Credit assessment

Abstract

Embodiments of the present disclosure relate to the technical field of computers. Disclosed are a credit assessment method and apparatus, a storage medium, a program product, and an electronic device. A credit network consists of a plurality of nodes comprising user data. A credit assessment request for performing credit assessment on a target user to provide credit support for the target user is made, and a first node in the credit network performs, on the basis of the credit assessment request, first privacy computing on user data corresponding to the target user, and sends a first computing result to at least one second node in the credit network to perform second privacy computing and obtain a second computing result, thereby avoiding leakage of user data of each node to other nodes; further, credit assessment is performed on the target user on the basis of the second computing result, to provide credit support, such as a resource amount, for the target user.

Description

Credit assessment TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to credit assessment. BACKGROUND

[0002] Credit assessment is a comprehensive evaluation of credit status, repayment ability, etc. of a user (an individual or an enterprise) by a financial institution (such as a bank) to determine whether to provide credit support to the user. After initial credit assessment of the user, the credit support to the user is not fixed, but is continuously assessed and adjusted according to the subsequent behavior of the user. The information usually used for credit assessment is: 1) the behavior information of the user recorded by the financial institution, that is, the credit behaviors of the user on the financial platform, such as use, repayment, overdue, etc.; and 2) the behavior information of the user recorded by other credit institutions, which is obtained by cooperation with other credit institutions, for example, the credit assessment report of the user by other credit institutions.

[0003] However, the above method of obtaining user behavior to assess the credit of the user has the following disadvantages: (1) the behavior information of the financial institution accumulates relatively slowly, and the data source is limited and cannot comprehensively evaluate the behavior of the user, resulting in inaccurate subsequent credit assessment results; (2) for the information of other credit institutions, some credit institutions will only report the behavior information of the user in the case of adverse behaviors such as overdue, and the above reporting mechanism cannot cover the case where the user has good use behavior in the financial institution but has a large amount of debt in other platforms. SUMMARY

[0004] Embodiments of the present disclosure provide a credit assessment method, device, storage medium, program product and electronic equipment, which can solve the above problems. The technical solution is as follows.

[0005] In a first aspect, the embodiments of the present disclosure provide a credit assessment method, which comprises: a first node obtains a credit assessment request for a target user, the first node being any node in a plurality of nodes included in a credit network; in response to the credit assessment request, performing first privacy calculation on user data corresponding to the target user included in the first node to obtain a first calculation result; sending the first calculation result to at least one second node in the credit network to obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node; and performing credit assessment on the target user according to the second calculation result.

[0006] In a second aspect, the embodiments of the present disclosure provide a credit assessment device, which comprises: a credit request module, configured to acquire a credit assessment request for a target user by a first node, the first node being any one of a plurality of nodes included in a credit network; a first calculation module, configured to perform first privacy calculation on user data corresponding to the target user included in the first node in response to the credit assessment request, to obtain a first calculation result; a second calculation module, configured to send the first calculation result to at least one second node in the credit network, and acquire a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node; and a credit assessment module, configured to perform credit assessment on the target user according to the second calculation result.

[0007] In a third aspect, the embodiments of the present disclosure provide a computer storage medium, which stores a plurality of instructions, the instructions being suitable for being loaded and executed by a processor to perform the method steps described above.

[0008] In a fourth aspect, the embodiments of the present disclosure provide a computer program product, which stores a plurality of instructions, the instructions being suitable for being loaded and executed by a processor to perform the method steps described above.

[0009] In a fifth aspect, the embodiments of the present disclosure provide an electronic device, which can comprise: a processor and a memory; wherein the memory stores a computer program, the computer program being suitable for being loaded and executed by the processor to perform the method steps described above.

[0010] The technical solutions provided by some embodiments of the present disclosure have at least the following beneficial effects: in the present disclosure, a credit network is composed of a plurality of nodes including user data, a credit assessment request for credit assessment on a target user is performed to provide credit support for the target user, a first node in the credit network performs first privacy calculation on user data corresponding to the target user according to the credit assessment request, and sends a first calculation result to at least one second node in the credit network to perform second privacy calculation and obtain a second calculation result, so as to ensure that each node only receives a privacy calculation result and not the original user data, avoid user data leakage from each node to other nodes, and improve the security of using user data when performing credit assessment on the user; further, credit assessment is performed on the target user according to the second calculation result, in other words, each node in the credit network shares user data of other nodes, and each node cooperates with each other to improve the utilization rate of user data of each node, thereby expanding the range of decision data for credit assessment on the user, and effectively improving the accuracy of credit decision, that is, the resource quantity of the node can be prevented from being damaged, and credit support can be better provided for the target user. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or related art description. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0012] FIG. 1 is an architecture schematic diagram of a credit evaluation method provided by an embodiment of the present disclosure;

[0013] FIG. 2 is a flow schematic diagram of a credit evaluation method provided by an embodiment of the present disclosure;

[0014] FIG. 3 is a flow schematic diagram of a second privacy calculation provided by an embodiment of the present disclosure;

[0015] FIG. 4 is a flow schematic diagram of a second privacy calculation provided by an embodiment of the present disclosure;

[0016] FIG. 5 is a flow schematic diagram of a credit evaluation method provided by an embodiment of the present disclosure;

[0017] FIG. 6 is a structural schematic diagram of a credit network provided by an embodiment of the present disclosure;

[0018] FIG. 7 is a flow schematic diagram of a credit evaluation method provided by an embodiment of the present disclosure;

[0019] FIG. 8 is a structural schematic diagram of a credit evaluation device provided by an embodiment of the present disclosure;

[0020] FIG. 9 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0022] In the description of the present disclosure, it is understood that the terms "first", "second" and the like are only for the purpose of description and cannot be understood as indicating or implying relative importance. In the description of the present disclosure, it is pointed out that, unless otherwise explicitly specified and limited, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units not listed, or optionally also includes other steps or units inherent to the process, method, product or device. The specific meaning of the above terms in the present disclosure can be understood by the person skilled in the art according to the specific circumstances. In addition, in the description of the present disclosure, "multiple" means two or more, unless otherwise specified. The association relationship of the associated objects is described, which means that there can be three relationships, for example, A and / or B can represent three cases: A exists alone, A and B exist, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0023] The present disclosure will be described in detail below in conjunction with specific embodiments.

[0024] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, the information involved in the present disclosure is obtained under sufficient authorization.

[0025] Credit assessment is a comprehensive evaluation of the credit status and repayment ability of users (individuals or enterprises) by financial institutions (such as banks) to determine whether to provide credit support to users. After the initial credit assessment of the user, the credit support to the user is not fixed, but is continuously assessed and adjusted according to the subsequent behavior of the user. The information commonly used for credit assessment is first the user behavior information recorded by the financial institution, that is, the credit behavior of the user on the financial platform, such as use, repayment, overdue, etc., and second, the user behavior information recorded by other credit institutions obtained through cooperation with other credit institutions, such as obtaining the credit assessment report of the user by other credit institutions.

[0026] However, the above method of obtaining user behavior to assess the creditworthiness of the user has the following disadvantages: (1) the behavior information of the financial institution accumulates relatively slowly, and the data sources are limited, which cannot comprehensively evaluate the user's behavior, resulting in inaccurate subsequent credit assessment results; (2) for information of other credit institutions, some credit structures only report user behavior information in the case of user overdue and other bad behaviors. The above reporting mechanism cannot cover the case where the user behaves well in the financial structure but has a large amount of debt in other platforms.

[0027] To solve the above problems, the present embodiment provides a credit assessment method. As shown in FIG. 1, FIG. 1 is an architecture schematic diagram of a credit assessment method provided by the present embodiment. FIG. 1 includes a credit network composed of multiple nodes, which at least includes node 1011, node 1012, node 1013, node 1014, node 1015 and node 1016. It can be understood that the number of nodes shown in FIG. 1 is only illustrative, and the present disclosure does not make any limitation thereon.

[0028] Each node in the credit network can be understood as a server or a cluster composed of multiple servers. The server corresponding to each node is used to receive requests or information through a plurality of interfaces set, and provide corresponding data or services based on the request content of the request. In the present embodiment, any node in the credit network stores user data corresponding to a plurality of users, which can include behavior data of the user in a certain time period or identity information of the user, etc. Any node in the credit network is used to perform credit assessment on the target user based on the credit assessment request for the target user. Taking the node corresponding to the institution as a fund-related institution as an example, it can specifically include a sales system (for example, a sales outlet, a personal user and other third-party sales system), a fund system and a share registration system (for example, an open-end fund registration and transfer system), etc. The user data includes historical repayment behavior of the user in the sales system corresponding to the node, loan balance, credit account number, credit history length, new credit account number, etc.

[0029] It can be understood that each node also has other service capabilities and functions to complete the tasks in the following embodiments. For example, node 1011 also provides portal services, resource management services, and CI / CD services, etc.

[0030] The servers corresponding to the above multiple nodes can be multiple physical servers, which are independent in hardware; or multiple servers are multiple virtual servers, which are deployed in the same hardware resource pool. The deployment mode of the virtual server includes but is not limited to VMware, Virtual Box and Virtual PC.

[0031] The plurality of nodes included in the credit network can communicate through a communication link established between the plurality of nodes by a communication protocol, for example: gRPC protocol, gRPC is a high-performance, general-purpose open source remote server call (Remote Procedure Call, RPC) framework, which is mainly designed for mobile application development and based on HTTP / 2 protocol standard, developed based on protocol buffer (Protocol Buffers, PB) serialization protocol, and supports many development languages. In addition, the communication link can also be a wireless communication link or a wired communication link, for example: the wired communication link includes optical fiber, twisted pair or coaxial cable, the wireless communication link includes Bluetooth communication link, wireless fidelity (WIreless-FIdelity, Wi-Fi) communication link or microwave communication link, etc.

[0032] Each node can receive a credit evaluation request input by a user through an electronic device. The electronic device includes but is not limited to a physical or virtual server, a mobile station (Mobile Station, MS), a mobile terminal, a mobile telephone, a handset, a portable equipment, a Bluetooth headset, a smart watch, etc. The electronic device can communicate with one or more core networks through a radio access network (Radio Access Network, RAN). In the embodiment of the present disclosure, a display device can also be installed on the electronic device. The display device can be various devices that can realize display functions, for example: the display device can be a cathode ray tube display (Cathode ray tube display, CR), a light-emitting diode display (Light-emitting diode display, LED), an electronic ink screen, a liquid crystal display (Liquid crystal display, LCD), a plasma display panel (Plasma display panel, PDP), etc. For example, the user can use the display device on the electronic device to view the credit evaluation result.

[0033] In one embodiment, as shown in FIG. 2, a flowchart of a credit evaluation method proposed in the embodiment of the present disclosure is shown. The method can be implemented by relying on a computer program and can run on a credit evaluation device based on the von Neumann system. The computer program can be integrated in an application or run as an independent tool application.

[0034] Specifically, the credit evaluation method includes the following steps.

[0035] S102, the first node obtains a credit evaluation request for a target user.

[0036] The target user can be understood as a specific object of the credit assessment, and can be an individual or an enterprise. The credit assessment request for the target user can be in any protocol-based language, such as Structured Query Language (SQL), and the content of the credit assessment request is a series of instructions for the first node to evaluate the credit status of the target user before providing credit support to the target user.

[0037] The first node can be any node in the credit network shown in FIG. 1 that receives the credit assessment request. For example, the first node is node 1011 or node 1012 or node 1013 or node 1014 or node 1015 or node 1016. The method by which the first node receives the credit assessment request for the target user can be receiving a credit assessment request sent by an electronic device carrying a user identifier corresponding to the target user, or receiving a credit assessment request for the target user initiated at a certain time to adjust the content of the credit support provided to the target user before that time.

[0038] S104, in response to the credit assessment request, performing first privacy calculation on user data corresponding to the target user included in the first node to obtain a first calculation result.

[0039] Taking the institution corresponding to the node as a fund-related institution as an example, the first node can include a sales system (for example, a sales outlet, a personal user, and other third-party sales systems), a fund system, and a share registration system (for example, an open-end fund registration and transfer system), and the like. The user data corresponding to the target user included in the first node at least includes historical repayment behavior, loan balance, credit account number, credit history length, and new credit account number of the target user in the sales system corresponding to the node.

[0040] In this embodiment, the first node performs first privacy calculation on the user data corresponding to the target user to obtain a first calculation result, and the first privacy calculation is one or more of a plurality of preset privacy calculations. Privacy calculation is a type of information technology that realizes data analysis and calculation under the premise of protecting data itself from being leaked, and includes the cross-fusion of many technical systems such as data science, cryptography, and artificial intelligence.

[0041] The first privacy calculation can be an elliptic curve cryptosystem (ECC), which is a public key encryption algorithm based on elliptic curve mathematics, and its essence is to use the discrete logarithm problem to achieve encryption, mainly used for encrypting data, decrypting data and exchanging keys. The first privacy calculation can also be a zero-knowledge proof (ZKP), the principle of which is that the prover can make the verifier believe that a certain proposition is correct without providing any useful information to the verifier. Zero-knowledge proof is essentially a protocol involving two or more parties, i.e. a series of steps that two or more parties need to take to complete a task. The prover proves to the verifier and makes him believe that he knows or has a certain message, but the proof process cannot leak any information about the message being proved to the verifier. The first privacy algorithm can also be a combination of the above privacy algorithms, and the present embodiment does not make any limitation thereto.

[0042] In an embodiment, in order to improve the security of privacy calculation of user data by each node, a secure calculation system based on a trusted execution environment can be deployed in each node. The trusted execution environment (TEE) can be a secure running environment for data processing, and the secure calculation system of the trusted execution environment can be implemented by a program written in a predetermined programming language (which can be in the form of software) or by a specified entity component and a written program (which can be in the form of hardware + software). The present embodiment does not limit which form the trusted execution environment is implemented by, and the specific form of implementation can be set according to actual conditions.

[0043] In each node, the trusted execution environment can be a secure and isolated data processing environment from other environments in the node, that is, the privacy calculation performed in the trusted execution environment and the data generated in the process of privacy calculation cannot be accessed by other execution environments or application programs outside the executable environment. The trusted execution environment can be implemented by creating a small operating system that can run independently in a trusted area (such as TrustZone, etc.), and the trusted execution environment can provide services directly in the form of system calls (such as directly handled by the TrustZone kernel).

[0044] For example, the node can include a REE (rich execution environment) and a TEE (trusted execution environment), and the REE can run an operating system installed on the terminal device, such as an Android operating system, an iOS operating system, a Windows operating system, a Linux operating system, and the like. The REE can provide all functions of the terminal device to an upper application program, such as a camera function and a touch function. The operating system can obtain all data of an application program, but it is difficult to verify whether the operating system or the application program is tampered with, and if tampered with, there is a great security risk to user data. To this end, a secure computing system based on a trusted execution environment is deployed in the node, and the secure computing system of each node is used to calculate the user data for privacy calculation. The trusted execution environment has its own execution space, that is, there is also an operating system in the trusted execution environment. The trusted execution environment has a higher security level than the REE, and the software and hardware resources in the terminal device that can be accessed by the trusted execution environment are separated from the REE. However, the trusted execution environment can directly obtain the information of the REE, and the REE cannot obtain the information of the trusted execution environment.

[0045] S106, send the first calculation result to at least one second node in the trusted network, and obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node.

[0046] The second node is any possible node in the trusted network except the first node. The first node sends the first calculation result obtained by performing the first privacy calculation to at least one second node in the trusted network, and the first calculation result is used to instruct the at least one second node to perform the second privacy calculation on the first calculation result, so that the first node obtains the second calculation result obtained by performing the second privacy calculation on the first calculation result by the at least one second node.

[0047] The second privacy calculation includes a process of performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node respectively, and the calculation result corresponding to each second node in the first calculation result can be the same or different, and the algorithm for performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node respectively can be the same or different.

[0048] The first node determines at least one second node from the plurality of nodes included in the credit network according to a preset sending rule. The preset sending rule can be related to the content of the credit evaluation request of the target user. For example, the credit evaluation request of the target user specifies that the institution providing credit support to the target user is the corresponding joint institution of the node 1011, the node 1012 and the node 1013, and the first node is the node 1011, so the second node is the node 1012 and the node 1013. For another example, the preset sending rule can be related to the first node receiving the credit evaluation request. For example, in the credit evaluation network, the node 1011, the node 1012 and the node 1013 can cooperate to evaluate the credit of the target user, the node 1011 and the node 1014 cannot cooperate to evaluate the credit of the target user, and therefore when the first node is the node 1011, the second node is the node 1012 and the node 1013.

[0049] In one embodiment, at least one second node is determined from the plurality of nodes included in the credit network according to the range of user data required by the credit evaluation request. Since each node stores user data, the plurality of users stored by each node is not completely the same as the plurality of users stored by other nodes, and the type of user data stored by each node is not completely the same as the type of user data stored by other nodes. Therefore, at least one second node is determined from the plurality of nodes included in the credit network according to the range of user data required by the credit evaluation request.

[0050] The range of user data can be a range corresponding to the type or date of user data of the target user required for evaluating the target user. For example, the credit evaluation request requires four types of user data of the target user, including historical repayment behavior, loan balance, credit account number and credit history length, and the second node storing the above types of user data of the target user is determined in the credit network. The type of user data can also be user identity information. For example, the credit evaluation request needs to evaluate whether multiple nodes have the same blacklisted user, and the node storing the identity information of the same blacklisted user is the second node.

[0051] In this embodiment, at least one second node is determined from the plurality of nodes included in the credit network according to the range of user data required by the credit evaluation request, so as to avoid sending the first calculation result to the node in the credit network which does not need to perform the second privacy calculation, avoid wasting the computer logic resources of the credit network, and improve the efficiency and accuracy of outputting the credit evaluation result in response to the credit evaluation request.

[0052] In one embodiment, a calculation order of performing second privacy calculation on the first calculation result by at least one second node in the trusted network is determined according to a calculation manner of user data required by the trusted evaluation request; and the first calculation result and a calculation request are sent to a target node in the at least one second node according to the calculation order, and a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request is acquired.

[0053] The second privacy calculation includes a process of performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node, and the calculation order of the second privacy calculation can be understood as the calculation order of performing privacy calculation by the second nodes. For example, the second nodes are node 1012, node 1013 and node 1014, and the calculation order is that node 1012, node 1013 and node 1014 perform privacy calculation according to the calculation result of the previous node in turn.

[0054] Further, the first calculation result and the calculation request are sent to the target node in the at least one second node according to the calculation order, and the second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request is acquired. The calculation request can be understood as a request for instructing each second node to perform privacy calculation on the calculation result corresponding to the second node in the first calculation result. The calculation result corresponding to each second node in the first calculation result can be the same or different, and the algorithm for performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node can be the same or different.

[0055] Based on the above embodiment, as shown in FIG. 3, FIG. 3 is a flowchart of second privacy calculation provided by an embodiment of the present disclosure. According to the calculation order, a second node in sequence first in the plurality of second nodes is determined as a target node; the first calculation result and the calculation request are sent to the target node, so that the at least one second node performs privacy calculation on the calculation result of the previous second node in turn according to the calculation order in response to the calculation request, until the second calculation result sent by the second node in sequence last is acquired.

[0056] As shown in FIG. 3, the first node is the node 2011 receiving the credit evaluation request, the second nodes include the node 2012, the node 2013 and the node 2014, and the determined calculation order is the node 2012, the node 2013 and the node 2014 in sequence. According to the calculation order, the node 2012, which is the first in the sequence among the plurality of second nodes, is determined as the target node. The first calculation result 2021 is further sent to the target node, i.e., the node 2012, so that the node 2012 performs privacy calculation on the first calculation result 2021 to obtain a calculation result 2022. The node 2012 sends the calculation result 2022 to the node 2013, so that the node 2013 performs privacy calculation on the calculation result 2022 to obtain a calculation result 2023. The node 2013 sends the calculation result 2023 to the node 2014, so that the node 2014 performs privacy calculation on the calculation result 2023 to obtain a second calculation result 2024. The first node, i.e., the node 2011, performs credit evaluation according to the second calculation result 2024. For example, a credit evaluation result 203 can be obtained.

[0057] As shown in FIG. 4, which is a flowchart of a second privacy calculation provided by an embodiment of the present disclosure, according to the calculation order, each second node is determined as a target node; the first calculation result and a calculation request are respectively sent to at least one second node, and the calculation result obtained by each second node in response to the calculation request on the first calculation result is acquired; and the second calculation result is obtained according to at least one calculation result. In other words, each node respectively performs privacy calculation on the first calculation result, the second calculation result includes the calculation result corresponding to each node, and the algorithm type for the privacy calculation of each node can be the same or different.

[0058] As shown in FIG. 4, the first node, i.e., the node 3011, performs first privacy calculation on the user data according to the credit evaluation request to obtain a first calculation result 3021. The node 3012, the node 3013 and the node 3014, which are second nodes, are determined as target nodes, and the first calculation result 3021 is sent to the node 3012, the node 3013 and the node 3014 respectively, so that the node 3012, the node 3013 and the node 3014 respectively perform privacy calculation on the first calculation result 3021 to obtain a calculation result 3022, a calculation result 3023 and a calculation result 3024, and the second calculation result 3024 includes the calculation result 3022, the calculation result 3023 and the calculation result 3024. The first node, i.e., the node 3011, performs credit evaluation according to the second calculation result 3024. For example, a credit evaluation result 303 can be obtained.

[0059] It can be understood that the calculation order corresponding to the plurality of second nodes included in the second privacy calculation can also be a combination of the calculation manners shown in FIG. 3 and FIG. 4, or other possible calculation orders.

[0060] S108, performing credit assessment on the target user according to the second calculation result.

[0061] The first node performs credit assessment on the target user according to the second calculation result obtained by the at least one second node performing second privacy calculation on the first calculation result. In other words, the second calculation result represents decision information that comprehensively reflects the user data of the target user included in the first node and the at least one second node and reflects the credit of the user. Through the second calculation result, the first node determines whether to provide credit support to the target user and the amount of resources for credit support.

[0062] For example, in response to a credit assessment request for a target user, the first node performs encryption calculation on the blacklist data included in the first node as the first privacy calculation to obtain the first calculation result. Further, the first node sends the first calculation result and the calculation request to a plurality of second nodes in the credit network. After each second node receives the first calculation request and the calculation request, the second node performs encryption calculation on the blacklist data included in the second node, and obtains the intersection result as the calculation result of the second node by intersecting the encryption result with the first calculation result. The second calculation result includes the calculation results output by the plurality of second nodes. The first node receives the second calculation result, and determines whether the target user is a common blacklist user of at least two nodes according to the second calculation result, that is, whether the identity information of the target user is in the blacklist data corresponding to at least two nodes respectively. If it is determined that the target user is a common blacklist user of at least two nodes, the target user is blacklisted, and credit support to the target user is refused.

[0063] In the present disclosure, the credit network is composed of a plurality of nodes including user data, a credit assessment request for a target user is performed to provide credit support to the target user, the first node in the credit network performs first privacy calculation on the user data corresponding to the target user according to the credit assessment request, and sends the first calculation result to at least one second node in the credit network for second privacy calculation to obtain a second calculation result, thereby ensuring that each node only receives a privacy calculation result and not the original user data, avoiding leakage of user data of each node to other nodes, and improving the security of using user data when performing credit assessment on the user; further, the target user is subjected to credit assessment according to the second calculation result, in other words, the nodes in the credit network share the user data of other nodes, and the nodes cooperate with each other to improve the utilization rate of the user data of each node, thereby expanding the range of decision data for credit assessment on the user and effectively improving the accuracy of credit decision, that is, the resource amount of the node can be prevented from being damaged, and credit support can be better provided to the target user.

[0064] In an embodiment, as shown in FIG. 5, a flowchart of a credit evaluation method proposed by an embodiment of the present disclosure is shown. The method can be implemented by a computer program and run on a credit evaluation device based on the von Neumann architecture. The computer program can be integrated in an application or run as a standalone tool application.

[0065] Specifically, the credit evaluation method includes the following steps.

[0066] S202, at least two nodes in a preset blockchain are grouped into a credit network through an alliance protocol.

[0067] The blockchain is a special distributed database technology suitable for storing simple, chronological, and verifiable data within the system. The data is ensured to be tamper-proof and unforgeable by cryptography and consensus algorithm. In the embodiment, the preset blockchain can be understood as including a plurality of nodes that can execute the credit evaluation method and / or provide credit support to users. The plurality of nodes form a peer-to-peer (P2P) network, and the P2P protocol is an application layer protocol running on the transmission control protocol (TCP) protocol.

[0068] In an embodiment, the plurality of nodes included in the blockchain correspond to the credit institution information of the respective nodes and are stored in the blockchain. The credit institution is taken as an example of a fund-related institution, which can specifically include a sales system (e.g., a sales outlet, a personal user, and other third-party sales systems), a fund system, and a share registration system (e.g., an open-end fund registration and transfer system), etc. The credit institution information can be understood as information representing the identity of the credit institution, such as basic information of the institution, production information of the institution, operating information of the institution, bank account information, etc.

[0069] In the embodiment, the institution can store the credit institution information in the blockchain through a smart contract. The smart contract is a contract program that automatically executes according to specific conditions, and is an important way for the institution to interact with the blockchain and implement transaction logic using the blockchain. For example, each institution can access the preset blockchain through the smart contract deployed on the blockchain in the server to use the data storage and data query functions provided by the preset blockchain, execute the credit evaluation task, and send the calculation results to other nodes.

[0070] In the preset blockchain, a number of specific nodes are selected to form an alliance chain through an alliance protocol. The alliance chain refers to a blockchain whose consensus process is controlled by preselected nodes. Only nodes belonging to the alliance have the right to access, and when the nodes forming the alliance chain change data, the change data is broadcast to the entire alliance chain.

[0071] As shown in FIG. 6, FIG. 6 is a structural diagram of a credit network provided by an embodiment of the present disclosure. The credit network includes at least nodes 4011, 4012, 4013, 4014, 4015 and 4016, and alliance agreements are signed between the nodes. The node 4011 stores credit agency information of a credit agency 4021, the node 4012 stores credit agency information corresponding to an agency 4022, the node 4013 stores credit agency information of a credit agency 4023, the node 4014 stores credit agency information corresponding to an agency 4024, the node 4015 stores credit agency information of a credit agency 4025, and the node 4016 stores credit agency information corresponding to an agency 4026.

[0072] Credit behaviors of a user in each credit agency, such as borrowing and repayment, are recorded by a server of the credit agency into user data, and stored into a server of a node corresponding to the credit agency in the credit network.

[0073] S204, the first node obtains a credit evaluation request for a target user.

[0074] Refer to S102 above, which will not be repeated here.

[0075] S206, in response to the credit evaluation request, performing first privacy calculation on user data corresponding to the target user included in the first node to obtain a first calculation result.

[0076] Refer to S104 above, which will not be repeated here.

[0077] S208, sending the first calculation result to at least one second node in the credit network to obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node.

[0078] Refer to S106 above, which will not be repeated here.

[0079] S210, storing the first calculation result corresponding to the first node and the calculation results corresponding to the at least one second node respectively into the credit network.

[0080] The second calculation result includes the calculation results corresponding to the at least one second node respectively. Storing the first calculation result and the second calculation result into the credit network ensures that the first calculation result and the second calculation result are tamper-proof and unforgeable by the credit network through cryptography and consensus algorithm. In addition, each node only stores the calculation results for use, and the user data stored in each node is not out of the domain, thereby protecting the privacy and security of the user data in each node.

[0081] S212, performing credit assessment on the target user according to the second calculation result.

[0082] See S108 above, which will not be repeated here.

[0083] In the present disclosure, the credit network is composed of multiple nodes including user data, a credit assessment request for credit assessment on a target user is performed to provide credit support for the target user, a first node in the credit network performs first privacy calculation on user data corresponding to the target user according to the credit assessment request, and sends the first calculation result to at least one second node in the credit network for second privacy calculation and obtains a second calculation result, so as to ensure that each node only receives the privacy calculation result and not the original user data, avoid user data leakage from each node to other nodes, and improve the security of using user data when performing credit assessment on the user; further, the target user is assessed according to the second calculation result, in other words, each node in the credit network shares user data of other nodes, and each node cooperates with each other to improve the utilization rate of user data of each node, thereby expanding the range of decision data for credit assessment on the user, effectively improving the accuracy of credit decision, that is, avoiding damage to the amount of resources of the node, and better providing credit support for the target user.

[0084] In one embodiment, as shown in FIG. 7, a flowchart of a credit assessment method according to an embodiment of the present disclosure is provided, which can be implemented by a computer program and run on a credit assessment device based on the von Neumann architecture. The computer program can be integrated in an application or run as an independent tool application.

[0085] Specifically, the credit assessment method includes the following steps.

[0086] S302, at least two nodes in a preset blockchain are grouped into a credit network through an alliance protocol.

[0087] See S302 above, which will not be repeated here.

[0088] S304, a first node obtains a credit assessment request for a target user.

[0089] See S102 above, which will not be repeated here.

[0090] S306, in response to the credit assessment request, performing first privacy calculation on user data corresponding to the target user included in the first node to obtain a first calculation result.

[0091] See S104 above, which will not be repeated here.

[0092] S308, determining at least one second node in the plurality of nodes included in the credit network according to a range of user data required by the credit evaluation request.

[0093] Since each node stores user data, the user data stored by each node is not completely identical to the user data stored by other nodes, and the type of user data stored by each node is not completely identical to the type of user data stored by other nodes. Therefore, at least one second node in the plurality of nodes included in the credit network is determined according to the range of user data required by the credit evaluation request.

[0094] The range of user data can be a range corresponding to the type or date of user data of the target user required for evaluating the target user. For example, the credit evaluation request requires four types of user data of the target user, including historical repayment behavior, loan balance, credit account number, and credit history length. A second node storing the target user data of the above-mentioned types in the credit network is determined. The type of user data can also be user identity information. For example, the credit evaluation request needs to evaluate whether multiple nodes have the same blacklisted user. The node storing the identity information of the same blacklisted user is the second node.

[0095] S310, determining a calculation order of performing second privacy calculation on the first calculation result by at least one second node in the credit network according to a calculation method of user data required by the credit evaluation request.

[0096] The second privacy calculation includes the process of performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node. The calculation order of the second privacy calculation can be understood as the calculation order of performing privacy calculation by the second node. As shown in FIG. 3 and FIG. 4, details are not repeated here.

[0097] S312, obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by at least one second node in response to a calculation request according to the calculation order, and sending the first calculation result and the calculation request to a target node in the at least one second node.

[0098] The calculation request can be understood as a request for instructing each second node to perform privacy calculation on the calculation result corresponding to the second node in the first calculation result. The calculation result corresponding to each second node in the first calculation result can be the same or different, and the algorithm for performing privacy calculation on the calculation result corresponding to each second node in the first calculation result by each second node can be the same or different. As shown in FIG. 3 and FIG. 4, details are not repeated here.

[0099] S314, performing credit evaluation on the target user according to the second calculation result.

[0100] See S108 above, details are not repeated here.

[0101] S316, providing the target user with a resource amount corresponding to the credit assessment result according to the credit assessment result of the target user.

[0102] The first node provides the target user with a resource amount corresponding to the credit assessment result according to the credit assessment result of the target user after determining to provide credit support to the target user. For example, the resource amount is a credit limit. The credit limit refers to a short-term credit transaction inventory management index approved by a commercial bank for a user. It can be divided into single loan credit limit, borrowing enterprise limit, and group borrowing enterprise limit. As long as the credit balance does not exceed the corresponding transaction variety index, no matter how many cumulative amounts and times of issuing are, the commercial bank transaction department can quickly provide short-term credit to the customer, that is, the short-term credit funds of the commercial bank can be conveniently recycled, thereby meeting the user's requirements for the speed and convenience of financial services.

[0103] In the present disclosure, the credit network is composed of multiple nodes including user data, a credit assessment request for credit assessment of a target user to provide credit support to the target user, the first node in the credit network performs first privacy calculation on the user data corresponding to the target user according to the credit assessment request, and sends the first calculation result to at least one second node in the credit network for second privacy calculation and obtains a second calculation result, so as to ensure that each node only receives a privacy calculation result and not the original user data, avoid user data leakage from each node to other nodes, and improve the security of using user data when performing credit assessment on the user; further, the target user is credit assessed according to the second calculation result, in other words, each node in the credit network shares the user data of other nodes, and each node cooperates with each other to improve the utilization rate of the user data of each node, thereby expanding the range of decision data for credit assessment of the user and effectively improving the accuracy of credit decision, that is, avoiding damage to the resource amount of the node, and better providing credit support to the target user.

[0104] The following is an apparatus embodiment of the present disclosure, which can be used to execute the method embodiments of the present disclosure. For details not disclosed in the apparatus embodiment of the present disclosure, please refer to the method embodiments of the present disclosure.

[0105] Please refer to FIG. 8, which shows a structural schematic diagram of a credit assessment apparatus provided by an example embodiment of the present disclosure. The credit assessment apparatus can be realized by software, hardware or a combination of both to become all or part of the apparatus. The credit assessment apparatus includes a credit request module 501, a first calculation module 502, a second calculation module 503 and a credit assessment module 504.

[0106] The credit request module 501 is configured to acquire a credit evaluation request for a target user by a first node, the first node being any node in a plurality of nodes included in the credit network; the first calculation module 502 is configured to perform first privacy calculation on user data corresponding to the target user and included in the first node in response to the credit evaluation request, to obtain a first calculation result; the second calculation module 503 is configured to send the first calculation result to at least one second node in the credit network, and acquire a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node; and the credit evaluation module 504 is configured to perform credit evaluation on the target user according to the second calculation result.

[0107] In one or more embodiments, the credit evaluation apparatus further includes a credit network module configured to group at least two nodes in a preset blockchain into the credit network through an alliance protocol.

[0108] In one or more embodiments, the credit evaluation apparatus further includes an information storage module configured to store, in the preset blockchain, credit agency information corresponding to a plurality of nodes included in the blockchain.

[0109] In one or more embodiments, the credit evaluation apparatus further includes a result storage module configured to store, in the credit network, the first calculation result corresponding to the first node and the calculation result corresponding to the at least one second node, the second calculation result including at least the calculation result corresponding to the at least one second node.

[0110] In one or more embodiments, the credit evaluation apparatus further includes a node determination module configured to determine at least one second node from a plurality of nodes included in the credit network according to a range of user data required by the credit evaluation request.

[0111] In one or more embodiments, the second calculation module includes a first calculation unit configured to determine a calculation sequence of performing second privacy calculation on the first calculation result by at least one second node in the credit network according to a calculation method of user data required by the credit evaluation request; and a second calculation unit configured to send the first calculation result and a calculation request to a target node in the at least one second node according to the calculation sequence, and acquire a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request.

[0112] In one or more embodiments, the second computing unit comprises: a first subunit configured to determine, according to the computing sequence, a second node in the plurality of second nodes in a first order as a target node; and a second subunit configured to send the first computing result and a computing request to the target node, so that the at least one second node performs privacy computation on a computing result of a previous second node in the order according to the computing sequence in response to the computing request, until a second computing result sent by a second node in a last order is obtained.

[0113] In one or more embodiments, the second computing unit comprises: a third computing subunit configured to determine each second node as a target node according to the computing sequence; a fourth computing subunit configured to send the first computing result and a computing request to the at least one second node respectively, and obtain a computing result obtained by performing privacy computation on the first computing result by each second node in response to the computing request; and a fifth subunit configured to obtain the second computing result according to at least one of the computing results.

[0114] In one or more embodiments, the credit assessment device further comprises a credit support module configured to provide a resource amount corresponding to the credit assessment result to the target user according to the credit assessment result of the target user.

[0115] In the present disclosure, a credit network is composed of a plurality of nodes including user data, a credit assessment request for credit assessment of a target user for credit support of the target user, a first node in the credit network performs first privacy computation on user data corresponding to the target user according to the credit assessment request, and sends a first computing result to at least one second node in the credit network for second privacy computation and obtains a second computing result, so as to ensure that each node only receives a privacy computation result and not the original user data, avoid user data leakage of each node to other nodes, and improve the security of using user data when performing credit assessment on the user; further, the target user is credit assessed according to the second computing result, in other words, each node in the credit network shares user data of other nodes, and each node cooperates with each other to improve the utilization rate of user data of each node, thereby expanding the range of decision data for credit assessment of the user, effectively improving the accuracy of credit decision, that is, avoiding damage to the resource amount of the node, and better providing credit support for the target user.

[0116] It should be noted that the credit evaluation device provided in the above embodiment is only used as an example to illustrate the division of the above functional modules when the credit evaluation method is performed. In actual application, the above functional distribution can be completed by different functional modules according to the needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the credit evaluation device and the credit evaluation method provided in the above embodiment belong to the same concept, and the implementation process is embodied in the method embodiment. Here, it is not repeated.

[0117] The above sequence numbers of the embodiments of the present disclosure are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0118] The embodiments of the present disclosure also provide a computer storage medium, which can store a plurality of instructions, the instructions being suitable for being loaded and executed by a processor to perform the credit evaluation method of the embodiments shown in FIGS. 1-7 described above. The specific execution process can be referred to the specific description of the embodiments shown in FIGS. 1-7, which is not repeated here.

[0119] The present disclosure also provides a computer program product, which stores at least one instruction, the at least one instruction is loaded and executed by the processor to perform the credit evaluation method of the embodiments shown in FIGS. 1-7 described above. The specific execution process can be referred to the specific description of the embodiments shown in FIGS. 1-7, which is not repeated here.

[0120] Please refer to FIG. 9, which provides a structural schematic diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 9, the electronic device 600 can include at least one processor 601, at least one network interface 604, a user interface 603, a memory 605, and at least one communication bus 602.

[0121] The communication bus 602 is used to realize the connection and communication between the components.

[0122] The user interface 603 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 603 can further include a standard wired interface and a wireless interface.

[0123] The network interface 604 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0124] The processor 601 can include one or more processing cores. The processor 601 connects various parts within the entire electronic device 600 by various interfaces and lines, executes various functions of the electronic device 600 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 605, and calling data stored in the memory 605. Optionally, the processor 601 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 601 can integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes an operating system, a user interface, and an application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 601, but can be realized by a separate chip.

[0125] The memory 605 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 605 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 605 can also be at least one storage device located away from the aforementioned processor 601. As shown in FIG. 9, the memory 605 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a credit evaluation application program.

[0126] In the electronic device 600 shown in FIG. 9, the user interface 603 is mainly used to provide an interface for the user to input and obtain data input by the user; and the processor 601 can be used to call the trust evaluation application stored in the memory 605 and specifically perform the following operations.

[0127] The first node obtains a trust evaluation request for a target user, the first node being any node included in a plurality of nodes of the trust network; in response to the trust evaluation request, performing first privacy calculation on user data corresponding to the target user and included in the first node to obtain a first calculation result; sending the first calculation result to at least one second node in the trust network to obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node; and performing trust evaluation on the target user according to the second calculation result.

[0128] In one embodiment, before the processor 601 performs the operation of obtaining the trust evaluation request for the target user by the first node, the processor 601 further performs the operation of: composing the trust network by at least two nodes in a preset blockchain through an alliance protocol.

[0129] In one embodiment, before the processor 601 performs the operation of composing the trust network by at least two nodes in a preset blockchain through an alliance protocol, the processor 601 further performs the operation of: storing, in the preset blockchain, information of a plurality of nodes included in the blockchain and corresponding to a plurality of trust institutions, respectively.

[0130] In one embodiment, after the processor 601 performs the operation of sending the first calculation result to at least one second node in the trust network to obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node, the processor 601 further performs the operation of: storing, in the trust network, the first calculation result corresponding to the first node and the calculation results corresponding to the at least one second node, respectively, the second calculation result at least including the calculation results corresponding to the at least one second node, respectively.

[0131] In one embodiment, before the processor 601 performs the operation of sending the first calculation result to at least one second node in the trust network to obtain a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node, the processor 601 further performs the operation of: determining at least one second node from a plurality of nodes included in the trust network according to a range of user data required by the trust evaluation request.

[0132] In an embodiment, the processor 601 performs the sending the first calculation result to at least one second node in the trusted network, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node, specifically performs: determining a calculation sequence of performing second privacy calculation on the first calculation result by the at least one second node in the trusted network according to a calculation manner of user data required by the trust evaluation request; and sending the first calculation result and a calculation request to a target node in the at least one second node according to the calculation sequence, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request.

[0133] In an embodiment, the processor 601 performs the sending the first calculation result to at least one second node in the trusted network, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node, specifically performs: determining a calculation sequence of performing second privacy calculation on the first calculation result by the at least one second node in the trusted network according to a calculation manner of user data required by the trust evaluation request; and sending the first calculation result and a calculation request to a target node in the at least one second node according to the calculation sequence, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request.

[0134] In an embodiment, the processor 601 performs the sending the first calculation result to at least one second node in the trusted network, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node, specifically performs: determining a calculation sequence of performing second privacy calculation on the first calculation result by the at least one second node in the trusted network according to a calculation manner of user data required by the trust evaluation request; and sending the first calculation result and a calculation request to a target node in the at least one second node according to the calculation sequence, and obtaining a second calculation result obtained by performing second privacy calculation on the first calculation result by the at least one second node in response to the calculation request.

[0135] In an embodiment, after the processor 601 performs the trust evaluation on the target user according to the second calculation result, the processor 601 further performs: providing a resource amount corresponding to the trust evaluation result to the target user according to the trust evaluation result of the target user.

[0136] In the present disclosure, the credit network is composed of multiple nodes including user data, a credit evaluation request is made for a target user for credit support of the target user, a first node in the credit network performs a first privacy calculation on user data corresponding to the target user according to the credit evaluation request, and sends the first calculation result to at least one second node in the credit network for a second privacy calculation and obtains a second calculation result, so as to ensure that each node only receives a privacy calculation result and not the original user data, avoid user data leakage of each node to other nodes, and improve the security of using user data when performing credit evaluation on the user; further, the target user is evaluated according to the second calculation result, in other words, each node in the credit network shares user data of other nodes, and each node cooperates with each other to improve the utilization rate of user data of each node, thereby expanding the range of decision data for credit evaluation of the user, effectively improving the accuracy of credit decision, that is, avoiding damage to the resource amount of the node, and better providing credit support for the target user.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory or a random access memory.

[0138] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present disclosure.

[0139] The above disclosure is only the preferred embodiment of the present disclosure, and of course cannot limit the scope of the present disclosure, so the equivalent changes made according to the claims of the present disclosure still fall within the scope of the present disclosure.

Claims

1. A credit assessment method, comprising: A first node obtains a credit assessment request for a target user, wherein the first node is any one of a plurality of nodes included in the credit network; In response to the credit assessment request, performing a first privacy calculation on the user data corresponding to the target user included in the first node to obtain a first calculation result; Sending the first calculation result to at least one second node in the trusted network, and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result; Perform a credit assessment on the target user based on the second calculation result.

2. The credit assessment method according to claim 1, before the first node obtains the credit assessment request for the target user, further comprising: At least two nodes in the preset blockchain are combined into the credit network through an alliance protocol.

3. The credit assessment method according to claim 2, before forming the credit network with at least two nodes in the preset blockchain through a consortium agreement, further comprising: The credit institution information corresponding to the multiple nodes included in the blockchain is uploaded to the preset blockchain for storage.

4. The credit assessment method according to claim 2, further comprising: sending the first calculation result to at least one second node in the trusted network, and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result; The first calculation result corresponding to the first node and the calculation results corresponding to the at least one second node are uploaded to the credit network for storage, and the second calculation result at least includes the calculation results corresponding to the at least one second node.

5. The credit assessment method according to claim 1, before sending the first calculation result to at least one second node in the trusted network and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result, further comprising: At least one second node is determined from a plurality of nodes included in the trusted network according to a range of user data required by the trust evaluation request.

6. The credit assessment method according to claim 1 or 5, wherein sending the first calculation result to at least one second node in the trusted network and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result comprises: determining, based on a calculation method for the user data required for the credit assessment request, a calculation order for performing a second privacy-preserving calculation on the first calculation result to at least one second node in the trusted network; The first calculation result and the calculation request are sent to the target node in the at least one second node according to the calculation order, and a second calculation result obtained by the at least one second node performing a second privacy calculation on the first calculation result in response to the calculation request is obtained.

7. The credit assessment method according to claim 6, wherein the sending the first calculation result and a calculation request to a target node among the at least one second node according to the calculation order, and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result in response to the calculation request, comprises: According to the calculation order, determining the second node that comes first in the plurality of second nodes as the target node; The first calculation result and the calculation request are sent to the target node, so that the at least one second node responds to the calculation request and performs privacy calculation on the calculation result of the previous second node in sequence according to the calculation order until the second calculation result sent by the second node in reverse order is obtained.

8. The credit assessment method according to claim 6, wherein the sending the first calculation result and a calculation request to a target node among the at least one second node according to the calculation order, and obtaining a second calculation result obtained by the at least one second node performing a second privacy-preserving calculation on the first calculation result in response to the calculation request, comprises: Determining each of the second nodes as a target node according to the calculation order; Sending the first calculation result and the calculation request to the at least one second node respectively, and obtaining a calculation result obtained by each second node performing a privacy-preserving calculation on the first calculation result in response to the calculation request; The second calculation result is obtained according to at least one of the calculation results.

9. The credit assessment method according to claim 1, after performing the credit assessment on the target user based on the second calculation result, further comprising: According to the credit assessment result of the target user, the target user is provided with an amount of resources corresponding to the credit assessment result.

10. A credit assessment device, comprising: A credit request module, configured to obtain, by a first node, a credit assessment request for a target user, wherein the first node is any one of a plurality of nodes included in the credit network; a first computing module, configured to perform a first privacy computing on the user data corresponding to the target user included in the first node in response to the credit assessment request, to obtain a first computing result; a second computing module, configured to send the first computing result to at least one second node in the trusted network, and obtain a second computing result obtained by the at least one second node performing a second privacy-preserving computing on the first computing result; A credit assessment module is used to perform a credit assessment on the target user according to the second calculation result.

11. A computer storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 9.

12. A computer program product, wherein the computer program product stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 9.

13. An electronic device comprising: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps according to any one of claims 1 to 9.

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