Generation method and device of proof chain, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a proof chain generation method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology or other related fields. The method comprises the following steps: acquiring network performance data and transaction characteristics of each participating object in a plurality of participating objects; determining a plurality of verification groups according to the network performance data and the transaction characteristics of each participating object, the verification groups comprising at least one participating object in the plurality of participating objects; for any verification group, performing encryption processing on transaction data corresponding to each participating object in the verification group, and generating an encryption intermediate result corresponding to the verification group; and based on the encryption intermediate result corresponding to each verification group, generating a cryptographic proof chain corresponding to the plurality of participating objects. According to the method, the reliability of data storage is improved.

Description

Technical Field

[0001] This application relates to the field of financial technology or other related fields, and in particular to a method, apparatus, device, storage medium and program product for generating a proof chain. Background Technology

[0002] In the financial sector, collaborative analysis of cross-institutional transaction data is crucial for identifying complex and unusual trading behaviors. Financial institutions (such as banks, securities companies, and insurance companies) need to share transaction data to construct fund flow maps and identify abnormal trading patterns, while protecting customer privacy.

[0003] In related technologies, participants can be required to upload raw transaction data to a central server for centralized analysis. To prevent data leakage, data anonymization techniques (such as k-anonymization) or encryption techniques (such as symmetric encryption and asymmetric encryption) can be used to protect the privacy of transaction data. However, encrypted transaction data lacks verifiability, and regulatory agencies or third-party auditors cannot effectively verify the correctness of the calculation process, leading to a black-box computation that triggers a trust crisis and results in low data reliability. Summary of the Invention

[0004] This application provides a method, apparatus, device, storage medium, and program product for generating a chain of proofs to solve the technical problem of low data reliability.

[0005] Firstly, this application provides a method for generating a proof chain, comprising:

[0006] Obtain network performance data and transaction characteristics of each participant among multiple participants;

[0007] Based on the network performance data of each participant and the transaction characteristics, multiple verification groups are determined, and each verification group includes at least one of the multiple participants.

[0008] For any verification group, the transaction data corresponding to each participating object in the verification group is encrypted to generate an encrypted intermediate result corresponding to the verification group.

[0009] Based on the encrypted intermediate results corresponding to each verification group, a cryptographic proof chain corresponding to the multiple participating objects is generated.

[0010] In some possible embodiments, multiple verification groups are determined based on the network performance data of each participant and the transaction characteristics, including:

[0011] Based on the network performance data and transaction characteristics of each participating entity, a comprehensive institutional score for each participating entity is determined.

[0012] Obtain the preset number of groups corresponding to the multiple verification groups, and the range of the number of institutions corresponding to each verification group;

[0013] Based on the preset number of verification groups, the range of the number of institutions corresponding to each verification group, and the comprehensive institutional score of each participating object, the multiple participating objects are divided into verification groups to obtain the multiple verification groups.

[0014] Secondly, embodiments of this application provide a proof chain generation apparatus, including an acquisition module, a determination module, an encryption processing module, and a generation module:

[0015] The acquisition module is used to acquire network performance data and transaction characteristics of each participant among multiple participants;

[0016] The determining module is used to determine multiple verification groups based on the network performance data of each participating object and the transaction characteristics, wherein the verification group includes at least one participating object among the multiple participating objects;

[0017] The encryption processing module is used to encrypt the transaction data corresponding to each participating object in any verification group, and generate the encrypted intermediate result corresponding to the verification group.

[0018] The generation module is used to generate a cryptographic proof chain corresponding to the multiple participating objects based on the encrypted intermediate results corresponding to each verification group.

[0019] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0020] The memory stores computer-executed instructions;

[0021] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0023] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0024] The proof chain generation method, apparatus, device, storage medium, and program product provided in this application can acquire network performance data and transaction characteristics of each participant among multiple participants; determine multiple verification groups based on the network performance data and transaction characteristics of each participant; for any verification group, encrypt the transaction data corresponding to each participant in the verification group to generate an encrypted intermediate result corresponding to the verification group; and generate a cryptographic proof chain corresponding to multiple participants based on the encrypted intermediate results corresponding to each verification group. The cryptographic proof chain enables end-to-end verifiability of transaction data encryption, allowing regulatory agencies to verify the correctness of the analysis process without obtaining the original data, completely solving the black-box computation problem and improving the reliability of data verification. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0026] Figure 1 A schematic diagram illustrating an application scenario provided in an embodiment of this application;

[0027] Figure 2 A flowchart illustrating a data processing method provided in an embodiment of this application;

[0028] Figure 3 A flowchart illustrating another method for generating a proof chain provided in an embodiment of this application;

[0029] Figure 4 A schematic diagram of a dynamic grouping verification architecture provided for an embodiment of this application;

[0030] Figure 5 A schematic diagram of a data flow architecture provided for an embodiment of this application;

[0031] Figure 6 This is a schematic diagram of the structure of a proof chain generation device provided in an embodiment of this application;

[0032] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0033] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0034] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0035] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0036] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0037] It should be noted that the proof chain generation method, apparatus, equipment, storage medium and program product provided in this application can be used in the field of financial technology or other related fields, or in any field other than the field of financial technology or other related fields. The application field of the proof chain generation method, apparatus, equipment, storage medium and program product in this application is not limited.

[0038] Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. Please refer to [link / reference]. Figure 1 This application scenario can include multiple object devices 101, processing devices 102, and storage devices 103 corresponding to multiple participating entities. The multiple participating entities are institutions participating in the collaborative analysis of cross-institutional transaction data. Storage device 103 is a storage system used to store the transaction data, cryptographic proof chains, and blockchain-based evidence data of each participating entity. Object device 101 is the transaction data storage and local preprocessing device for its corresponding participating entity.

[0039] Each object device 101 can send transaction data and related feature data to the processing device 102 in real time. The processing device 102 can adjust the grouping strategy of each participating object in real time based on network performance data and transaction features. It performs encrypted calculation on the transaction data through an improved Secure Multi-Party Computation (MPC) protocol to generate encrypted intermediate results. The encrypted intermediate results are transmitted through a secure channel to the Trusted Execution Environment (TEE) aggregation node built into the processing device 102. Denoising and global aggregation calculation are completed in a hardware encryption environment. Finally, the global results are combined with the correlation graph to generate a cryptographic proof chain. After verification, the proof data and global result hash are stored in the blockchain corresponding to the storage device 103 for public auditing.

[0040] Among them, the proof chain is an immutable verification link built with layered chain-based zero-knowledge proof (ZKP) as its core. It is used to prove the correctness of the entire process from the original transaction encryption processing, intra-group MPC calculation, TEE global aggregation to the generation of the association graph, without leaking any sensitive transaction data.

[0041] In related technologies, participants can be required to upload raw transaction data to a central server for centralized analysis. To prevent data leakage, data anonymization techniques (such as k-anonymization) or encryption techniques (such as symmetric encryption and asymmetric encryption) can be used to protect the privacy of transaction data. However, encrypted transaction data lacks verifiability, and regulatory agencies or third-party auditors cannot effectively verify the correctness of the calculation process, leading to a black-box computation that triggers a trust crisis and results in low data reliability.

[0042] The proof chain generation method provided in this application can obtain network performance data and transaction characteristics of each participant among multiple participants; determine multiple verification groups based on the network performance data and transaction characteristics of each participant; for any verification group, encrypt the transaction data corresponding to each participant in the verification group to generate an encrypted intermediate result corresponding to the verification group; and generate a cryptographic proof chain corresponding to multiple participants based on the encrypted intermediate results corresponding to each verification group. The cryptographic proof chain enables end-to-end verification of transaction data encryption, allowing regulatory agencies to verify the correctness of the analysis process without obtaining the original data, completely solving the black-box computation problem and improving the reliability of data verification.

[0043] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0044] Figure 2 This is a flowchart illustrating a data processing method provided in an embodiment of this application. Please refer to [link / reference]. Figure 2 The method may include:

[0045] S201. Obtain the network performance data and transaction characteristics of each participant among multiple participants.

[0046] The execution entity in this application embodiment can be a processing device or a proof chain generation device disposed in the processing device. The proof chain generation device can be implemented by software or by a combination of software and hardware.

[0047] Multiple participants are institutions involved in the collaborative analysis of cross-institutional transaction data.

[0048] The network performance data of each participant can be collected by the network status monitoring module built into the processing device. Transaction features can be extracted by the transaction-related data uploaded in real time by the corresponding device of each participant, ensuring the real-time and accuracy of data acquisition.

[0049] Network performance data can be used to indicate the performance of data transmission of the corresponding target device. Network performance data can include communication latency (e.g., the average latency between institutions in the last 10 minutes), bandwidth (e.g., the minimum available uplink / downlink bandwidth of the institution), and packet loss rate (e.g., the proportion of data packets lost between institutions). All network performance data have undergone real-time sampling and normalization preprocessing to provide standardized input for subsequent group calculations.

[0050] Transaction features can be used to indicate the characteristics of the transaction data of the corresponding participants. Transaction features may include transaction amount (e.g., the total transaction amount of an institution in the last hour), frequency (e.g., the number of transactions of an institution in the last hour), association complexity (quantitative scoring based on the number of counterparties / fund flow branches, with a value range of 1-5 points), etc.

[0051] S202. Based on the network performance data and transaction characteristics of each participant, determine multiple verification groups.

[0052] A validation group may include at least one of multiple participating objects.

[0053] For example, suppose there are 10 participants, namely participants 1-10, and 3 verification groups can be identified, namely verification groups 1-3. Verification group 1 corresponds to participants 1-3, verification group 2 corresponds to participants 4-6, and verification group 3 corresponds to participants 7-10.

[0054] In some embodiments, the comprehensive institutional score of each participant can be determined based on the network performance data and transaction characteristics of each participant; the preset number of groups corresponding to multiple verification groups and the range of the number of institutions corresponding to each verification group can be obtained; based on the preset number of groups and the range of the number of institutions, the multiple participants can be divided into multiple verification groups according to the comprehensive institutional score of each participant.

[0055] The overall score of an institution can be a weighted sum of the overall network performance score and the overall transaction characteristic score of the participating entity.

[0056] For example, the preset number of groups can be 3, and the number of participating institutions corresponding to each verification group can be between 3 and 5.

[0057] This application enables dynamic adaptability and optimization of verification group division, and strengthens data privacy protection through transaction feature adaptability grouping, thereby improving the accuracy and security of subsequent encrypted computation and aggregation analysis.

[0058] S203. For any verification group, encrypt the transaction data corresponding to each participant in the verification group and generate the encrypted intermediate result corresponding to the verification group.

[0059] The transaction data of each participant in the verification group consists of the original transaction details, including sensitive data such as counterparty information, amount, and timestamp.

[0060] By employing encrypted preprocessing, distributed interference, and aggregation operations, encrypted intermediate results that can support subsequent global aggregation can be generated while ensuring data privacy is not compromised, thus achieving the goal of making data usable but invisible.

[0061] Specifically, a homomorphic encryption algorithm can be used to encrypt each transaction data corresponding to the verification group to obtain encrypted data for each transaction data; generate interference numbers corresponding to the verification group; and perform aggregation operations on the encrypted data and interference numbers corresponding to each transaction data to generate encrypted intermediate results.

[0062] The homomorphic encryption algorithm is the Paillier homomorphic encryption algorithm, which is a public-key encryption algorithm that supports addition homomorphism and can perform summation operations in the encrypted state without decrypting the original data.

[0063] The system's built-in distributed random number generator can be used to collaboratively generate a globally consistent set of random interference numbers by all participating institutions in the verification group. The interference numbers can be large integers, and their range must cover the volume of transaction data to ensure that reverse derivation is not feasible.

[0064] Based on the additive homomorphic property of Paillier homomorphic encryption, the encrypted data of each institution and the encrypted form of random interference numbers can be aggregated and operated in the encrypted state to generate the encrypted intermediate result corresponding to the verification group.

[0065] In this application, the encrypted data corresponding to the transaction data is used to determine the corresponding encrypted intermediate result, so that only its own encrypted data and the global encrypted intermediate result can be obtained, and the original data or data fragments of other institutions cannot be inferred, thus completely eliminating the risk of data leakage. By adding random interference numbers, the encrypted intermediate result can be intercepted, and attackers cannot deduce the sum of the real transaction data through brute force or mathematical deduction, which can improve the reliability of data verification.

[0066] S204. Based on the encrypted intermediate results corresponding to each verification group, generate cryptographic proof chains corresponding to multiple participating objects.

[0067] In some embodiments, the encrypted intermediate results corresponding to each verification group are transmitted to the trusted execution environment aggregation node; through the trusted execution environment aggregation node, denoising and global aggregation operations are performed in a hardware-level encrypted environment to obtain the target aggregated data corresponding to each verification group; and a cryptographic proof chain is generated based on the target aggregated data corresponding to each verification group.

[0068] Encrypted intermediate results can be transmitted to the trusted execution environment aggregation node via an end-to-end encrypted secure channel.

[0069] Trusted Execution Environment (TEE) aggregation nodes are built into the processing device and perform computations within a hardware-isolated secure zone.

[0070] The cryptographic proof chain, the hash value of the target aggregated data, and the hash value of the fund association graph can be submitted together to the verification smart contract of the consortium blockchain / public blockchain. The contract records the proof chain index and the evidence storage timestamp to achieve tamper-proof evidence storage, which can be accessed and verified by regulatory agencies and third-party auditors at any time.

[0071] In this application, the verifier can verify the transaction without obtaining the original transaction data, simply by calling the publicly available commitment value and hash value through a smart contract, sequentially verifying the transaction according to the proof chain hierarchy, thus completely solving the black-box computation problem. From data transmission and aggregation computation to proof generation, the entire process is protected by triple protection through encryption technology, hardware isolation, and zero-knowledge proofs, ensuring that the original transaction data and intermediate computation processes are not leaked, meeting high compliance requirements for data privacy. In addition, the immutability of blockchain enables the proof chain to have permanent storage and traceability capabilities. Combined with the remote proof mechanism, it can effectively avoid the risk of malicious computing nodes and improve the efficiency and credibility of regulatory audits.

[0072] The proof chain generation method provided in this application can obtain network performance data and transaction characteristics of each participant among multiple participants; determine multiple verification groups based on the network performance data and transaction characteristics of each participant; for any verification group, encrypt the transaction data corresponding to each participant in the verification group to generate an encrypted intermediate result corresponding to the verification group; and generate a cryptographic proof chain corresponding to multiple participants based on the encrypted intermediate results corresponding to each verification group. The cryptographic proof chain enables end-to-end verification of transaction data encryption, allowing regulatory agencies to verify the correctness of the analysis process without obtaining the original data, completely solving the black-box computation problem and improving the reliability of data verification.

[0073] Figure 3 This is a flowchart illustrating another method for generating a proof chain provided in an embodiment of this application. Please refer to [link / reference]. Figure 3 The method may include:

[0074] S301. Obtain the network performance data and transaction characteristics of each participant among multiple participants.

[0075] The execution process of S301 can be found in the execution process of S201, and will not be repeated here.

[0076] S302. Determine the comprehensive institutional score of each participant based on their network performance data and transaction characteristics.

[0077] Specifically, network performance indicators corresponding to network performance data can be determined based on multiple sub-performance data and their corresponding weights; data feature indicators corresponding to transaction features can be determined based on multiple sub-features and their corresponding weights; and the sum of network performance indicators and data feature indicators can be determined as the institution's comprehensive score.

[0078] Network performance data can include multiple sub-performance data, such as communication latency, bandwidth, and packet loss rate.

[0079] Transaction characteristics can include multiple sub-characteristics, which can be transaction amount, frequency, correlation complexity, etc.

[0080] When multiple sub-performance data can be communication latency, bandwidth, and packet loss rate, network performance indicators can be determined as follows: First, normalize each sub-performance data by using the preset maximum value corresponding to each sub-performance data (to eliminate differences in dimensions). Then, weight the normalized sub-performance data with its corresponding weight and sum them to obtain the network performance indicator. See the following formula for details.

[0081]

[0082] in, Due to communication delay, The maximum communication delay preset for the system (e.g., 500ms). Weights corresponding to communication delays; For bandwidth, The maximum bandwidth preset for the system (e.g., 1000Mbps). The weight corresponds to the bandwidth; This represents the packet loss rate (within the range of 0-1). The weights corresponding to the packet loss rate; This is a network performance metric (with a value ranging from 0 to 1, where a higher value indicates better network performance).

[0083] When multiple sub-features can be transaction amount, transaction frequency, and correlation complexity, the data is normalized by using the preset maximum value corresponding to each sub-feature. This ensures that the better the sub-feature (higher amount, higher frequency, higher correlation complexity), the higher the score after normalization. The specific formula is as follows:

[0084]

[0085] in, For the transaction amount, The maximum transaction amount preset by the system (e.g., 100 million yuan). Weights corresponding to transaction amounts; For trading frequency, Set the maximum preset transaction frequency for the system (e.g., 1000 transactions / hour). Weights are assigned based on the frequency of transactions; The complexity is related (values ​​range from 1 to 5). The weights correspond to the complexity of association. These are data feature indicators (with values ​​ranging from 0 to 1; higher values ​​indicate a stronger fit between the transaction features and the target scenario).

[0086] The sum of the weights corresponding to each sub-performance data is the total weight of the network performance index. , The sum of the weights corresponding to each sub-feature is the total weight of the data feature index. And the total weight of network performance indicators and the total weight of data feature indicators The sum is 1.

[0087] The weight values ​​can be dynamically adjusted according to the business scenario. For example, in cross-institutional financial transaction scenarios where transaction security is emphasized, the weight can be set to... , When prioritizing computational efficiency, one can set... , .

[0088] The sum of network performance indicators and data characteristic indicators can be used to determine the comprehensive score of the organization. The comprehensive score of the organization ranges from 0 to 2. The higher the value, the more suitable the participating object is to be classified into the same group for collaborative calculation.

[0089] In this application, the priority requirements of business for network performance and transaction features can be accurately matched by the weights corresponding to each sub-performance data and each sub-feature. Furthermore, a quantified comprehensive institutional score ensures the scientific and reasonable nature of the validation group division. Abstract network performance and transaction features are transformed into calculable score values, avoiding subjectivity in grouping decisions and improving the consistency of grouping results. Institutions with superior network performance and suitable transaction features are grouped together using score values, reducing cross-group communication losses, improving MPC computation efficiency, and strengthening data privacy protection through transaction feature similarity clustering, thus reducing the risk of sensitive information leakage.

[0090] S303. Obtain the preset number of groups corresponding to multiple verification groups, and the range of the number of institutions corresponding to each verification group.

[0091] In some embodiments, the preset number of groups and the range of the number of institutions corresponding to each verification group can be obtained directly through fixed parameters pre-configured by the system (e.g., the default setting is 3 groups and the range of the number of institutions is [5,10]).

[0092] This mode is suitable for scenarios with stable transaction characteristics and minimal network fluctuations. It can quickly complete grouping by simplifying configuration, but it is only suitable for simple and fixed scenarios and not for complex and dynamic businesses.

[0093] In some embodiments, to address the problems of unbalanced allocation of computing resources and inability to adapt to dynamic changes in transaction characteristics and network status caused by static grouping strategies, transaction characteristic classification and network status optimization are adopted to dynamically determine the preset number of groups and the range of the number of institutions corresponding to each verification group.

[0094] Specifically, by using a pre-set inference model, multiple transaction data are classified based on the transaction characteristics of each transaction data to obtain the corresponding classification scenarios for each transaction data; by using a group size adjustment strategy, the pre-set quantity range corresponding to each classification scenario is adjusted to obtain the target quantity range corresponding to each classification scenario; based on the target quantity range corresponding to each classification scenario and the classification scenario corresponding to each transaction data, the pre-set group number and the number range of institutions corresponding to each verification group are determined.

[0095] The preset inference model can adopt a random forest machine learning model (balancing classification accuracy and real-time performance, and supporting feature importance quantification). The model has been trained using historical transaction data (training features include transaction amount, frequency, and association complexity, and the labels are preset classification scenarios).

[0096] Input the transaction characteristics (quantified values ​​of transaction amount, frequency, and correlation complexity) of each participant into the preset inference model. The model performs real-time classification processing on the transaction data and outputs the corresponding classification scenario for each participant.

[0097] The classification scenarios can include high-frequency small-amount transactions, low-frequency large-amount transactions, high-frequency large-amount transactions, and low-frequency small-amount transactions.

[0098] The baseline quantity range for each category scenario can be preset (e.g., baseline range for scenario 1 [3,8], baseline range for scenario 2 [8,15]). Then, the baseline range can be adjusted by adjusting the group size and combining the feature importance weights (e.g., transaction amount weight 0.4, frequency weight 0.3, association complexity weight 0.3) to obtain the target quantity range for each category scenario.

[0099] For example, scenario 1 involves high-frequency, low-value transactions with a large volume of transaction data and low value per transaction. It is necessary to improve computational efficiency and parallel processing capabilities. The adjustment strategy is to reduce the group size, lower the baseline range to the target number range [3,6], and increase the preset number of groups (e.g., from the default 3 groups to 5-8 groups) to reduce the computational pressure on a single group.

[0100] For example, scenario 2 involves low-frequency, high-value transactions with high single-transaction value and complex relationships. It is necessary to capture the complete flow of funds and enhance security. The strategy is to expand the group size, increase the baseline range to the target range [10,20], and reduce the number of preset groups (e.g., from the default 3 groups to 2-3 groups) to ensure that closely related institutions are covered within the group.

[0101] For example, scenario 3 is a mixed scenario. A balanced adjustment strategy is used to maintain the baseline quantity range [5,12]. Based on the feature importance weight, institutions with similar features (such as transaction amounts in high-frequency, high-value scenarios) are prioritized to be grouped into the same group.

[0102] Based on the communication delay (L) and bandwidth (B) data collected by the network status monitoring module, the cross-regional communication cost coefficient is determined. A higher cross-regional communication cost coefficient indicates a greater cross-regional communication overhead.

[0103]

[0104] in, , For normalized bandwidth, For normalization delay.

[0105] If, in a certain classification scenario, the cross-regional communication cost coefficient of institutions in different geographical areas is greater than a preset threshold (e.g., 0.5), then the target number interval is split, and a dedicated interval is assigned to each independent geographical area (e.g., the first regional interval is [10,15], and the second regional interval is [10,15]), and the preset number of groups is increased (e.g., the original 2 groups are increased to 4 groups) to avoid being in the same group across high-latency areas.

[0106] If the cross-regional communication cost coefficient of institutions within the same geographical area is less than the preset threshold and they belong to the same category scenario, then the target quantity range is merged (e.g., the third region and the fourth region are merged into [12,20]), reducing the number of groups and improving computational efficiency.

[0107] The final determined range of the number of organizations must meet the following constraints: greater than or equal to the minimum number of organizations and less than or equal to the maximum number of organizations. This can prevent insufficient MPC calculation security due to too few organizations in the group and prevent calculation delay due to too many organizations in the group.

[0108] Based on the target number range corresponding to each category scenario and the cross-regional network optimization results, the global preset number of groups (e.g., the final 6 groups) and the range of the number of institutions corresponding to each verification group (e.g., Asia high frequency small amount group [3,6], Europe low frequency large amount group [10,15]) are determined.

[0109] In this application, the system adapts in real time to changes in transaction characteristics (such as abnormal transaction frequency / amount caused by abnormal transactions) and fluctuations in the network environment (such as cross-regional bandwidth reduction) to ensure that the grouping strategy always matches the business and technical environment. At the same time, by dynamically adjusting the grouping strategy, the system avoids the problem of unbalanced allocation of computing resources caused by fixed grouping, thereby enhancing the system's adaptability in complex business scenarios.

[0110] S304. Based on the preset number of verification groups, the range of the number of institutions corresponding to each verification group, and the comprehensive score of each participating object, the multiple participating objects are divided into verification groups to obtain multiple verification groups.

[0111] Based on the classification scenarios corresponding to each participant, participants with the same classification scenario can be identified as a candidate group.

[0112] For each candidate group corresponding to a scenario, verify whether the number of participating entities matches the range of the number of institutions corresponding to that scenario:

[0113] If the number of objects in a candidate group falls within the range of the number of institutions, then it is determined as a validation group;

[0114] If the number of objects in a candidate group is greater than the maximum value of the range of the number of institutions, the participating objects in the candidate group are sorted in descending order according to the comprehensive score of the institution. The participating objects with similar comprehensive scores of institutions are divided into a subgroup, and the number of objects in each subgroup is within the range of the number of institutions. Each subgroup is then determined as a verification group.

[0115] If the number of objects in a candidate group is less than the minimum value of the institutional data range, other candidate groups with similar classification scenarios within the same region should be searched and merged (e.g., merging the low-frequency small-amount group with the low-frequency large-amount group). After merging, it should be ensured that the number of objects in the group conforms to the range of institutional numbers.

[0116] S305. For any verification group, use a homomorphic encryption algorithm to encrypt each transaction data corresponding to the verification group to obtain the encrypted data corresponding to each transaction data.

[0117] The Paillier homomorphic encryption algorithm can be used to encrypt the transaction data of each participant in the verification group, thus obtaining the encrypted data corresponding to each transaction.

[0118] The transaction data may include the amount, counterparty information, timestamp, etc.

[0119] The encryption key (Paillier public key) is collaboratively generated and distributed by the various organizations within the verification group through a secure multi-party key negotiation protocol (e.g., DKG distributed key generation protocol); the private key is split into multiple private key fragments according to a threshold secret sharing mechanism, which are kept separately by each organization. The private key can only be recovered by collaborating with a preset threshold number of organizations (e.g., ≥2 / 3 of the organizations within the group) to avoid the risk of single-point key leakage.

[0120] S306. For any verification group, generate the corresponding interference number.

[0121] The system uses a built-in cryptographically secure distributed random number generator to collaboratively generate a globally consistent set of random interference numbers from all participating institutions within the verification group. These interference numbers are large integers, and their range must cover the magnitude of the total transaction data within the verification group, ensuring that even if the encrypted intermediate results are obtained, the original data sum cannot be deduced in reverse.

[0122] The generation process can employ a multi-party collaborative signature mechanism, preventing any single organization from manipulating the value of the interference. Furthermore, all participating organizations must sign and confirm the generated value to ensure the fairness, security, and global consistency of the interference number, thus preventing the interference number from becoming invalid due to malicious actions by a single organization.

[0123] S307. For any verification group, aggregate the encrypted data and interference numbers corresponding to each transaction data to generate an encrypted intermediate result.

[0124] Based on the additive homomorphic property of Paillier homomorphic encryption, the encrypted data of each institution and the encrypted form of random interference numbers are aggregated in the encrypted state to generate the intermediate encryption result corresponding to the verification group. The specific calculation formula is as follows:

[0125]

[0126] in, This represents addition under homomorphic encryption. To verify the sum of the original transaction data of all institutions within the group, Indicates the total amount of raw data Sum and interference numbers The encrypted result of the sum, For the first transaction data Corresponding encrypted data For the second transaction data Corresponding encrypted data For the i-th transaction data The corresponding encrypted data, Interference number The corresponding encrypted data.

[0127] S308. Transmit the encrypted intermediate results corresponding to each verification group to the trusted execution environment aggregation node.

[0128] It can be transmitted to the trusted execution environment aggregation node built into the processing device through an end-to-end encrypted secure channel (such as TLS1.3 protocol and national cryptographic algorithm SM4).

[0129] During transmission, data integrity is verified (e.g., using SHA-256 hash verification) to prevent data from being tampered with or stolen.

[0130] S309. Through the trusted execution environment aggregation node, perform denoising and global aggregation operations in a hardware-level encrypted environment to obtain the target aggregation data corresponding to each verification group.

[0131] The trusted execution environment aggregation node performs computations within a hardware-isolated secure zone. First, it decrypts the intermediate encrypted results of each verification group (only decrypting the encryption level within the group). It can then access the corresponding Paillier private key fragment for the verification group (obtained compliantly and temporarily combined via the SGX remote proof mechanism, and immediately destroyed after use) to perform the first-level decryption of the intermediate encrypted results, obtaining the group's transaction data and interference count. Complete the aggregation within the group.

[0132] Subsequently, the random noise numbers of each verification group are synchronized, and a second-level global aggregation is performed. Distributed random noise numbers are removed from the aggregation results of each group to obtain the target aggregated data for all participating objects. Then, the target aggregate data of all validation groups are summed to obtain the global target aggregate data corresponding to all participating objects.

[0133] During the operation, the original transaction data and intermediate calculation results only exist briefly in plaintext within the secure area. The confidentiality and integrity of the data are guaranteed by memory encryption and remote attestation mechanisms. At the same time, side-channel attacks are resisted by randomizing the operation sequence and obfuscating the memory access mode.

[0134] It can rely on the Memory Encryption Engine (MEE) mechanism to perform real-time AES-128 encryption on the memory data in the secure area; and verify the legitimacy of the secure area to participating institutions through remote attestation (proving that the secure area has not been tampered with and that compliant code is running), thus ensuring the trustworthiness of the computational logic.

[0135] In the computation process within the secure zone, pseudo-random delay instructions are inserted to cause the execution time of the same operation to fluctuate randomly, eliminating the characteristic of "computation time being related to data value" in timing attacks and preventing attackers from deducing data from the execution time.

[0136] By employing Address Space Layout Randomization (ASLR) and data fragmentation out-of-order storage strategies, intermediate data within the secure area is fragmented and randomly mapped to different memory pages. Simultaneously, the read order of the fragments is randomly adjusted during access, disrupting the correlation between memory access patterns and data content, thus resisting side-channel threats such as cache attacks and page table attacks.

[0137] S310. Generate a cryptographic proof chain based on the target aggregated data corresponding to each verification group.

[0138] In some embodiments, a connection graph corresponding to multiple participating objects is constructed based on the target aggregated data corresponding to each verification group. An intermediate proof chain corresponding to the connection graph is generated based on the zero-knowledge proof corresponding to the encrypted intermediate results and the validity proof corresponding to each edge. Based on the intermediate proof chain, a consistency proof corresponding to multiple participating objects is generated, and the zero-knowledge proof corresponding to each verification group, the validity proof corresponding to each edge, and the intermediate proof chain corresponding to the connection graph are combined to generate a cryptographic proof chain.

[0139] Based on the target aggregated data corresponding to each validation group, core features such as fund flow, counterparty association, and association strength are extracted to construct a structured fund association graph. .

[0140] in, For the hash nodes of the participating objects ( ), To ensure the authenticity of participating entities, the salt value is generated locally by the organization and securely shared to prevent node identity leakage; For transaction-related edges, the edge attributes include encrypted transaction frequency, amount ratio, and association time limit, ensuring that the graph topology is completely consistent with the target aggregated data, with no redundant or missing associations.

[0141] The Groth16 high-efficiency zero-knowledge proof protocol can be used to construct a three-level chain proof based on the data layer, the intra-group computation layer, and the global graph layer. Each level of proof is based on pre-compiled circuit constraints and does not disclose any sensitive data.

[0142] Zero-knowledge proof at the data layer: Each participating institution has already prepared the transaction data when inputting it. Generate commitment value The salt value is generated independently by the organization and shared within the group through a secure channel to ensure the uniqueness and collision resistance of the commitment value; a zero-knowledge proof is generated based on this commitment value. Verify the commitment value Compared with the original transaction data Consistency is ensured, and the verifier does not need to obtain transaction data. This will confirm the validity of the input data.

[0143] Intra-group computational layer validity proof: Generate validity proof for each validation group. The verification of the encrypted intermediate result is the valid aggregation result of the encrypted transaction data within the group and the random noise number. The public input is... and the number of disturbances commitment values The private input is a fragment of the encrypted private key within the group and a signature of the interference number generation process, ensuring that the calculations within the group have not been tampered with.

[0144] Consistency proof at the global graph layer: Based on the target aggregated data and the fund correlation graph of each validation group, a consistency proof is generated. This verifies the consistency between the global aggregation results and the graph topology. Specifically, this includes: matching the sum of the aggregation values ​​of all nodes, the sum of the association strengths of all edges with the proportion of the global transaction amount, and confirming that the graph has no spurious association edges / nodes.

[0145] Zero-knowledge proofs, validity proofs, and consistency proofs are chained together in a hierarchical order, with each proof step containing the hash value of the proof from the previous step, forming a complete cryptographic proof chain.

[0146] In this application, a cryptographic proof chain that can be publicly verified and is tamper-proof is generated by secure transmission, hardware-level aggregation, hierarchical chain-based zero-knowledge proof construction, and on-chain evidence storage, under the premise of ensuring data privacy, so as to realize traceability of the computation process and verifiability of the result.

[0147] In some embodiments, to further enhance the credibility of the proof chain, at least two independent verification nodes are required to cross-verify the proofs of the same verification group and execute the verification logic through a blockchain smart contract to prevent malicious behavior by a single point of verification node.

[0148] Specifically, third-party verification nodes obtain publicly available input data through the blockchain. , , , The system includes a cryptographic proof chain; a verification algorithm based on the Groth16 protocol, which independently performs validity checks on the three-level proof, generates verification results and verification logs; and submits the verification results to the blockchain smart contract. The contract only confirms the validity of the proof chain and records the evidence when the verification results of all verification nodes are consistent (all are passed). If the verification results are inconsistent, the contract triggers an abnormal alarm and simultaneously freezes the subsequent processing of the corresponding transaction data until it is manually reviewed and then executed.

[0149] Public input data includes commitment values Interference number commitment value The sum of aggregated target data for each validation group Image hash value .

[0150] In this application, third-party cross-validation avoids the risk of malicious behavior by a single node, and the standardized verification logic of smart contracts reduces human intervention and enhances the credibility of the proof chain.

[0151] The proof chain generation method provided in this application can obtain network performance data and transaction characteristics of each participant among multiple participants; determine multiple verification groups based on the network performance data and transaction characteristics of each participant; for any verification group, encrypt the transaction data corresponding to each participant in the verification group to generate an encrypted intermediate result corresponding to the verification group; and generate a cryptographic proof chain corresponding to multiple participants based on the encrypted intermediate results corresponding to each verification group. The cryptographic proof chain enables end-to-end verification of transaction data encryption, allowing regulatory agencies to verify the correctness of the analysis process without obtaining the original data, completely solving the black-box computation problem and improving the reliability of data verification.

[0152] Figure 4 This is a schematic diagram of a dynamic grouping verification architecture provided for an embodiment of this application. Please refer to [link / reference]. Figure 4 After each participant inputs their corresponding transaction data, the system enters the dynamic grouping module. This module combines real-time network performance data (communication latency, bandwidth, packet loss rate) provided by network status monitoring to generate a grouping strategy, dividing the participants into multiple verification groups.

[0153] Each verification group performs MPC calculations within the group and simultaneously calls a distributed random number generator to generate globally consistent random interference numbers. The group's transaction data is then encrypted, and encrypted intermediate results are output. These encrypted intermediate results are transmitted to a trusted execution environment (TEE). Leveraging the hardware encryption capabilities provided by TEE isolated computing, denoising and global aggregation operations are performed within the secure zone to obtain the target aggregated data. Based on this target aggregated data, a zero-knowledge proof generation process is initiated, constructing a layered chain of zero-knowledge proofs. Simultaneously, the proof chain is immutably stored using blockchain evidence. Finally, the verification results are output, achieving privacy-preserving collaborative analysis and end-to-end verifiability of transaction data.

[0154] Figure 5 This is a schematic diagram of a data flow architecture provided for an embodiment of this application. Please refer to [link / reference]. Figure 5 The transaction data processing chain from input to final blockchain verification involves the original transaction data of the participating parties being processed by Paillier homomorphic encryption and then mixed with random interference numbers provided by a distributed random number generator. The data is input together, and at the same time, the network status data is transmitted to the dynamic group decision module to generate a group configuration adapted to the current network environment. All three then enter the MPC calculation phase within the group.

[0155] Intra-group MPC computation is based on homomorphic encryption, which aggregates encrypted transaction data with random interference numbers to generate encrypted intermediate results. The result is transmitted to the TEE aggregation node via an end-to-end encrypted secure channel. The TEE aggregation node performs computations within a hardware-isolated secure zone, denoising the encrypted intermediate results to obtain a denoised global result (i.e., the true aggregated value of transaction data from all participating entities). Based on the denoised global result, a relational graph (structured fund flow topology) is generated, followed by the construction of a zero-knowledge proof chain (containing a three-level verification proof: a data layer, an intra-group computation layer, and a global graph layer). Finally, the proof chain is submitted to the blockchain verification contract, achieving immutable evidence storage and public verifiability.

[0156] Figure 6 This is a schematic diagram of a proof chain generation device provided in an embodiment of this application. Please refer to [link / reference]. Figure 6 The proof chain generation device 600 may include an acquisition module 601, a determination module 602, an encryption processing module 603, and a generation module 603.

[0157] The acquisition module 601 is used to acquire the network performance data and transaction characteristics of each participant among multiple participants;

[0158] The determination module 602 is used to determine multiple verification groups based on the network performance data and transaction characteristics of each participant. Each verification group includes at least one participant among the multiple participants.

[0159] The encryption processing module 603 is used to encrypt the transaction data corresponding to each participant in any verification group and generate the encrypted intermediate result corresponding to the verification group.

[0160] The generation module 603 is used to generate cryptographic proof chains for multiple participating objects based on the encrypted intermediate results corresponding to each verification group.

[0161] In some possible embodiments, the determining module 602 is specifically used for:

[0162] Based on the network performance data and transaction characteristics of each participant, the comprehensive institutional score of each participant is determined;

[0163] Obtain the preset number of groups corresponding to multiple verification groups, and the range of the number of institutions corresponding to each verification group;

[0164] Based on the preset number of verification groups, the range of the number of institutions corresponding to each verification group, and the comprehensive institutional score of each participating object, multiple participating objects are divided into verification groups to obtain multiple verification groups.

[0165] In some possible embodiments, network performance data includes multiple sub-performance data; transaction characteristics include multiple sub-characteristics; for any given participant; the determining module 602 is specifically used for:

[0166] Based on multiple sub-performance data and the weights corresponding to each sub-performance data, the network performance indicators corresponding to the network performance data are determined.

[0167] Based on multiple sub-features and the weights corresponding to each sub-feature, the data feature indicators corresponding to the transaction features are determined.

[0168] The sum of network performance indicators and data characteristic indicators is determined as the comprehensive score of the institution.

[0169] The weight corresponding to each sub-performance data and the sum of the weights corresponding to each sub-performance data are 1.

[0170] In some possible embodiments, the determining module 602 is specifically used for:

[0171] By using a pre-set inference model, multiple transaction data are classified based on their transaction characteristics to obtain the corresponding classification scenarios for each transaction data.

[0172] By adjusting the group size strategy, the preset quantity range corresponding to each category scenario is adjusted to obtain the target quantity range corresponding to each category scenario.

[0173] Based on the target quantity range corresponding to each category scenario and the category scenario corresponding to each transaction data, determine the preset number of groups and the range of the number of institutions corresponding to each verification group.

[0174] In some possible embodiments, the encryption processing module 603 is specifically used for:

[0175] Homomorphic encryption algorithm is used to encrypt each transaction data corresponding to the verification group to obtain encrypted data for each transaction data.

[0176] Generate the interference number corresponding to the validation group;

[0177] The encrypted data and interference data corresponding to each transaction are aggregated and calculated to generate an encrypted intermediate result.

[0178] In some possible embodiments, the generation module 603 is specifically used for:

[0179] Transmit the encrypted intermediate results corresponding to each verification group to the trusted execution environment aggregation node;

[0180] By using the trusted execution environment aggregation node, denoising and global aggregation operations are performed in a hardware-level encrypted environment to obtain the target aggregated data corresponding to each verification group;

[0181] Based on the target aggregated data corresponding to each verification group, a cryptographic proof chain is generated.

[0182] In some possible embodiments, the generation module 603 is specifically used for:

[0183] Based on the target aggregated data corresponding to each verification group, a correlation graph corresponding to multiple participating objects is constructed. In the correlation graph, the object identifier corresponding to each participating object is used as a node, and the transaction relationship between each participating object is used as an edge.

[0184] Based on the zero-knowledge proofs corresponding to the encrypted intermediate results and the validity proofs corresponding to each edge, an intermediate proof chain corresponding to the association graph is generated.

[0185] Based on the intermediate proof chain, a consistency proof is generated for multiple participating objects, and a cryptographic proof chain is generated by combining the zero-knowledge proof for each verification group, the validity proof for each edge, and the intermediate proof chain for the association graph.

[0186] In some possible embodiments, the apparatus further includes a cross-validation module, which is used to:

[0187] The proofs of the same verification group are cross-validated by at least two independent verification nodes, and the verification logic is executed through a blockchain smart contract.

[0188] The proof chain generation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0189] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Please refer to... Figure 7 The electronic device 700 may include a processor 701 and a memory 702. Exemplarily, the processor 701 and the memory 702 are interconnected via a bus 703.

[0190] Memory 702 stores instructions executed by the computer;

[0191] The processor 701 executes computer execution instructions stored in the memory 702, causing the processor 701 to execute the proof chain generation method as shown in the above method embodiment.

[0192] Accordingly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the proof chain generation method of the above method embodiments.

[0193] Accordingly, embodiments of this application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the proof chain generation method shown in the above method embodiments.

[0194] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0196] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0197] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0198] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0199] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0200] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0201] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0202] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for generating a proof chain, characterized in that, include: Obtain network performance data and transaction characteristics of each participant among multiple participants; Based on the network performance data of each participant and the transaction characteristics, multiple verification groups are determined, and each verification group includes at least one of the multiple participants. For any verification group, the transaction data corresponding to each participating object in the verification group is encrypted to generate an encrypted intermediate result corresponding to the verification group. Based on the encrypted intermediate results corresponding to each verification group, a cryptographic proof chain corresponding to the multiple participating objects is generated.

2. The method according to claim 1, characterized in that, Based on the network performance data of each participant and the transaction characteristics, multiple verification groups are determined, including: Based on the network performance data and transaction characteristics of each participating entity, a comprehensive institutional score for each participating entity is determined. Obtain the preset number of groups corresponding to the multiple verification groups, and the range of the number of institutions corresponding to each verification group; Based on the preset number of verification groups, the range of the number of institutions corresponding to each verification group, and the comprehensive institutional score of each participating object, the multiple participating objects are divided into verification groups to obtain the multiple verification groups.

3. The method according to claim 2, characterized in that, The network performance data includes multiple sub-performance data; the transaction characteristics include multiple sub-characteristics; for any participating entity; based on the network performance data and transaction characteristics of each participating entity, a comprehensive institutional score for each participating entity is determined, including: Based on the multiple sub-performance data and the weights corresponding to each sub-performance data, the network performance index corresponding to the network performance data is determined. Based on the multiple sub-features and the weights corresponding to each sub-feature, the data feature indicators corresponding to the transaction feature are determined; The sum of the network performance indicators and the data characteristic indicators is determined as the comprehensive score of the institution. Wherein, the sum of the weights corresponding to each sub-performance data and the weights corresponding to each sub-performance data is 1.

4. The method according to claim 2, characterized in that, Obtaining the preset number of groups corresponding to the multiple verification groups, and the range of the number of institutions corresponding to each verification group, including: By using a pre-set inference model, multiple transaction data are classified based on their transaction characteristics to obtain the corresponding classification scenarios for each transaction data. By adjusting the group size strategy, the preset quantity range corresponding to each category scenario is adjusted to obtain the target quantity range corresponding to each category scenario. Based on the target quantity range corresponding to each category scenario and the category scenario corresponding to each transaction data, the preset group quantity and the institution quantity range corresponding to each verification group are determined.

5. The method according to claim 1, characterized in that, The transaction data corresponding to each participant in the verification group is encrypted to generate an encrypted intermediate result corresponding to the verification group, including: A homomorphic encryption algorithm is used to encrypt each transaction data corresponding to the verification group to obtain encrypted data corresponding to each transaction data. Generate the interference number corresponding to the verification group; The encrypted data corresponding to each transaction data and the interference number are aggregated and calculated to generate the encrypted intermediate result.

6. The method according to claim 1, characterized in that, Based on the intermediate encryption results corresponding to each verification group, a cryptographic proof chain corresponding to the multiple participating objects is generated, including: The encrypted intermediate results corresponding to each verification group are transmitted to the trusted execution environment aggregation node; Through the trusted execution environment aggregation node, denoising and global aggregation operations are performed in a hardware-level encrypted environment to obtain the target aggregated data corresponding to each verification group; The cryptographic proof chain is generated based on the target aggregated data corresponding to each verification group.

7. The method according to claim 6, characterized in that, Based on the target aggregated data corresponding to each verification group, the cryptographic proof chain is generated, including: Based on the target aggregated data corresponding to each verification group, a correlation graph corresponding to the multiple participating objects is constructed. In the correlation graph, the object identifiers corresponding to each participating object are used as nodes, and the transaction correlation relationships between each participating object are used as edges. Based on the zero-knowledge proof corresponding to the encrypted intermediate result and the validity proof corresponding to each edge, an intermediate proof chain corresponding to the association graph is generated. Based on the intermediate proof chain, a consistency proof corresponding to the multiple participating objects is generated, and the zero-knowledge proof corresponding to each verification group, the validity proof corresponding to each edge, and the intermediate proof chain corresponding to the association graph are used to generate the cryptographic proof chain.

8. The method according to claim 1, characterized in that, After generating the cryptographic proof chain corresponding to the multiple participating objects based on the encrypted intermediate results corresponding to each verification group, the process further includes: The proofs of the same verification group are cross-validated by at least two independent verification nodes, and the verification logic is executed through a blockchain smart contract.

9. A proof chain generation device, characterized in that, It includes an acquisition module, a determination module, an encryption processing module, and a generation module: The acquisition module is used to acquire network performance data and transaction characteristics of each participant among multiple participants; The determining module is used to determine multiple verification groups based on the network performance data of each participating object and the transaction characteristics, wherein the verification group includes at least one participating object among the multiple participating objects; The encryption processing module is used to encrypt the transaction data corresponding to each participating object in any verification group, and generate the encrypted intermediate result corresponding to the verification group. The generation module is used to generate a cryptographic proof chain corresponding to the multiple participating objects based on the encrypted intermediate results corresponding to each verification group.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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