Automatic artificial intelligence data sharing method and system based on block chain

Abstract

The invention discloses an automatic artificial intelligence data sharing method and system based on a block chain, and relates to the technical field of data sharing, and the method comprises the steps that a data provider uploads encrypted original data to an under-chain storage unit, an on-chain verification model directly operates on the encrypted data, and a validity verification result of the data is generated; after the validity verification is passed, an award is automatically issued to the data provider, and an access control smart contract is compiled for the data uploaded by the data provider; if the validity verification is not passed, the data uploaded by the data provider is prevented from being linked, and the encrypted data in the under-chain storage unit is cleared; after the data requester submits an access request, the on-chain data sharing center automatically shares data to the data requester based on the access control smart contract; key operation records on the chain are stored in real time, and audit reports are generated regularly according to the stored records, so that the compliance of the sharing process is ensured. The problems of data islands, privacy security, trust missing, low efficiency and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of data sharing technology, and in particular to an automated artificial intelligence data sharing method and system based on blockchain. Background Art

[0002] With the rapid development of artificial intelligence technology, data has become a core resource to promote AI model training and optimization. However, data sharing faces many challenges in practical applications: 1. Data island problem. Data between different institutions or individuals are often stored in isolation and lack effective sharing mechanisms, resulting in low utilization of data resources and difficulty in supporting cross-domain collaboration; 2. Privacy and security issues. Data sharing involves sensitive information (such as personal privacy and commercial secrets). Traditional centralized sharing methods have the risk of data leakage, tampering and abuse; 3. Manual operation is inefficient. Traditional data sharing processes rely on manual negotiation and approval, which is inefficient and difficult to meet large-scale, real-time data sharing needs; 4. Lack of incentive mechanism. Data providers lack effective incentives, resulting in insufficient willingness to share data, which limits the circulation and value mining of data resources. At present, some technologies have been tried to solve the above problems, but there are still limitations: 1. Centralized data sharing platforms rely on third-party intermediaries, have single point failure risks, and are difficult to ensure data transparency and immutability; 2. Traditional encryption technology, although it can protect data privacy, cannot solve the trust problem in the sharing process and lacks automated execution capabilities; 3. The initial application of blockchain technology, blockchain technology is attempted to be used for data sharing due to its decentralized, immutable and traceable characteristics. However, existing solutions are mostly limited to data storage and access control, lack of deep support for AI data sharing scenarios, especially in terms of automated execution, privacy protection and incentive mechanisms. In response to the above problems, the present invention proposes an automated artificial intelligence data sharing method and system based on blockchain, which aims to solve the problems of data islands, privacy security, lack of trust and inefficiency in the prior art, provide an efficient, secure and automated data sharing solution, promote the deep integration of artificial intelligence and blockchain technology, and provide reliable support for data-driven AI applications. Summary of the invention

[0003] The present invention provides an automated artificial intelligence data sharing method based on blockchain, comprising: Step 1. The data provider uploads the encrypted original data to the off-chain storage unit. The on-chain verification model runs directly on the encrypted data to generate the data validity verification result. Step 2: After the validity verification is passed, rewards are automatically issued to the data provider, and an access control smart contract is written for the data uploaded by the data provider; Step 3: If the validity verification fails, the data uploaded by the data provider will be blocked from being uploaded to the chain, and the encrypted data in the off-chain storage unit will be cleared; Step 4: After the data requester submits an access request, the on-chain data sharing center automatically shares the data with the data requester based on the access control smart contract; Step 5. Save key operation records on the chain in real time, and generate audit reports regularly based on the saved records to ensure the compliance of the sharing process.

[0004] As described above, an automated artificial intelligence data sharing method based on blockchain, wherein the off-chain storage unit is specifically used to store data uploaded by the data provider, and a distributed storage architecture is adopted. After the data provider uploads the data, the off-chain storage unit will automatically generate a unique storage identifier for it based on the descriptive information therein and the characteristics of the random sample.

[0005] As described above, a blockchain-based automated artificial intelligence data sharing method, in which the on-chain verification model can verify the validity of the data without decrypting the data, the training process specifically includes the following sub-steps: Encrypt both the training data set and the test data set into ciphertext format; Create an artificial intelligence model in a ciphertext environment and use the training data set to train and tune the model; Use the test data set to cyclically check whether the output results of the model meet expectations. After the test passes, terminate the training and obtain the final verification model.

[0006] As described above, an automated artificial intelligence data sharing method based on blockchain, wherein an access control smart contract is written for the data uploaded by the data provider, specifically comprising the following sub-steps: Define data access fees, single access time, access roles, and whether to make four-way key data items public; Automatically generate access control smart contracts based on defined data entries and associate them with uploaded data; Add a reentrancy detection unit to promptly respond to reentrancy attacks during smart contract execution.

[0007] As described above, an automated artificial intelligence data sharing method based on blockchain, wherein after the data requester submits an access request, the on-chain data sharing center automatically shares the data with the data requester based on the access control smart contract, specifically including the following sub-steps: Generate a search identifier based on the data requester's requirement description and sample; Perform a matching operation on the retrieval identifier and the storage identifier of the on-chain data, and retrieve the on-chain data with the highest matching degree and put it into the cache area; Display several samples of retrieved data in the cache area to the data requester, and the data requester determines whether to submit an access request based on the samples; If an access request is submitted, the access control smart contract is executed immediately, and the retrieved data in the cache is automatically shared with the data requester based on each event in the contract; If the data requester does not submit an access request within a predetermined time, the retrieved data is cleared from the cache area and the cache is released.

[0008] As described above, an automated artificial intelligence data sharing method based on blockchain, wherein if the data requester only provides a demand description, the data description part in the storage identifier is obtained by inverse XOR operation and matched with the retrieval identifier; if the data requester provides both the demand description and the instance sample, the storage identifier and the retrieval identifier are directly matched, and finally the on-chain data with the highest degree of matching is retrieved and placed in the cache area based on the operation results.

[0009] As described above, a blockchain-based automated artificial intelligence data sharing method, in which key operation records on the chain are saved in real time, and audit reports are regularly generated based on the saved records, specifically includes the following sub-steps: Define key operational events in smart contracts; Use off-chain listeners to capture key operation events in real time and store them in the database; Set up a scheduled task to regularly generate audit reports based on the monitoring data in the database.

[0010] The present invention also provides an automated artificial intelligence data sharing system based on blockchain, comprising: an off-chain storage module, an on-chain verification module, an on-chain data sharing module, and an off-chain monitoring module; The off-chain storage module is used to store encrypted data uploaded by the data provider; On-chain verification module, used to verify the validity of uploaded data; On-chain data sharing module, used to automatically share on-chain data with data requesters; The off-chain monitoring module is used to save key operation records on the chain in real time and generate audit reports regularly based on the saved records.

[0011] The beneficial effects achieved by the present invention are as follows: it solves the problems of data silos, privacy security, lack of trust and low efficiency in the prior art, provides an efficient, secure and automated data sharing solution, promotes the deep integration of artificial intelligence and blockchain technology, and provides reliable support for data-driven AI applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0013] Figure 1 This is a flowchart of an automated artificial intelligence data sharing method based on blockchain provided in Example 1 of the present application; Figure 2 This is a schematic diagram of an automated artificial intelligence data sharing system based on blockchain provided in Example 2 of the present application. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0015] Embodiment 1 like Figure 1 As shown, the first embodiment of the present application provides an automated artificial intelligence data sharing method based on blockchain, including: Step S10: The data provider uploads the encrypted original data to the off-chain storage unit, and the on-chain verification model runs directly on the encrypted data to generate the data validity verification result; The off-chain storage unit is specifically used to store data uploaded by the data provider. It adopts a distributed storage architecture. After the data provider uploads the data, the off-chain storage unit will automatically generate a unique storage identifier for it based on the description information and the characteristics of the random sample. Through this storage identifier, the on-chain data sharing center can quickly obtain the data in the required field or industry. The automatic generation formula of the storage identifier is expressed as: , where S is the generated storage identifier, Hash() is a hash function used to convert data into a hash value. is the jth word element vector in the uploaded data description information, is the standard deviation of the word vector in the description information, is the mean value of the word vector in the description information, j ranges from 1 to J, J is the total number of word vectors in the description information, is the characteristic value (such as variance, mean or principal component) of the kth random sample, is the minimum eigenvalue among all random samples, is the maximum eigenvalue among all random samples, k ranges from 1 to K, K is the number of random samples drawn, and the symbol " " indicates an exclusive-OR operation.

[0016] The on-chain verification model can verify the validity of data without decrypting the data. The training process specifically includes the following sub-steps: Step S11: Encrypt both the training data set and the test data set into ciphertext format; The floating point encryption algorithm adopted in this embodiment is more suitable for machine learning tasks.

[0017] Step S12: Create an artificial intelligence model in a ciphertext environment, and use the training data set to train and tune the model; Since the computational complexity of the model in a ciphertext environment is relatively high, this embodiment adopts a lightweight linear model as the base model.

[0018] The loss function of the model in the ciphertext environment also needs to directly operate on the encrypted data, so the loss function of the model is expressed as: , where L is the loss value of the current training batch, E() represents the encryption function, is the i-th plaintext input in the current training batch, is the i-th plaintext output in the current training batch, and Respectively represent ciphertext addition and ciphertext multiplication, i ranges from 1 to n, n is the total number of training samples in the current training batch, for ciphertext E(A) and ciphertext E(B), E(A) E(B)=E(A+B), E(A) E(B)=E(AXB).

[0019] Ciphertext Decrypted into plaintext L, and then tune the parameters in the model according to the change of L value until the L value no longer decreases or has no obvious trend of decreasing, then suspend training.

[0020] Step S13: Use the test data set to cyclically check whether the output results of the model meet expectations. After the test passes, the training is terminated to obtain the final verification model; The test data in ciphertext format is input into the model to obtain the ciphertext output of the model, and then the ciphertext output is decrypted into plaintext output. If it is consistent with the expected output, the test data passes the inspection. When more than 99% of the test data in the test data set can pass the inspection, the model is considered to have passed the inspection, the model training is terminated, and the final verification model is deployed on the blockchain.

[0021] Step S20: After the validity verification is passed, rewards are automatically issued to the data provider, and an access control smart contract is written for the data uploaded by the data provider; As an incentive mechanism, after the data provider provides valid data, the on-chain data sharing center will immediately issue rewards to the data provider to increase the user's enthusiasm for providing data. The reward can be in any legal and compliant form such as virtual tokens, points, gift redemption codes, etc.

[0022] After the data is registered to the on-chain data sharing center, that is, after the data is on-chain, a special smart contract needs to be written for it to define its access rules, which is divided into the following sub-steps: Step S21: define data access fee, single access time, access role, and whether to disclose four-way key data items; Data access fee, single access time, access role, and whether it is public are mandatory items. In addition, data providers can also add other data items as needed.

[0023] Step S22: automatically generate an access control smart contract based on the defined data entries and associate it with the uploaded data; Each defined data entry will be mapped to a dedicated state variable, and then the logic code will be automatically generated according to the content filled in by the data provider. These logic codes contain multiple events, and a single event is only used to check whether a state variable is compliant. The generated logic code is stored in a separate function as an automatic control unit of the access control smart contract. The generated smart contract is associated with the uploaded data to realize the automation of access control of the uploaded data.

[0024] Step S23: Add a reentry detection unit to promptly respond to reentry attacks suffered during the execution of the smart contract; The reentry detection unit is used to monitor the triggering of various events and the changes of various state variables in the execution of smart contracts in real time, and judge whether the contract has suffered a reentry attack based on this information, and freeze the account of the current data accessor if the judgment result is yes, so as to achieve rapid response and processing of reentry attacks. The core function of the reentry detection unit is expressed as: , where R represents the probability coefficient of the current smart contract being attacked by reentry, Indicates the number of external calls of event q within the current detection time (the detection time set in this embodiment is 50ms), is the average number of external calls of event q within a preset detection period (the preset detection period in this embodiment is 5s), is the reentrancy attack judgment coefficient of event q, q takes values ​​from 1 to Q, Q is the total number of events included in the smart contract, I() is an indicator function, when Returns 1 if yes, otherwise returns 0. represents the current value of the pth state variable, Indicates the value of the pth state variable after the last external call ends. It represents the reentrancy attack judgment coefficient of the p-th state variable, where p ranges from 1 to P, and P is the total number of state variables contained in the smart contract.

[0025] When the probability coefficient R is higher than the threshold, it is considered that the smart contract has suffered a reentry attack, and the current data accessor account is frozen in time to reduce losses.

[0026] Step S30: If the validity verification fails, the data uploaded by the data provider is prevented from being uploaded to the chain, and the encrypted data in the off-chain storage unit is cleared; Uploading data to the chain means registering the unique storage identifier of the off-chain stored data to the on-chain data sharing center for subsequent automatic sharing. Data that is not uploaded to the chain will not be shared and should not be stored off-chain, so accordingly, uploaded data that fails the validity verification will be cleared by the off-chain storage unit.

[0027] Step S40: After the data requester submits an access request, the on-chain data sharing center automatically shares the data with the data requester based on the access control smart contract; The on-chain data sharing center can quickly retrieve the data that best meets the needs of the data requester and share the data with the data requester in strict compliance with the contract. The specific sharing process is divided into the following sub-steps: Step S41: Generate a search identifier according to the data requester's requirement description and sample; The requirement description is mandatory. The data requester needs to describe in text what kind of data he wants. The example sample is optional and is used to accurately describe the data format, field, etc. that the data requester wants. The generation method of the retrieval identifier is consistent with the generation method of the storage identifier.

[0028] Step S42: Perform a matching operation on the search identifier and the storage identifier of the on-chain data, and retrieve the on-chain data with the highest matching degree and put it into the cache area; If the data requester only provides a demand description, the data description part in the storage identifier is obtained by inverse XOR operation and matched with the retrieval identifier; if the data requester provides both the demand description and the instance sample, the storage identifier and the retrieval identifier are directly matched, and finally the on-chain data with the highest matching degree is retrieved and put into the cache area according to the operation result. The retrieved data can be one or more. The expression of the matching operation is: , where D is the result of matching calculation, To retrieve the zth byte in the identifier, To store the zth byte in the identifier, z ranges from 1 to M, and M is the identifier length, that is, the number of bytes contained in the current hash value.

[0029] Step S43: showing several samples of the retrieved data in the cache area to the data requester, and the data requester determines whether to submit an access request based on the samples; The display sample is the sample extracted when generating the storage identifier. The data requester checks whether the display sample meets his or her expectations to determine whether to submit an access request.

[0030] Step S44: If an access request is submitted, the access control smart contract is immediately executed, and the retrieved data in the cache area is automatically shared with the data requester based on various events in the contract; The smart contract will first call its own associated events to verify whether the data accessor meets the access role, whether the access fee has been paid, whether it is within the single access time, and other custom access conditions. As long as one of the verifications fails, the data accessor cannot successfully obtain the data. If the verification results are all yes, the authorization key will be sent to the data accessor, and the data accessor will use the authorization key to obtain the decrypted original data. The authorization key will be automatically discarded after the data accessor completes the data acquisition action or after the single access time expires. The original data cannot be obtained again using this key; if the data is public data, the data acquisition action will be directly triggered after the access condition verification passes.

[0031] Step S45: If the data requester does not submit an access request within a predetermined time, the retrieved data is cleared from the cache area to release the cache.

[0032] Step S50: Save key operation records on the chain in real time, and generate audit reports regularly based on the saved records to ensure the compliance of the sharing process; This part of the function is implemented by the off-chain listener, which is divided into the following sub-steps: Step S51: defining key operation events in the smart contract; For example, data modification, permission change, transaction execution, etc. are set by the administrator as needed.

[0033] Step S52: Use the off-chain listener to capture key operation events in real time and store them in the database; Every time a key operation occurs, the contract will trigger a corresponding event containing relevant parameters. The off-chain listener can monitor and capture these events in real time and store the data in the database.

[0034] Step S53: Setting a scheduled task to regularly generate an audit report based on the monitoring data in the database; Set up a scheduled task to extract data from the database regularly, generate reports according to preset templates, and provide them to relevant personnel via email or user interface.

[0035] Embodiment 2 like Figure 2 As shown, Embodiment 2 of the present application provides an automated artificial intelligence data sharing system based on blockchain, including: an off-chain storage module 21, an on-chain verification module 22, an on-chain data sharing module 23, and an off-chain monitoring module 24; The off-chain storage module 21 is used to store the encrypted data uploaded by the data provider; it specifically includes the following submodules: 1. Data receiving submodule, used to receive encrypted data uploaded by the data provider; 2. The storage identifier generation submodule is used to generate a unique storage identifier for the uploaded data based on the description information of the uploaded data and the characteristics of the random sample; Through this storage identifier, the on-chain data sharing module can quickly obtain data in the required field or industry. The automatic generation formula of the storage identifier is expressed as: , where S is the generated storage identifier, Hash() is a hash function used to convert data into a hash value. is the jth word element vector in the uploaded data description information, is the standard deviation of the word vector in the description information, is the mean value of the word vector in the description information, j ranges from 1 to J, J is the total number of word vectors in the description information, is the characteristic value (such as variance, mean or principal component) of the kth random sample, is the minimum eigenvalue among all random samples, is the maximum eigenvalue among all random samples, k ranges from 1 to K, K is the number of random samples drawn, and the symbol " " indicates an exclusive-OR operation.

[0036] The on-chain verification module 22 is used to verify the validity of the uploaded data and implement the data uploading or clearing operation according to the validity verification result; it specifically includes the following submodules: 1. The on-chain verification model training submodule is used to train an artificial intelligence model to verify the validity of data in a ciphertext environment; The training process specifically includes the following sub-steps: ① Encrypt both the training data set and the test data set into ciphertext format; The floating point encryption algorithm adopted in this embodiment is more suitable for machine learning tasks.

[0037] ② Create an artificial intelligence model in a ciphertext environment and use the training data set to train and tune the model; Since the computational complexity of the model in a ciphertext environment is relatively high, this embodiment adopts a lightweight linear model as the base model.

[0038] The loss function of the model in the ciphertext environment also needs to operate directly on the encrypted data, so the loss function designed for this model is expressed as: , where L is the loss value of the current training batch, E() represents the encryption function, is the i-th plaintext input in the current training batch, is the i-th plaintext output in the current training batch, and Respectively represent ciphertext addition and ciphertext multiplication, i ranges from 1 to n, n is the total number of training samples in the current training batch, for ciphertext E(A) and ciphertext E(B), E(A) E(B)=E(A+B), E(A) E(B)=E(AXB).

[0039] Ciphertext Decrypted into plaintext L, and then tune the parameters in the model according to the change of L value until the L value no longer decreases or has no obvious trend of decreasing, then suspend training.

[0040] ③ Use the test data set to cyclically check whether the output results of the model meet expectations. After the test passes, terminate the training and obtain the final verification model; The test data in ciphertext format is input into the model to obtain the ciphertext output of the model, and then the ciphertext output is decrypted into plaintext output. If it is consistent with the expected output, the test data passes the inspection. When more than 99% of the test data in the test data set can pass the inspection, the model is considered to have passed the inspection, the model training is terminated, and the final verification model is deployed on the blockchain.

[0041] 2. The model running submodule is used to directly run the on-chain verification model on the uploaded encrypted data to generate data validity verification results; As an incentive mechanism, after the data provider provides valid data, the on-chain data sharing module will immediately issue rewards to the data provider to increase the user's enthusiasm for providing data. The reward can be in any legal and compliant form such as virtual tokens, points, gift redemption codes, etc.

[0042] The on-chain data sharing module 23 is used to automatically share on-chain data with data requesters; it specifically includes the following submodules: 1. The data chain submodule is used to upload the uploaded data that has passed the validity verification to the chain, and to clear the data that has not passed the validity verification; Uploaded data that fails the validity verification cannot be uploaded to the chain, and data that is not uploaded to the chain will not be shared, so accordingly, uploaded data that fails the validity verification will be cleared by the off-chain storage unit. 2. Access control smart contract writing submodule, which is used to write a special smart contract for the uploaded data on the chain to define its access rules; It includes the following sub-steps: ① Define data access fees, single access time, access roles, and whether to disclose four-way key data items; Data access fee, single access time, access role, and whether it is public are mandatory items. In addition, data providers can also add other data items as needed.

[0043] ② Automatically generate access control smart contracts based on defined data entries and associate them with uploaded data; Each defined data entry will be mapped to a dedicated state variable, and then the logic code will be automatically generated according to the content filled in by the data provider. These logic codes contain multiple events, and a single event is only used to check whether a state variable is compliant. The generated logic code is stored in a separate function as an automatic control unit of the access control smart contract. The generated smart contract is associated with the uploaded data to realize the automation of access control of the uploaded data.

[0044] ③Add a reentrancy detection unit to promptly respond to reentrancy attacks suffered during the execution of smart contracts; The reentry detection unit is used to monitor the triggering of various events and the changes of various state variables in the execution of smart contracts in real time, and judge whether the contract has suffered a reentry attack based on this information, and freeze the account of the current data accessor if the judgment result is yes, so as to achieve rapid response and processing of reentry attacks. The core function of the reentry detection unit is expressed as: , where R represents the probability coefficient of the current smart contract being attacked by reentry, Indicates the number of external calls of event q within the current detection time (the detection time set in this embodiment is 50ms), is the average number of external calls of event q within a preset detection period (the preset detection period in this embodiment is 5s), is the reentrancy attack judgment coefficient of event q, q takes values ​​from 1 to Q, Q is the total number of events included in the smart contract, I() is an indicator function, when Returns 1 if yes, otherwise returns 0. represents the current value of the pth state variable, Indicates the value of the pth state variable after the last external call ends. It represents the reentrancy attack judgment coefficient of the p-th state variable, where p ranges from 1 to P, and P is the total number of state variables contained in the smart contract.

[0045] When the probability coefficient R is higher than the threshold, it is considered that the smart contract has suffered a reentry attack, and the current data accessor account is frozen in time to reduce losses.

[0046] 3. Contract execution submodule, used to automatically share data with data requesters based on access control smart contracts; The specific sharing process is divided into the following sub-steps: ① Generate a search identifier based on the data requester’s requirement description and sample; The requirement description is mandatory. The data requester needs to describe in text what kind of data he wants. The example sample is optional and is used to accurately describe the data format, field, etc. that the data requester wants. The generation method of the retrieval identifier is consistent with the generation method of the storage identifier.

[0047] ② Perform a matching operation on the retrieval identifier and the storage identifier of the on-chain data, and retrieve the on-chain data with the highest matching degree and put it into the cache area; If the data requester only provides a demand description, the data description part in the storage identifier is obtained by inverse XOR operation and matched with the retrieval identifier; if the data requester provides both the demand description and the instance sample, the storage identifier and the retrieval identifier are directly matched, and finally the on-chain data with the highest matching degree is retrieved and put into the cache area according to the operation result. The retrieved data can be one or more. The expression of the matching operation is: , where D is the result of matching calculation, To retrieve the zth byte in the identifier, To store the zth byte in the identifier, z ranges from 1 to M, and M is the identifier length, that is, the number of bytes contained in the current hash value.

[0048] ③Show several samples of the retrieved data in the cache area to the data requester, and the data requester determines whether to submit the access request based on the samples; The display sample is the sample extracted when generating the storage identifier. The data requester checks whether the display sample meets his or her expectations to determine whether to submit an access request.

[0049] ④ If an access request is submitted, the access control smart contract will be executed immediately, and the retrieved data in the cache will be automatically shared with the data requester based on the various events in the contract; The smart contract will first call its own associated events to verify whether the data accessor meets the access role, whether the access fee has been paid, whether it is within the single access time, and other custom access conditions. As long as one of the verifications fails, the data accessor cannot successfully obtain the data. If the verification results are all yes, the authorization key will be sent to the data accessor, and the data accessor will use the authorization key to obtain the decrypted original data. The authorization key will be automatically discarded after the data accessor completes the data acquisition action or after the single access time expires. The original data cannot be obtained again using this key; if the data is public data, the data acquisition action will be directly triggered after the access condition verification passes.

[0050] ⑤ If the data requester does not submit an access request within the predetermined time, the retrieved data is cleared from the cache area and the cache is released.

[0051] The off-chain monitoring module 24 is used to save the key operation records on the chain in real time and regularly generate audit reports based on the saved records to ensure the compliance of the sharing process; It is divided into the following sub-steps: ①Define key operation events in smart contracts; For example, data modification, permission change, transaction execution, etc. are set by the administrator as needed.

[0052] ② Capture key operation events in real time and store them in the database; Every time a key operation occurs, the contract will trigger a corresponding event containing relevant parameters. The off-chain monitoring module can monitor and capture these events in real time and store the data in the database.

[0053] ③ Set a scheduled task to regularly generate audit reports based on the monitoring data in the database; Set up a scheduled task to extract data from the database regularly, generate reports according to preset templates, and provide them to relevant personnel via email or user interface.

[0054] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to execute an automated artificial intelligence data sharing method based on blockchain.

[0055] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute an automated artificial intelligence data sharing method based on blockchain.

[0056] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned automated artificial intelligence data sharing method based on blockchain.

[0057] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0058] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0059] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0060] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0061] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus DRAM (DRRAM).

[0062] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0063] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0064] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A blockchain-based automated artificial intelligence data sharing method, characterized in that: include: Step 1. The data provider uploads the encrypted original data to the off-chain storage unit. The on-chain verification model runs directly on the encrypted data to generate the data validity verification result. Step 2: After the validity verification is passed, rewards are automatically issued to the data provider, and an access control smart contract is written for the data uploaded by the data provider; Step 3: If the validity verification fails, the data uploaded by the data provider will be blocked from being uploaded to the chain, and the encrypted data in the off-chain storage unit will be cleared; Step 4: After the data requester submits an access request, the on-chain data sharing center automatically shares the data with the data requester based on the access control smart contract; Step 5. Save key operation records on the chain in real time, and generate audit reports regularly based on the saved records to ensure the compliance of the sharing process.

2. According to claim 1, the automated artificial intelligence data sharing method based on blockchain is characterized in that: The off-chain storage unit is specifically used to store data uploaded by data providers. It adopts a distributed storage architecture. After the data provider uploads the data, the off-chain storage unit will automatically generate a unique storage identifier for it based on the descriptive information and the characteristics of the random sample.

3. The method for sharing automated artificial intelligence data based on blockchain according to claim 1, characterized in that: The on-chain verification model can verify the validity of data without decrypting the data. The training process specifically includes the following sub-steps: Encrypt both the training data set and the test data set into ciphertext format; Create an artificial intelligence model in a ciphertext environment and use the training data set to train and tune the model; Use the test data set to cyclically check whether the output results of the model meet expectations. After the test passes, terminate the training and obtain the final verification model.

4. The method for sharing automated artificial intelligence data based on blockchain according to claim 1, characterized in that: Write an access control smart contract for the data uploaded by the data provider, which includes the following sub-steps: Define data access fees, single access time, access roles, and whether to make four-way key data items public; Automatically generate access control smart contracts based on defined data entries and associate them with uploaded data; Add a reentrancy detection unit to promptly respond to reentrancy attacks during smart contract execution.

5. The method for sharing automated artificial intelligence data based on blockchain according to claim 1, characterized in that: After the data requester submits an access request, the on-chain data sharing center automatically shares the data with the data requester based on the access control smart contract, which includes the following sub-steps: Generate a search identifier based on the data requester's requirement description and sample; Perform a matching operation on the retrieval identifier and the storage identifier of the on-chain data, and retrieve the on-chain data with the highest matching degree and put it into the cache area; Display several samples of the retrieved data in the cache area to the data requester, and the data requester determines whether to submit the access request based on the samples; If an access request is submitted, the access control smart contract is executed immediately, and the retrieved data in the cache is automatically shared with the data requester based on each event in the contract; If the data requester does not submit an access request within a predetermined time, the retrieved data is cleared from the cache area and the cache is released.

6. The method for sharing automated artificial intelligence data based on blockchain according to claim 5, characterized in that: If the data requester only provides a requirement description, the data description part in the storage identifier is obtained by inverse XOR operation and matched with the retrieval identifier; if the data requester provides both the requirement description and the instance sample, the storage identifier and the retrieval identifier are directly matched, and finally the on-chain data with the highest matching degree is retrieved and placed in the cache area based on the operation result.

7. The method for sharing automated artificial intelligence data based on blockchain according to claim 1, characterized in that: Save key operation records on the chain in real time and generate audit reports regularly based on the saved records, which includes the following sub-steps: Define key operational events in smart contracts; Use off-chain listeners to capture key operation events in real time and store them in the database; Set up a scheduled task to regularly generate audit reports based on the monitoring data in the database.

8. An automated artificial intelligence data sharing system based on blockchain, characterized in that: include: Off-chain storage module, on-chain verification module, on-chain data sharing module, off-chain monitoring module; The off-chain storage module is used to store encrypted data uploaded by the data provider; On-chain verification module, used to verify the validity of uploaded data; On-chain data sharing module, used to automatically share on-chain data with data requesters; The off-chain monitoring module is used to save key operation records on the chain in real time and generate audit reports regularly based on the saved records.

9. The automated artificial intelligence data sharing system based on blockchain according to claim 8, characterized in that: On-chain verification module details Includes the following submodules: The on-chain verification model training submodule is used to train an artificial intelligence model to verify the validity of data in a ciphertext environment; The model running submodule is used to directly run the on-chain verification model on the uploaded encrypted data to generate data validity verification results.

10. The automated artificial intelligence data sharing system based on blockchain according to claim 8, characterized in that: The on-chain data sharing module specifically includes the following sub-modules: The data chain submodule is used to chain the uploaded data that has passed the validity verification and clear the data that has failed the validity verification; The access control smart contract writing submodule is used to write a special smart contract for the uploaded data on the chain to define its access rules; The contract execution submodule is used to automatically share data with the data requester based on the access control smart contract.

Images