confidential scientific research data, the scope management contract is called by multiple proof nodes to obtain the parameter value range of the laboratory record of the scientific research data. Further, the laboratory record of the scientific research data is pre-processed based on the parameter value range, and the scientific research data parameters of the proof contract are obtained. According to the parameter value range and the scientific research data parameters, a zero-knowledge range proof is generated, and the zero-knowledge range proof is uploaded to the blockchain ledger. Finally, multiple verification nodes call the verification contract to download the zero-knowledge range proof from the blockchain account book and verify the scientific research data in the laboratory notebook, obtain multiple confidential data audit behavior results, and record the multiple confidential data audit behavior results on the chain based on the alliance blockchain. This solves the technical problem of the lack of hierarchical audit of different scientific research data in the laboratory notebook in the existing technology, and achieves the technical effect of improving the security of data management through hierarchical audit of data.

[0065] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0067] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations 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 equivalents, the present application intends to include these modifications and variations.

Claims

1. A blockchain-based laboratory record scientific research data audit security management method, characterized by: The method comprises: Initialize a consortium blockchain, wherein the consortium blockchain includes a plurality of certification nodes, a plurality of verification nodes, and a blockchain ledger, wherein the plurality of certification nodes and the plurality of verification nodes include an experimental audit contract, a certification contract, a verification contract, and a scope management contract; The experimental data hash value of the laboratory record data is stored on the alliance blockchain through the laboratory record platform, the laboratory record data is read based on the multiple proof nodes, and confidential data identification is performed on the laboratory record data to obtain data confidentiality labeling information; When the data confidentiality label information is non-confidential scientific research data, the experimental data hash value is audited by calling the experimental audit contract through the multiple verification nodes to obtain multiple non-confidential data audit behavior results, and the multiple non-confidential data audit behavior results are recorded on the chain based on the alliance blockchain; When the data confidentiality label information is confidential scientific research data, the range management contract is called through the multiple proof nodes to obtain the parameter value range of the laboratory recording the scientific research data; Preprocessing the laboratory record scientific research data based on the parameter value range, obtaining the scientific research data parameters of the proof contract, generating a zero-knowledge range proof according to the parameter value range and the scientific research data parameters, and uploading the zero-knowledge range proof to the blockchain account book; The verification contract is called by the multiple verification nodes to download the zero-knowledge range proof from the blockchain account book and verify the laboratory's recorded scientific research data, obtain multiple confidential data audit behavior results, and record the multiple confidential data audit behavior results on the chain based on the alliance blockchain.

2. The blockchain-based laboratory record scientific research data audit security management method according to claim 1, characterized in that: The initialization of the alliance blockchain includes: Establishing a consortium network, the consortium network is composed of the plurality of certification nodes and the plurality of verification nodes, and setting the experimental audit contract, the certification contract, the verification contract, and the scope management contract on the network nodes of the consortium network; Calling the range management contract through the multiple verification nodes to initialize parameters, determine the range binary tuple and primary key ID information, and upload the range binary tuple and primary key ID information to the blockchain account book; According to the scope binary tuple, a scope identifier and a proof identifier are initialized and set, and based on the consortium network, the blockchain ledger, the scope identifier and the proof identifier, the consortium blockchain is obtained.

3. The blockchain-based laboratory record scientific research data audit security management method according to claim 1, characterized in that: The obtaining of multiple non-confidential data audit behavior results includes: Calculate and obtain the audit data hash value of the laboratory's record of the scientific research data, and compare and determine the audit data hash value with the experimental data hash value to obtain multiple experimental integrity audit results of the multiple verification nodes; Recording multiple timestamps of the multiple experimental integrity audit results, and acquiring multiple audit verification identifiers based on the multiple experimental integrity audit results and the multiple timestamps; Based on the multiple experimental integrity audit results and the multiple audit verification identifiers, the multiple non-confidential data audit behavior results are obtained.

4. The blockchain-based laboratory record scientific research data audit security management method according to claim 1, characterized in that: The obtaining of multiple confidential data audit behavior results includes: When the data confidentiality label information is confidential scientific research data, role discrimination is performed on the multiple verification nodes to obtain verification role information; If the verification role information is a reviewer of the research institute, the experimental audit contract is called to audit the experimental data hash value to obtain the multiple confidential data audit behavior results; If the verification role information is a publishing auditor and a regulatory auditor, the zero-knowledge range proof is downloaded from the blockchain account book and the laboratory records the scientific research data to obtain the multiple confidential data audit behavior results.

5. The blockchain-based laboratory record scientific research data audit security management method according to claim 1, characterized in that: The obtaining of scientific research data parameters of the certification contract includes: Determine the parameter value range, and when the parameter value range is a standard range, perform a first preprocessing on the laboratory record of the scientific research data to generate a standard aggregation range certificate; When the parameter value range is an arbitrary range, performing a second preprocessing on the laboratory record scientific research data to generate an arbitrary aggregation range certificate; According to the standard aggregation range proof and the arbitrary aggregation range proof, the scientific research data parameters of the proof contract are obtained.

6. The blockchain-based laboratory record scientific research data audit security management method as claimed in claim 5, characterized in that: The generation of standard aggregate scope proofs includes: When the value range is the standard range, a first commitment random number is selected as a blinding factor to calculate the first Pedersen commitment of the laboratory to record the scientific research data; Convert the laboratory record data into a binary vector and a complement vector, select a second commitment random number as a blinding factor to calculate a second Pedersen commitment of the binary vector and the complement vector; Packing the first Pedersen commitment and the second Pedersen commitment to obtain multiple standard range proofs; Aggregating the multiple standard range proofs by commitment to generate a standard aggregate value commitment and a standard aggregate vector commitment; A standard aggregate value blinding factor and a standard aggregate vector blinding factor are calculated and generated, and the aggregate range proof is generated according to the standard aggregate value blinding factor and the standard aggregate vector blinding factor, as well as the standard aggregate value commitment and the standard aggregate vector commitment.

7. The blockchain-based laboratory record scientific research data audit security management method according to claim 5, characterized in that: The generation of arbitrary aggregate range proofs includes: When the value range is an arbitrary range, performing range conversion on the laboratory record scientific research data to generate first scientific research data and second scientific research data; Selecting a first random number and a second random number as blinding factors, and calculating and obtaining a first data Pedersen commitment and a second data Pedersen commitment of the first scientific research data and the second scientific research data, respectively; Convert the first scientific research data and the second scientific research data into a binary vector and a complement vector to obtain a first vector representation and a second vector representation; Selecting a first vector commitment random number and a second vector commitment random number as blinding factors, and respectively calculating to obtain a first vector Pedersen commitment represented by the first vector and a second vector Pedersen commitment represented by the second vector; Packing the first data Pedersen commitment and the second data Pedersen commitment, as well as the first vector Pedersen commitment and the second vector Pedersen commitment, to generate a plurality of arbitrary range proofs; Aggregate the multiple arbitrary range proofs by commitment to generate an arbitrary aggregate value commitment and an arbitrary aggregate vector commitment; An arbitrary aggregate value blinding factor and an arbitrary aggregate vector blinding factor are calculated and generated respectively, and the arbitrary aggregate range proof is generated according to the arbitrary aggregate value blinding factor and the arbitrary aggregate vector blinding factor, as well as the aggregate value commitment and the aggregate vector commitment.

8. The blockchain-based laboratory record scientific research data audit security management method as claimed in claim 2, characterized in that: The obtaining of multiple confidential data audit behavior results includes: Check whether the experimental audit contract, certification contract, verification contract, scope management contract, primary key ID information and parameter value range have been modified. If modified, verification fails; If not modified, the verification contract is called by the multiple verification nodes to download the zero-knowledge range proof from the blockchain account book to verify the laboratory's recorded scientific research data, and obtain the multiple confidential data audit behavior results.

9. The blockchain-based laboratory record scientific research data audit security management method according to claim 8, characterized in that: The method comprises: Acquire the trust factor information of the verification party, wherein the trust factor information of the verification party includes professional ability, historical experience and verification security; Based on the verifier trust factor information, the multiple verification nodes are evaluated for credibility, and the credibility of multiple verification parties is obtained; The credibility of the multiple confidential data audit behavior results is modified based on the credibility of the multiple verification parties.

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