A Cross-Platform Business Integration and Handling Method Based on Blockchain

Abstract

The present invention relates to the field of blockchain technology, and discloses a cross-platform business integration handling method based on blockchain, including: Step 1, each business participant obtains private data and performs encryption processing, slices the private data through a secret sharing protocol, each slice generates sub-data according to a preset rule, distributes the sliced data to the participants, for the data that needs to participate in non-linear calculation, uses the partially homomorphic encryption technology to generate encrypted data, and each participant uploads the sliced data and the encrypted data to the blockchain platform to generate a corresponding integrity hash value for the original data; Step 2, based on the encrypted data and the sliced data uploaded to the blockchain in Step 1. By combining the secret sharing and the partially homomorphic encryption technology, a double-layer privacy protection for the original data is realized in the multi-party collaboration process, ensuring that the operation can be completed without decrypting the data during the calculation process, and achieving the effect of effectively preventing the leakage of data privacy.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and specifically to a cross-platform business integration and handling method based on blockchain. Background Art

[0002] With the rapid development of modern information technology and digital transformation, cross-platform business collaboration has become a key link for various industries to achieve efficient resource integration, information sharing, and collaborative operation. Especially in the fields of finance, supply chain management, healthcare, and government services, different platforms need to process distributed computing tasks through multi-party collaboration to meet the growing needs of users. For example, in the supply chain scenario, different nodes need to share order, logistics, and inventory information, and collaborate to complete cost settlement and optimization decisions, while in cross-border payments, different financial institutions need to complete joint risk control and payment clearing while protecting user privacy.

[0003] Currently, cross-platform business integration faces various complexity issues. Firstly, there are differences in data protocols and processing standards between different platforms, resulting in the inability to interoperate and share business data. Secondly, each platform usually has an independent data storage and processing mechanism, lacking a unified collaboration framework, making cross-platform collaboration require solving system compatibility and dealing with challenges in data privacy and security, computational trustworthiness, and efficient processing performance.

[0004] Specifically, the current cross-platform business collaboration faces the following technical problems:

[0005] In multi-party collaboration, business data usually needs to be shared and interacted between platforms, resulting in sensitive data being at risk of leakage and abuse. Traditional data encryption schemes can protect data privacy, but usually need to be decrypted when calculating encrypted data, thereby introducing potential risks of privacy leakage.

[0006] The dynamic nature of cross-platform collaboration allows the number and scope of participants to change. Existing methods lack efficient management of the consistency and security of data sharding when dealing with dynamic participants, easily leading to inaccurate calculations and data leakage.

[0007] In cross-platform computing tasks, the transparency of the computing path and the credibility of the results cannot be verified. Existing solutions usually rely on a centralized trusted third party for auditing and verification, increasing collaboration costs and being unable to prevent tampering.

[0008] Cross-platform collaboration scenarios often involve complex non-linear calculations. Traditional methods cannot effectively distinguish between lightweight and complex calculations, resulting in the same calculation strategy being adopted for tasks, and introducing additional computational resource consumption and performance problems.

[0009] Therefore, those skilled in the art provide a cross-platform business integration handling method based on blockchain to solve the above-mentioned problems. Summary of the Invention

[0010] Aiming at the deficiencies of the prior art, the present invention provides a cross-platform business integration handling method based on blockchain to solve the problems raised in the above background technology.

[0011] To achieve the above objectives, the present invention is realized through the following technical solutions: A cross-platform business integration handling method based on blockchain, including:

[0012] Step 1: Each business participant obtains private data and performs encryption processing. The private data is fragmented through a secret sharing protocol. Each fragment generates sub-data according to a preset rule, and the fragmented data is distributed to the participants. For the data that needs to participate in non-linear calculations, homomorphic encryption technology is used to generate encrypted data. Each participant uploads the fragmented data and encrypted data to the blockchain platform to generate a corresponding integrity hash value for the original data;

[0013] Step 2: Based on the encrypted data and fragmented data uploaded to the blockchain in Step 1, each participant establishes a dynamic multi-party secure computing protocol according to the set computing task target, constructs a set of computing participants. When a new participant joins, the key fragments will be updated, and the fragmented data will be regenerated by adding adjustment factors to the existing fragmented data. For the exiting participants, the held fragmented data is distributed to the remaining participants through reallocation;

[0014] Step 3: After the start of distributed dynamic calculation is completed, based on the constructed set of participants and fragmented data, multi-layer computing tasks are performed on the private data according to the computing task target. The lightweight computing tasks include addition and multiplication operations of participant data, and perform combined operations of fragmented data among different participants. For the private data involving complex non-linear computing tasks, the encrypted data is called to perform logarithmic and exponential operations in the encrypted domain to generate corresponding intermediate results. The computing tasks are decomposed into logic gate operations according to the Boolean circuit model, and the intermediate results of the computing tasks will be verified and stored in subsequent steps;

[0015] Step 4: During the execution of the multi-layer computing tasks, each intermediate result generated is verified by calculating the hash value. The hash value of each result is concatenated with the hash value of the previous computing step to generate a new hash value. The new hash value represents the integrity of the intermediate result of the step. The hash value of the intermediate result is stored in the nodes of the recursive tree in sequence according to the computing path, and the generated hash value of the root node of the recursive tree is uploaded to the blockchain for on-chain storage of subsequent result verification;

[0016] Step 5: Based on the hash value of the root node of the recursive tree generated in Step 4, all participating parties jointly complete the decryption of the final calculation result. The decrypted result is verified for credibility by all participating parties through comparison with the hash value of the root node of the recursive tree stored in the blockchain. If the verification passes, the decrypted result is regarded as complete and credible and is used for subsequent processing of cross-platform business integration.

[0017] Preferably, the sharded data generated through the secret sharing protocol in Step 1 satisfies the following relationship:

[0018]

[0019] where x i is the original data of the i-th business participating party, s i，j is the j-th sharded data of the i-th participating party, n is the total number of shards, and p is the prime modulus value for protecting data privacy;

[0020] Each shard s i，j is generated, where the last shard is calculated through the following formula:

[0021]

[0022] where s i，n is the n-th sharded data of the i-th participating party.

[0023] Preferably, for the data that needs to participate in non-linear calculations in Step 1, the generated partially homomorphic encrypted data E(x i ) satisfies the following encryption relationship:

[0024]

[0025] where g is the encryption base, x i is the original data, N is the modulus, and modN represents the modulo operation;

[0026] E(x i ) supports addition and multiplication operations in the encrypted domain and satisfies the following properties:

[0027] E(x i + x j ) = E(x i ) · E(x j )(modN),

[0028] where E(x i + x j ) represents the encrypted result after adding x i and x j in the encrypted domain, and E(x i ) and E(x j ) are xi and x j in encrypted form, where N is the modulus and mod N represents modular arithmetic.

[0029] Preferably, in step 2, when a new participant joins, the existing shard data is adjusted by the following formula:

[0030] s′ i，j = s i，j + r i，new (mod p),

[0031] where s i，j is the shard data before adjustment, s′ i，j is the shard data after adjustment, r i，new is the generated adjustment factor, and p is a preset large prime modulus value;

[0032] When a participant withdraws, the shard data is redistributed as follows:

[0033] s′ i，j = s i，j - s i，exit (mod p),

[0034] where s i，j is the shard data before adjustment, s′ i，j is the shard data after adjustment, s i，exit is the shard data held by the withdrawing party, and p is a preset large prime modulus value.

[0035] Preferably, in step 3, the lightweight computing tasks perform data addition and multiplication based on the combined operations of shard data, where the square operation is calculated by the following formula:

[0036]

[0037] where is the square value of the data of the i-th participant, s i，j and s i，k are shard data, n is the total number of shards, p is a preset large prime modulus value,

[0038] represents the sum of the squares of the shard data,

[0039] 1 ≤ j < k ≤ n means that j and k are different shard indices and j < k.

[0040] Preferably, in step 3, the logarithmic operations involving complex non-linear computing tasks are calculated through homomorphic encrypted data and satisfy the following relationship:

[0041]

[0042] where, ln(1 + x j ) is the logarithm value of the data of the j-th participating party, g and N are the encryption base and modulus, and E(ln(1 + x j )) is the encrypted logarithm value, and modN represents the modulo operation.

[0043] Preferably, in step 4, the intermediate results of the computing tasks are stored in a recursive tree, where the hash values of the intermediate results are generated in the following manner:

[0044] H k+1 = H(H k ||H k+1 ),

[0045] where, H k is the hash value of the intermediate result at the k-th step, H k+1 is the hash value of the intermediate result at the (k + 1)-th step, and || represents the concatenation operation;

[0046] H k+1 is stored in the node of the recursive tree for verifying the computing path.

[0047] Preferably, the hash value H root of the root node of the recursive tree is calculated by the following formula:

[0048] H root = H(H k-1 ||H k ),

[0049] where, H root is the hash value of the root node of the recursive tree, H k-1 and H k are the hash values of the intermediate results at the (k - 1)-th step and the k-th step, and || represents the concatenation operation;

[0050] The hash value of the root node is uploaded to the blockchain for verifying the integrity and credibility of the computing.

[0051] Preferably, the final result after decryption in step 5 is represented by the following formula:

[0052] y = D(E(y)),

[0053] where, y is the combined computing result, E(y) is the encrypted final computing result, and D is the corresponding decryption function.

[0054] Preferably, the credibility verification is performed by using the hash value H root of the root node of the recursive tree and the hash value H root of the final computing result, where the verification formula is as follows:

[0055] Hresult = H(H k-1 || H k ), H root = H(H result || H final ),

[0056] where H k-1 and H k are the hash values of the intermediate results at the (k - 1)-th and k-th steps, H result is the hash value of the final task result calculated based on the intermediate results, H final is the hash value generated for the final decryption result y,

[0057] H root represents the hash value of the root node of the recursive tree, stored in the blockchain for verifying the integrity of the calculation path and the final result.

[0058] The present invention provides a cross-platform business integration handling method based on the blockchain. It has the following beneficial effects:

[0059] 1. By combining the secret sharing and partial homomorphic encryption technologies, the present invention realizes double-layer privacy protection for the original data during the multi-party collaboration process, ensuring that the operation can be completed without decrypting the data during the calculation process, and achieving the effect of effectively preventing data privacy leakage.

[0060] 2. By introducing a dynamic multi-party secure calculation protocol, the present invention realizes the secure adjustment and reallocation of the sharded data when the participants dynamically join and exit in the cross-platform business collaboration, achieving the effect of ensuring data consistency and collaboration security.

[0061] 3. By using the recursive tree structure to record the hash values of the intermediate results at each step in the calculation path and uploading the hash value of the root node to the blockchain, the present invention realizes the transparency and traceability of the calculation path, achieving the effect of enhancing the credibility of the calculation and preventing the result from being tampered with.

[0062] 4. By hierarchically optimizing complex non-linear calculation tasks, using secret sharing to complete lightweight operations, and using partial homomorphic encryption to process complex non-linear operations, the present invention realizes the efficient adaptation to cross-platform diverse calculation tasks, achieving the effect of significantly improving the calculation performance and reducing the resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is the flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0065] The present invention will be described in detail below with reference to the accompanying drawings:

[0066] Embodiment:

[0067] Please refer to the attached Figure 1 , the embodiment of the present invention provides a cross-platform service integration and handling method based on blockchain, including:

[0068] Step 1: Each business participant obtains private data and performs encryption processing, slices the private data through a secret sharing protocol, generates sub-data for each slice according to a preset rule, distributes the sliced data to the participants, and for the data that needs to participate in non-linear calculation, generates encrypted data using partial homomorphic encryption technology. Each participant uploads the sliced data and the encrypted data to the blockchain platform to generate a corresponding integrity hash value for the original data;

[0069] Step 2: Based on the encrypted data and sliced data uploaded to the blockchain in Step 1, each participant establishes a dynamic multi-party secure computing protocol according to the set computing task target, constructs a set of computing participants. When a new participant joins, the key slices will be updated, and the sliced data will be regenerated by adding an adjustment factor to the existing sliced data. For the exited participant, the held sliced data will be distributed to the remaining participants by reallocation;

[0070] Step 3: After the start of distributed dynamic calculation is completed, based on the constructed set of participants and sliced data, perform multi-layer computing tasks on the private data according to the computing task target. The lightweight computing tasks include addition and multiplication operations of participant data, perform combined operations of sliced data among different participants, and for the private data involving complex non-linear computing tasks, call the encrypted data to perform logarithm and exponential operations in the encrypted domain to generate corresponding intermediate results. The computing tasks are decomposed into logic gate operations according to the Boolean circuit model, and the intermediate results of the computing tasks will be verified and stored in the subsequent steps;

[0071] Step 4: During the execution of the multi-layer computing task, the intermediate results of each step are verified by calculating the hash value. The hash values of each result are concatenated with the hash value of the previous computing step to generate a new hash value. The new hash value represents the integrity of the intermediate result of the step. The hash values of the intermediate results are sequentially stored in the nodes of the recursive tree according to the computing path. The generated hash value of the root node of the recursive tree is uploaded to the blockchain for on-chain storage of subsequent result verification;

[0072] Step 5: Based on the hash value of the root node of the recursive tree generated in Step 4, all participating parties jointly complete the decryption of the final computing result. The decrypted result is verified for credibility by all participating parties by comparing it with the hash value of the root node of the recursive tree stored in the blockchain. If the verification passes, the decrypted result is considered complete and credible and is used for subsequent processing of cross-platform business integration.

[0073] Benefits of Step 1: Secret sharing sharding makes it impossible for a single participating party to restore the original data, while partial homomorphic encryption allows direct calculation on encrypted data without decryption, significantly enhancing data privacy. Generating an integrity hash value for the original data and storing it in the blockchain ensures that the data is not tampered with during transmission and calculation. The distribution of sharded data and the generation of encrypted data lay the foundation for the distributed collaboration of subsequent computing tasks and support data classification processing for different tasks;

[0074] Benefits of Step 2: Support the dynamic joining and exiting of participating parties. By updating the key shards, the security and computing consistency of the existing data are prevented from being affected by the newly joined and exited participating parties. By regenerating the sharded data through the shard adjustment factor, data leakage and loss caused by the dynamic change of participating parties are avoided. By redistributing the sharded data of the exited participating parties, the continuous progress of the computing task and the stability of the overall collaboration are ensured;

[0075] Benefits of Step 3: Layer the lightweight computing tasks and complex non-linear tasks for processing, effectively optimizing the allocation of computing resources and improving the overall computing efficiency. The complex non-linear tasks call encrypted data for non-linear calculation, ensuring the privacy of private data while completing the collaborative computing task. Using the Boolean circuit model to decompose the computing task into logical gate operations makes the computing process refined and verifiable, facilitating the execution of complex computing tasks;

[0076] Benefits of Step 4: The hash values of the intermediate results of each step are concatenated with the previous result to generate a new hash value, making the computing path transparently verifiable. After the hash value of the root node of the recursive tree is uploaded to the blockchain, the integrity and consistency of the intermediate results during the computing process are ensured. Using the recursive tree structure to store the hash value of the root node reduces the storage requirements of the blockchain and retains the complete path information required for computing verification;

[0077] Benefits of Step 5: By comparing the hash value of the decryption result with the hash value of the root node of the recursion tree stored in the blockchain, the authenticity and credibility of the calculation result are ensured. The decryption process is jointly completed by all participating parties, avoiding a single participating party from manipulating the decryption result and further ensuring the privacy and security of the calculation result. The result verification depends on the recursion tree path, making the calculation process and result have complete traceability, which is convenient for subsequent auditing and inspection.

[0078] The sharded data generated through the secret sharing protocol in Step 1 satisfies the following relationship:

[0079]

[0080] where x i is the original data of the i-th business participating party, s i，j is the j-th sharded data of the i-th participating party, n is the total number of shards, and p is the prime modulus value used to protect data privacy;

[0081] Each shard s i，j is generated, where the last shard is calculated through the following formula:

[0082]

[0083] where s i，n is the n-th sharded data of the i-th participating party.

[0084] The data x i of each business participating party is sharded into n parts through the secret sharing protocol. Among them, each shard s i，j is a generated data segment, and the original data cannot be restored from a single shard. The generation method of the last shard s i，n ensures that the sum of the shards can completely restore the data x i by difference calculation, ensuring that even if some shards are leaked, the data x i cannot be inferred, effectively enhancing the privacy protection ability.

[0085] The sharded data generated through the secret sharing protocol can flexibly expand the number of shards n. When the number of participating parties changes, adjusting the number of shards can meet the needs of dynamic participating parties. Especially when introducing dynamic multi-party secure computing in subsequent steps, it provides a basis for adjusting the shards of newly added and withdrawn participating parties, making the cross-platform business integration highly adaptable.

[0086] The generation process of the sharded data provides a basis for subsequent distributed computing tasks. Both lightweight computing tasks and complex non-linear computing tasks are directly processed distributively based on the sharded data. The variability and consistency of the sharded data ensure the task allocation of distributed computing.

[0087] In Step 1, for the data that needs to participate in the non - linear calculation, the generated partially homomorphic encrypted data E(x i ) satisfies the following encryption relationship:

[0088]

[0089] where g is the encryption base, x i is the original data, N is the modulus, and modN represents the modulo operation;

[0090] E(x i ) supports addition and multiplication operations in the encrypted domain and satisfies the following properties:

[0091] E(x i +x j ) = E(x i )·E(x j )(modN),

[0092] where E(x i +x j ) represents the encrypted result after adding x i and x j in the encrypted domain, E(x i ) and E(x j ) are the encrypted forms of x i and x j , N is the modulus, and modN represents the modulo operation.

[0093] Partial homomorphic encryption encrypts the key data that needs non - linear calculation, rather than applying encryption operations to all data. This optimized design significantly reduces the computational complexity. Compared with fully homomorphic encryption, partial homomorphic encryption supports limited addition and multiplication operations, reducing the operation overhead, especially showing performance advantages in large - scale computing tasks in cross - platform collaboration.

[0094] By directly performing calculations in the encrypted domain, it avoids the errors and tampering risks introduced by multiple encryption and decryption operations in the traditional calculation method. The encryption relationship between the encrypted result and the original data is strictly determined, making the process and result of encrypted calculation credible and providing a basis for the trusted verification of cross - platform business integration.

[0095] In summary, the encrypted data generated by the partial homomorphic encryption technology has the following benefits:

[0096] During the calculation process, the data always remains encrypted, avoiding privacy leakage caused by decryption operations.

[0097] Directly perform addition and multiplication operations in the encrypted domain to implement privacy - preserving computing tasks in cross - platform collaboration without data decryption.

[0098] Compared with fully homomorphic encryption, partial homomorphic encryption reduces computational overhead and improves processing efficiency, making it suitable for large-scale collaborative tasks.

[0099] Modular arithmetic ensures the consistency and accuracy of encrypted data during the calculation process, providing guarantee for the consistency of the decrypted results.

[0100] Supports dynamic multi-party collaboration scenarios and maintains the adaptability of encrypted data when the number of participating parties changes.

[0101] Operations in the encrypted domain directly correspond to the actual calculation results, avoiding data tampering and errors, and providing guarantee for result verification.

[0102] Through the partial homomorphic encryption technology, the present invention solves the conflict between data privacy and distributed computing, shows significant advantages in terms of computing efficiency, consistency and dynamic adaptability, and provides an efficient, secure and trustworthy privacy computing solution for cross-platform business integration.

[0103] In step 2, when a new participating party joins, the existing shard data is adjusted through the following formula:

[0104] s′ i，j =s i，j +r i，new (modp),

[0105] where s i，j is the shard data before adjustment, s′ i，j is the shard data after adjustment, r i，new is the generated adjustment factor, and p is a preset large prime modulus value;

[0106] When a certain participating party exits, the shard data is redistributed in the following way:

[0107] s′ i，j =s i，j -s i，exit (modp),

[0108] where s i，j is the shard data before adjustment, s′ i，j is the shard data after adjustment, s i，exit is the shard data held by the exiting party, and p is a preset large prime modulus value.

[0109] By dynamically adjusting the shard data in the scenarios of new participating party joining and existing participating party exiting, the present invention realizes the following benefits in a dynamic collaboration environment:

[0110] Dynamically adjust the shard data, support the flexible joining and exiting of participating parties, and there is no need to re-initialize the system.

[0111] Using a random adjustment factor and an exit shard clearing mechanism to ensure data security and avoid threats to data privacy posed by new and exiting participants.

[0112] Maintaining the secret sharing relationship between shard data and the original data through modular arithmetic to ensure the correctness of the calculation results.

[0113] The reallocation mechanism for the shards of the exiting party avoids calculation interruption and ensures the continuity of task execution.

[0114] Strengthening the data privacy protection ability in dynamic scenarios through dynamic adjustment and exit clearing mechanisms.

[0115] After the change of participants, the adjusted shard mechanism still meets the mathematical requirements of the computing task, providing an operating basis for cross-platform business collaboration.

[0116] The shard adjustment mechanism designed in the present invention for the joining and exiting of dynamic participants provides a comprehensive solution for data management and privacy protection in cross-platform business collaboration in a dynamic multi-party environment. At the same time, it effectively improves the flexibility of the system and the continuity of computing tasks, demonstrating strong practicality and stability.

[0117] In step 3, the lightweight computing task performs data addition and multiplication based on the combined operation of shard data. Among them, the square operation is calculated through the following formula:

[0118]

[0119] Among them, is the square value of the data of the i-th participant, s i，j and s i，k are shard data, n is the total number of shards, p is a preset large prime modulus value,

[0120] represents the sum of the squares of the shard data,

[0121] 1 ≤ j < k ≤ n means that j and k are different shard indices and j < k.

[0122] In step 3, the logarithmic operation involving complex non-linear computing tasks is calculated through homomorphic encrypted data, satisfying the following relationship:

[0123]

[0124] Among them, ln(1 + x j ) is the logarithmic value of the data of the j-th participant, g and N are the encryption base and modulus, E(ln(1 + x j )) is the encrypted logarithmic value, and modN represents modular arithmetic.

[0125] The computational design of the present invention for lightweight tasks and complex non-linear tasks in step 3 exhibits the following advantages:

[0126] The square operation is completed through the combined operation of sharded data, supporting parallel computing and significantly improving the task processing efficiency.

[0127] Logarithmic operations are directly performed on encrypted data through homomorphic encryption technology without decryption, ensuring data privacy.

[0128] The results of both lightweight tasks and complex tasks are strictly consistent with the original data, avoiding errors in distributed computing.

[0129] Through a hierarchical optimization strategy, different computing methods are adopted according to task complexity, reducing resource consumption while improving efficiency.

[0130] It can adapt to diverse computational task requirements, and both simple algebraic calculations and complex non-linear models can be efficiently executed.

[0131] The present invention realizes the unification of privacy protection, efficient computing, and result consistency in the computational design of lightweight tasks and complex tasks, providing a secure, reliable, and flexible computing solution for cross-platform business integration.

[0132] In step 4, the intermediate results of the computational tasks are stored in a recursive tree, and the hash values of each intermediate result are generated as follows:

[0133] H k+1 = H(H k ||H k+1 ),

[0134] where H k is the hash value of the intermediate result at the k-th step, H k+1 is the hash value of the intermediate result at the (k + 1)-th step, and || represents the concatenation operation;

[0135] H k+1 is stored in the nodes of the recursive tree for verifying the computational path.

[0136] The hash value H root of the root node of the recursive tree is calculated by the following formula:

[0137] H root = H(H k-1 ||H k ),

[0138] where H root is the hash value of the root node of the recursive tree, H k-1 and H k are the hash values of the intermediate results at the (k - 1)-th and k-th steps, and || represents the concatenation operation;

[0139] The root node hash value is uploaded to the blockchain for verifying the integrity and credibility of the calculation.

[0140] In step 4 of the present invention, the intermediate results of the calculation task and the upload of the root node hash value are stored through a recursive tree, showing the following advantages:

[0141] The recursive tree records each step path of the calculation task, ensuring that the intermediate results are public and verifiable to all participating parties.

[0142] By uploading the root node hash value to the blockchain, the integrity of the calculation path is ensured, preventing data from being tampered with.

[0143] The combination of the recursive tree and the blockchain provides a strong proof of credibility for the calculation results, establishing trust in a collaborative environment.

[0144] Storing the root node hash value greatly reduces the storage pressure on the blockchain. At the same time, the ability to trace all intermediate results is maintained.

[0145] The hierarchical structure of the recursive tree supports distributed verification. Each participating party only needs to verify the calculation nodes related to it to participate in the overall verification of the results.

[0146] The intermediate results are located through the recursive tree path, facilitating subsequent auditing and anomaly checking.

[0147] Through the design of combining the recursive tree and the blockchain, the present invention realizes the unity of transparency, credibility and integrity of the calculation task, providing an efficient, secure and traceable solution for cross-platform business integration, and is particularly suitable for complex collaborative environments that require transparency and high credibility requirements.

[0148] The final result after decryption in step 5 is represented by the following formula:

[0149] y = D(E(y)),

[0150] where y is the combined calculation result, E(y) is the encrypted final calculation result, and D is the corresponding decryption function.

[0151] In step 5, through the root node hash value H of the recursive tree root and the hash value H of the final calculation result root credible verification is carried out, where the verification formula is as follows:

[0152] H result = H(H k-1 || H k ), H root = H(H result || H final ),

[0153] where Hk-1 and H k is the hash value of the intermediate results at steps k-1 and k, and H result is the hash value of the final task result calculated based on the intermediate results, and H final is the hash value generated for the final decryption result y

[0154] H root represents the hash value of the root node of the recursive tree, stored in the blockchain, and is used to verify the integrity of the calculation path and the final result.

[0155] Through the decryption and verification design in step 5, the present invention shows the following benefits in terms of ensuring the credibility of the calculation results and establishing collaborative trust:

[0156] The hash value H of the root node of the recursive tree root associates the path with the result and prevents path tampering through blockchain verification.

[0157] By means of the joint decryption mechanism and the recursive tree verification path design, the privacy of the final result is protected, and the risk caused by unilateral decryption is prevented.

[0158] The H stored in the blockchain root provides complete traceability for the calculation path and the result.

[0159] Without relying on a trusted third party, a strong trust foundation is established among multiple parties through the verification mechanisms of the root node and the final result.

[0160] Verification can be completed by comparing the hash value of the root node and the hash value of the final result, significantly reducing the computational overhead.

[0161] By combining joint decryption, recursive tree storage, and blockchain verification, the present invention achieves comprehensive optimization in terms of the credibility of the calculation results, path integrity, privacy protection, and collaborative trust construction, providing a secure, efficient, and trustworthy solution for complex computational tasks in cross-platform business integration.

[0162] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cross-platform business integration handling method based on blockchain, characterized in that, Including: Step 1: Each business participant obtains private data and performs encryption processing. The private data is fragmented through a secret sharing protocol. Each fragment generates sub-data according to a preset rule, and the fragmented data is distributed to the participants. For the data that needs to participate in non-linear calculations, encrypted data is generated using partial homomorphic encryption technology. Each participant uploads the fragmented data and the encrypted data to the blockchain platform, and generates a corresponding integrity hash value for the original data; Step 2: Based on the encrypted data and fragmented data uploaded to the blockchain in Step 1, each participant establishes a dynamic multi-party secure computing protocol according to the set computing task target, constructs a set of computing participants. When a new participant joins, the key fragments are updated, and the fragmented data is regenerated by adding an adjustment factor to the existing fragmented data. For the exiting participant, the held fragmented data is distributed to the remaining participants through reallocation; Step 3: After the start of distributed dynamic computing is completed, based on the constructed set of participants and fragmented data, multi-layer computing tasks are performed on the private data according to the computing task target. The lightweight computing tasks include addition and multiplication operations of participant data, and perform combined operations of fragmented data among different participants. For the private data involving complex non-linear computing tasks, the encrypted data is called to perform logarithmic and exponential operations in the encrypted domain, generating corresponding intermediate results. The computing tasks are all decomposed into logic gate operations according to the Boolean circuit model, and the intermediate results of the computing tasks will be verified and stored in the subsequent steps; Step 4: During the execution of the multi-layer computing tasks, each intermediate result generated is verified by calculating the hash value. The hash value of each result is concatenated with the hash value of the previous computing step to generate a new hash value. The new hash value represents the integrity of the intermediate result of the step. The hash value of the intermediate result is stored in the nodes of the recursive tree in sequence according to the computing path, and the generated hash value of the root node of the recursive tree is uploaded to the blockchain for on-chain storage of subsequent result verification; In Step 4, the intermediate results of the computing tasks are stored through a recursive tree. Among them, the hash value of each intermediate result is generated in the following way: H k+1 = H(H k || H k+1 ) Among them, H k is the hash value of the intermediate result at the k-th step, and H k+1 is the hash value of the intermediate result at the (k + 1)-th step; || represents the concatenation operation; H k+1 Stored in the nodes of the recursive tree for verifying the calculation path; The hash value H of the root node of the recursive tree root is calculated by the following formula: H root = H(H k-1 || H k ) Among them, H root is the hash value of the root node of the recursive tree, H k-1 and H k are the hash values of the intermediate results at the (k - 1)-th step and the k-th step, and || represents the concatenation operation; The hash value of the root node is uploaded to the blockchain for verifying the integrity and credibility of the calculation; Step 5: Based on the hash value of the root node of the recursive tree generated in Step 4, all participants jointly complete the decryption of the final calculation result. The decrypted result is verified for credibility by all participants by comparing it with the hash value of the root node of the recursive tree stored in the blockchain. If the verification passes, the decrypted result is regarded as complete and credible, and is used for subsequent processing of cross-platform business integration.

2. The cross-platform service integration handling method based on blockchain according to claim 1, characterized in that, The fragmented data generated through the secret sharing protocol in Step 1 satisfies the following relationship: where x i is the original data of the i-th service participant, s i，j is the j-th shard data of the i-th participant, n is the total number of shards, and p is the prime modulus value used to protect data privacy; Each shard s i，j is generated, where the last shard is calculated by the following formula: where s i，n is the nth shard data of the ith participating party.

3. A cross-platform business integration handling method based on blockchain according to claim 1, characterized in that, In the above step 1, for the data that needs to participate in the non-linear calculation, the generated partially homomorphic encrypted data E(x i ) satisfies the following encryption relationship: where g is the encryption base, x i is the original data, N is the modulus, and modN represents the modulo operation; E(x i ) supports addition and multiplication operations in the encrypted domain and satisfies the following properties: E(x i +x j ) = E(x i ) · E(x j )(mod N), Among them, E(x i +x j ) represents the encrypted result after adding x i and x j in the encrypted domain. E(x i ) and E(x j ) are the encrypted forms of x i and x j . N is the modulus, and modN represents the modulo operation.

4. A cross-platform business integration and handling method based on blockchain according to claim 1, characterized in that In Step 2, when a new participant joins, the existing fragmented data is adjusted through the following formula: s′ i，j = s i，j + r i，new (mod p), Among them, s i，j is the sharded data before adjustment, s' i，j is the sharded data after adjustment, r i，new is the generated adjustment factor, and p is the preset large prime modulus value; When a certain participant exits, the fragmented data is reallocated in the following way: s′ i，j = s i，j - s i，exit (mod p), Among them, s i，j is the sharded data before adjustment, s′ i，j is the sharded data after adjustment, s i，exit is the sharded data held by the exiting party, and p is a preset large prime modulus value.

5. A cross-platform business integration and handling method based on blockchain according to claim 1, characterized in that, In Step 3, the lightweight computing tasks perform data addition and multiplication based on the combined operation of fragmented data. Among them, the square operation is calculated through the following formula: Among them, is the square value of the data of the i-th participating party, s i，j and s i，k are shard data, n is the total number of shards, and p is a preset large prime modulus value. represents the sum of the squares of the sharded data, 1≤j<k≤n means that j and k are different fragment indices and j < k.

6. The cross-platform service integration handling method based on blockchain according to claim 5, characterized in that, In step 3, the logarithmic operation involving complex non-linear calculation tasks is calculated using homomorphic encrypted data and satisfies the following relationship: where, ln(1 + x j ) is the logarithm value of the data of the j-th participant, g and N are the encryption base and modulus, E(ln(1 + x j )) is the encrypted logarithm value, and modN represents the modulo operation.

7. A cross-platform business integration handling method based on blockchain according to claim 1, characterized in that, In step 5, the final result after decryption is represented by the following formula: y = D(E(y)), where y is the combined calculation result, E(y) is the final encrypted calculation result, and D is the corresponding decryption function.

8. A cross-platform business integration handling method based on blockchain according to claim 7, characterized in that In step 5, the credibility verification is performed through the hash value H of the root node of the recursive tree root and the hash value H of the final calculation result root where the verification formula is as follows: H result = H(H k-1 || H k ), H root = H(H result || H final ) Among them, H k-1 and H k are the hash values of the intermediate results at the (k - 1)-th and k-th steps, H result is the hash value of the final task result calculated based on the intermediate results, and H final is the hash value generated for the final decryption result y. H root Represents the hash value of the root node of the recursive tree, stored in the blockchain, and is used to verify the integrity of the calculation path and the final result.

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