Risk assessment based on multi-party computation

Abstract

Disclosed in the present application are a method and apparatus for performing risk assessment on the basis of multi-party computation, and a storage medium, a product and an electronic device. The method comprises: receiving a risk assessment initiating request for a target user, and determining at least two target data providing nodes corresponding to a user identifier of the target user; using the at least two target data providing nodes to perform multi-party computation processing on target user data corresponding to the user identifier on each target data providing node, so as to obtain risk parameter information of the target user; and performing risk assessment processing on the target user on the basis of the risk parameter information, so as to obtain a risk assessment result for the target user.

Description

Risk assessment based on multi-party computing Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a risk assessment method, device, storage medium, and electronic device based on multi-party computing. Background Art

[0002] Financial institutions can conduct risk assessments on users to evaluate their risk tolerance, and thus recommend investment and financial products that are in line with their risk tolerance, so that the user's risk tolerance matches the risks that may be brought about by investment and financial products. In order to protect user privacy, data between different financial institutions cannot be interoperable. Therefore, in the existing technology, all financial institutions can only conduct risk assessments on users based on the questionnaires filled out by the users at the financial institution and the user data of the users in the financial institution. Therefore, if the user changes to a financial institution, he or she needs to fill out the questionnaire again, and the risk assessment is only performed based on the user's user data in one financial institution. The risk assessment process is cumbersome and the risk assessment results are one-sided. It is necessary to propose a more convenient and more comprehensive risk assessment method.

[0003] Summary of the Invention

[0004] The present invention provides a multi-party computing-based risk assessment method, apparatus, storage medium, and electronic device. These methods utilize multi-party computing to obtain risk parameter information, integrate user data from multiple data provision nodes while maintaining information security, and improve the comprehensiveness and accuracy of risk assessment results. The technical solution is as follows.

[0005] In the first aspect, an embodiment of the present application provides a risk assessment method based on multi-party computing, the method comprising: receiving a risk assessment initiation request for a target user, determining at least two target data providing nodes corresponding to the user identifier of the target user; using the at least two target data providing nodes, performing multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node, and obtaining risk parameter information of the target user; performing risk assessment processing on the target user based on the risk parameter information, and obtaining a risk assessment result of the target user.

[0006] In the second aspect, an embodiment of the present application provides a risk assessment device based on multi-party computing, which includes: a request initiation receiving module, which is used to receive a risk assessment initiation request for a target user and determine at least two target data providing nodes corresponding to the user identifier of the target user; a multi-party computing processing module, which is used to use the at least two target data providing nodes to perform multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user; and a risk assessment module, which is used to perform risk assessment processing on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

[0007] In a third aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0008] In a fourth aspect, an embodiment of the present application provides a computer program product, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0009] In a fifth aspect, an embodiment of the present application provides an electronic device, which may include: 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 above-mentioned method steps.

[0010] In one or more embodiments of the present application, a risk assessment request for a target user is received, at least two target data providing nodes corresponding to the target user's user identifier are determined, and multi-party computing is performed on the target user data corresponding to the user identifier on each target data providing node using the at least two target data providing nodes to obtain risk parameter information for the target user. Based on the risk parameter information, a risk assessment is performed on the target user to obtain a risk assessment result for the target user. By using multi-party computing to obtain risk parameter information and integrating user data from multiple data providing nodes while ensuring information security, the comprehensiveness of the risk assessment results is improved, thereby improving the accuracy of the risk assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] FIG1 is a schematic diagram of the structure of a risk assessment device provided in an embodiment of the present application.

[0013] FIG2 is a flow chart of a risk assessment method based on multi-party computing provided in an embodiment of the present application.

[0014] FIG3 is a flow chart of a risk assessment method based on multi-party computing provided in an embodiment of the present application.

[0015] FIG4 is a schematic diagram illustrating an example of updating a risk assessment file provided in an embodiment of the present application.

[0016] FIG5 is a schematic diagram illustrating an example of a multi-party computing process provided in an embodiment of the present application.

[0017] FIG6 is a schematic structural diagram of a risk assessment device based on multi-party computing provided in an embodiment of the present application.

[0018] FIG7 is a schematic structural diagram of a risk assessment device based on multi-party computing provided in an embodiment of the present application.

[0019] FIG8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] Please refer to Figure 1, which provides a structural diagram of a risk assessment device according to an embodiment of the present application. The data providing node can use a front-end software development kit (SDK) to obtain user data of the user. The data providing node can be a server held by a licensed financial institution, which is used to provide various services of the licensed financial institution on the Internet. Among them, a licensed financial institution refers to a financial institution approved and licensed by the national financial management department. It is understood that only licensed financial institutions can carry out financial activities, such as recommending and providing investment and financial products to users, initiating risk assessment processing for users to assess their risk tolerance, etc. The front-end SDK can be installed in the terminal device held by the user, so as to be quickly embedded in the application client, web page and mini program of the data providing node in the user's terminal device. The front-end SDK can also be an application client for accessing the data providing node in the terminal device held by the user, providing an interface for the user to use the services provided by the data providing node, fill in and upload risk assessment files, etc. Among them, the terminal device can be an electronic device such as a mobile phone, computer, tablet computer, wearable device and vehicle-mounted device.

[0022] The user's terminal device can be installed with the front-end SDKs of multiple data provider nodes, which makes it convenient for the user to use the services provided by multiple data provider nodes. For example, the user can store funds in the financial licensed institution corresponding to data provider node A, and can also store funds in the financial licensed institution corresponding to data structure B at the same time. However, in order to ensure the privacy and security of the user, different financial licensed institutions cannot disclose the user's information to each other. For example, data provider node A cannot inform data provider node B of the user's stored funds on data provider node A, and similarly, data provider node B cannot inform data provider node A of the user's stored funds on data provider node B. It is understandable that the front-end SDK can obtain the user's risk assessment file, which may include the user's age, gender, salary range, family situation, investor risk assessment questionnaire records, etc. After the data provider node obtains the user's risk assessment file through the front-end SDK, it can store the risk assessment file in the data provider node, and the data provider node can also record the user data generated by the user in the data provider node. The user data can be the data generated by the user on the data provider node and related to the risk assessment processing. For example, the user data can be the funds stored by the user on the data provider node, the amount spent on purchasing investment products, the monthly consumption amount, etc.

[0023] Since different data providing nodes cannot leak user data to each other, if a data providing node wants to perform risk assessment on the user by itself, it can only use the user data generated by the user on the data providing node. For example, as shown in Figure 1, the user has user data on both data providing node A and data providing node B. If data providing node A wants to perform risk assessment on the user by itself, it can only perform risk assessment on the user based on the user data generated by the user on data providing node A, and cannot combine the user data generated by the user on data providing node B, which can easily make the risk assessment result one-sided and inaccurate. The risk assessment method provided in the embodiment of the present application can be implemented by relying on a computer program and can be run on a risk assessment device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application. The risk assessment device can use at least one data providing node with user data to perform multi-party computing on the user's user data, thereby obtaining risk parameter information for performing risk assessment on the user. Multi-party computation can be Secure Multi-Party Computation (MPC), which enables multiple data-providing nodes to collaboratively complete computational tasks without a trusted third party, ensuring that each data-providing node cannot access any user data from other data-providing nodes, except for the computational results. Risk parameter information is obtained by integrating user data from various data-providing nodes and is one or more parameters required for user risk assessment, such as monthly spending, disposable assets, and monthly investable amounts.

[0024] The risk assessment device can send a multi-party computing SDK to each data providing node. Each data providing node can install the multi-party computing SDK. The multi-party computing SDK is used to instruct the data providing nodes that have user data to participate in the multi-party computing processing, calculate and split the user data, and then calculate the risk parameter information required for the risk assessment processing.

[0025] The risk assessment device may include a risk assessment file management module, an authorization information management module, a multi-party computing management module, and a blockchain medium. The blockchain medium provides the ability to accept the risk assessment files of users provided by each data providing node, the ability to authenticate the user's identity, and the ability to perform risk assessment on the blockchain. It also serves as a storage medium for risk assessment files, multi-party computing operators, and rules. In order to achieve the purpose of decentralization, the risk assessment device can use a blockchain network to store user-related data. The risk assessment device can issue a user identifier for each user. The user identifier can be a distributed identity (DID). DID is an identity authentication mechanism based on distributed technologies such as blockchain and has the characteristics of decentralization. The risk assessment device and all data providing nodes can use the user identifier to authenticate and verify the user's identity. The risk assessment device can create a block corresponding to the user identifier in the blockchain network based on the user identifier. The block corresponding to the user identifier can be used to store data information of the user corresponding to the user identifier.

[0026] The risk assessment file management module provides user risk assessment file management functionality and can include the ability to store, update, and process risk assessment files. It should be understood that the risk assessment file management device is not used to store user risk assessment files, but rather to store and update user risk assessment files within the blockchain network. It should be understood that user risk assessment files can be stored within the blockchain network. If a data provider node wishes to access or view a user's risk assessment file within the blockchain network, it must obtain the user's authorization information. This authorization information grants the data provider node the right to view the user's risk assessment file within the blockchain network. Therefore, the authorization information management module is used to store the user's authorization information for all data provider nodes. The multi-party computation management module is used to instruct data provider nodes to participate in multi-party computation processing to obtain the risk parameter information required for risk assessment processing.

[0027] The risk assessment method based on multi-party computing provided by this application is described in detail below with reference to specific embodiments.

[0028] Please refer to Figure 2, which is a flowchart of a risk assessment method based on multi-party computing according to an embodiment of the present application. As shown in Figure 2, the method according to the embodiment of the present application may include the following steps S102-S106.

[0029] S102: Receive a wind measurement initiation request for a target user, and determine at least two target data providing nodes corresponding to the user identifier of the target user.

[0030] Specifically, the risk assessment device can receive a risk assessment initiation request for a target user, where the target user can be any user. It is understandable that the risk assessment process for the user needs to be initiated by a licensed financial institution, and the risk assessment device can only provide the calculation process and risk assessment results of the risk assessment process. Therefore, the risk assessment initiation request can be initiated by any data providing node. For example, the user can agree to perform the risk assessment process on the application client corresponding to the data providing node, and the data providing node can then send a risk assessment initiation request for the target user to the risk assessment device.

[0031] The risk assessment device can obtain the user ID of the target user in the wind test initiation request. It is understood that if a user uses the services provided by the data provision node, user data will be left in the data provision node. The data provision node will distinguish and store the user data according to the user ID. Therefore, the risk assessment device can find the target data provision node that contains the target user data of the target user based on the target user's user ID, that is, determine at least two target data provision nodes corresponding to the user ID of the user, wherein the target user data is the user data of the target user, and the target data provision node is the data provision node that contains the target user data.

[0032] S104: Using at least two target data providing nodes, perform multi-party computing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user.

[0033] Specifically, the risk assessment device may utilize at least two target data providing nodes to perform multi-party computation on the target user data corresponding to the user identifier on each target data providing node, thereby obtaining risk parameter information for the target user. The risk parameter information is obtained by integrating the target user data on each target data providing node and comprises one or more parameters required for risk assessment of the target user, such as the user's monthly spending amount, disposable assets, and monthly investable amount. The multi-party computation process may be a multi-party secure computation. For example, the risk assessment device may send a multi-party computation subtask to each target data providing node, and the multi-party computation subtask may instruct the target data providing node to perform multi-party computation on the target user data.

[0034] S106: Perform risk assessment on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

[0035] Specifically, the risk assessment device may perform a risk assessment on the target user based on the risk parameter information, thereby obtaining a risk assessment result for the target user. The risk assessment result is used to reflect the user's risk tolerance. For example, the risk parameter information may include one or more parameters. The risk assessment device may assign a weight to each parameter. Based on the weight and the risk parameter information, a risk tolerance score for the target user may be calculated. The risk assessment result may be a risk tolerance score. A higher risk tolerance score indicates a stronger risk tolerance of the target user, and conversely, a lower risk tolerance score indicates a weaker risk tolerance of the target user.

[0036] The risk assessment device can return the risk assessment results of the target user to the data providing node that initiates the risk assessment request. The data providing node can evaluate the risk tolerance of the target user based on the risk assessment results, and generate an investment plan, recommend investment products, etc. for the target user based on the risk tolerance of the target user.

[0037] In an embodiment of the present application, a risk assessment request for a target user is received, at least two target data providing nodes corresponding to the target user's user identifier are determined, and multi-party computing is performed on the target user data corresponding to the user identifier on each target data providing node using the at least two target data providing nodes to obtain risk parameter information for the target user. Based on the risk parameter information, a risk assessment is performed on the target user to obtain a risk assessment result for the target user. By using multi-party computing to obtain risk parameter information and integrating user data from multiple data providing nodes while ensuring information security, the comprehensiveness of the risk assessment results is improved, thereby improving the accuracy of the risk assessment results.

[0038] Please refer to Figure 3, which is a flowchart of a risk assessment method based on multi-party computing according to an embodiment of the present application. As shown in Figure 3, the method according to the embodiment of the present application may include the following steps S201-S216.

[0039] S202: Receive blockchain storage authorization information sent by the target user, and create a target block corresponding to the user identifier in the blockchain network based on the user identifier of the target user.

[0040] Specifically, a blockchain can be described as a chain of blocks, each containing specific information. These blocks are linked together in chronological order. This chain is stored across all servers. As long as at least one server in the blockchain network is operational, the entire blockchain remains secure. These servers, known as nodes in the blockchain system, provide storage space and computing power for the entire blockchain network. Blockchains are decentralized and tamper-resistant, offering high levels of confidentiality and security. Therefore, risk assessment devices can use the blockchain network to store user data, such as historical risk assessment files and results. Risk assessment devices and all data-providing nodes provide storage space and computing power for the blockchain network.

[0041] It is understandable that if the risk assessment device wants to store the user's data information in the blockchain network, it needs to obtain the user's permission. The target user can send blockchain storage authorization information to the risk assessment device. The blockchain storage authorization information is used to authorize the risk assessment device to store the target user's data information in the blockchain network. The risk assessment device can issue a corresponding user identifier to the target user based on the blockchain storage authorization information. The user identifier can be the target user's DID, which has the characteristics of decentralization. The risk assessment device and all data providing nodes can use this user identifier to authenticate and verify the user. After receiving the target user's blockchain storage authorization information, the risk assessment device can create a target block corresponding to the user identifier in the blockchain network based on the target user's user identifier. The target block can be used to store the target user's data information.

[0042] Optionally, the target user can open the application client corresponding to any data providing node on the terminal device he holds, and then send the blockchain storage authorization information to the risk assessment device through the application client.

[0043] Optionally, the risk assessment device can save the target user's blockchain storage authorization information in the authorization information management module, and save the correspondence between the blockchain storage authorization information and the target user's user identification in the authorization information management module, so that the risk assessment device can query the target user's blockchain storage authorization information in the authorization information management module through the target user's user identification.

[0044] S204: Obtain the risk assessment file of the target user sent by the third data providing node, and save the risk assessment file in the target block in the blockchain network.

[0045] Specifically, a third data providing node can obtain a target user's risk assessment profile. The third data providing node can be any data providing node. The risk assessment profile can include the user's age, gender, salary range, family situation, and investor risk assessment questionnaire records. It is understood that the target user can confirm the creation of a risk assessment profile in the application client corresponding to the third data providing node. The third data providing node can retrieve the target user's risk assessment profile based on the data generated when the target user uses the services provided by the third data providing node. The investor risk assessment questionnaire records can be filled out by the target user in the application client corresponding to the third data providing node. The questions in the investor risk assessment questionnaire records can be set by the third data providing node or by the risk assessment device. After obtaining the target user's risk assessment profile, the third data providing node can send the risk assessment profile to the risk assessment device, which can then store the risk assessment profile in the target block of the blockchain network.

[0046] Optionally, after the risk assessment device obtains the risk assessment file of the target user sent by the third data providing node, it can search in the authorization information management module based on the user ID of the target user to see whether there is blockchain storage authorization information corresponding to the user ID. If so, the risk assessment file of the target user is saved in the target block in the blockchain network.

[0047] Optionally, in addition to the risk assessment device storing the risk assessment file in the target block, a third data providing node may also store the risk assessment file in the target block. The third data providing node may query the risk assessment device for the target user's blockchain storage authorization information. For example, the third data providing node may send a storage authorization query request containing the target user's user identifier to the risk assessment device. After receiving the storage authorization query request, the risk assessment device may search the authorization information management module for the existence of blockchain storage authorization information corresponding to the user identifier. If so, the third data providing node may send the target user's blockchain storage authorization information to the third data providing node. Based on the target user's blockchain storage authorization information, the third data providing node may store the target user's risk assessment file in the target block.

[0048] Optionally, if no other risk assessment profile exists in the target block before the target user's risk assessment profile sent by the third data providing node is obtained, the risk assessment profile sent by the third data providing node is the target user's first risk assessment profile uploaded to the blockchain network. This risk assessment profile can be directly saved in the target block. If another risk assessment profile exists in the target block before the target user's risk assessment profile sent by the third data providing node is obtained, the risk assessment profile sent by the third data providing node is an updated risk assessment profile used to update the target block. It is understood that the target user's data and information will change over time, and the user's updated risk assessment profile will also change, necessitating the use of an updated risk assessment profile to update the risk assessment profile stored in the target block. Due to the tamper-resistant nature of the blockchain network, the risk assessment device can mark the risk assessment profile previously stored in the target block as unavailable and then save the updated risk assessment profile in the target block. When the risk assessment device or other data providing node retrieves the risk assessment profile from the target block, the retrieved risk assessment profile is the one without the unavailable mark.

[0049] Please refer to Figure 4, which provides an example schematic diagram of a risk assessment file update for an embodiment of the present application. The risk assessment file D can be the risk assessment file of the target user sent by the third data providing node. It can be understood that before the risk assessment file D is saved to the target block, the risk assessment file A, risk assessment file B and risk assessment file C already exist in the target block, so the risk assessment file D is an updated risk assessment file, wherein the risk assessment file A and the risk assessment file B are both uploaded to the chain earlier than the risk assessment file C, so the risk assessment file A and the risk assessment file B are both marked with an unavailable mark. Before the risk assessment file D is saved to the target block, the risk assessment file obtained from the target block by the risk assessment device and the data providing node is risk assessment file C. If the risk assessment file D is saved to the target block, the risk assessment file C needs to be marked as unavailable as well. Then, the risk assessment file obtained from the target block by the risk assessment device and the data providing node is risk assessment file D.

[0050] It is understood that once the target user's risk assessment profile is uploaded to the target blockchain, it can be easily viewed by the risk assessment device and other data providing nodes. The user can send query authorization information for the data providing node to the risk assessment device, and the risk assessment device can store the query authorization information in the authorization information management module. For example, the user can send query authorization information for a fourth data providing node to the risk assessment device, where the fourth data providing node can be any data providing node. The risk assessment device can then store the target user's query authorization information for the fourth data providing node in the authorization information management module. To eliminate the need for the target user to complete the investor risk assessment questionnaire and retrieve the target user's risk assessment profile, the fourth data providing node can directly view the target user's risk assessment profile in the blockchain network. The fourth data providing node can send a profile query request for the target user to the risk assessment device. After receiving the profile query request for the target user sent by the fourth data providing node, the risk assessment device can query the authorization information management module for the existence of query authorization information. If query authorization information for the target user for the fourth data providing node exists, the fourth data providing node is allowed to access the risk assessment profile.

[0051] Optionally, the fourth data providing node can obtain the risk assessment file from the risk assessment device, that is, after the risk assessment device confirms that there is query authorization information of the target user for the fourth data providing node, it can obtain the risk assessment file of the target user in the target block, and then send the risk assessment file to the fourth data providing node.

[0052] Optionally, the fourth data providing node can obtain the risk assessment file directly from the blockchain network. That is, after the risk assessment device confirms the existence of the target user's query authorization information for the fourth data providing node, the target user's query authorization information for the fourth data providing node can be sent to the fourth data providing node. The fourth data providing node can obtain the risk assessment file of the target user from the target block of the blockchain network based on the query authorization information.

[0053] S206: Receive a wind measurement initiation request for a target user, and determine at least two target data providing nodes corresponding to the user identifier of the target user.

[0054] Specifically, the risk assessment device can receive a risk assessment initiation request for a target user, where the target user can be any user. It is understandable that the risk assessment process for the user needs to be initiated by a licensed financial institution, and the risk assessment device can only provide the calculation process and risk assessment results of the risk assessment process. Therefore, the risk assessment initiation request can be initiated by any data providing node. For example, the user can agree to perform the risk assessment process on the application client corresponding to the data providing node, and the data providing node can then send a risk assessment initiation request for the target user to the risk assessment device.

[0055] The risk assessment device can obtain the user ID of the target user in the wind test initiation request. It is understood that if a user uses the services provided by the data provision node, user data will be left in the data provision node. The data provision node will distinguish and store the user data according to the user ID. Therefore, the risk assessment device can find the target data provision node that contains the target user data of the target user based on the target user's user ID, that is, determine at least two target data provision nodes corresponding to the user ID of the user, wherein the target user data is the user data of the target user, and the target data provision node is the data provision node that contains the target user data.

[0056] S208: Send a multi-party computing subtask to each target data providing node of at least two target data providing nodes.

[0057] Specifically, the risk assessment device can use at least two target data providing nodes to perform multi-party computing on the target user data corresponding to the user identifier on each target data providing node. The risk assessment device can send a multi-party computing subtask to each target data providing node among the at least two target data providing nodes. The multi-party computing subtask is used to instruct the target data providing node to perform multi-party computing on the target user data corresponding to the user identifier.

[0058] Optionally, the risk assessment device may determine a first data providing node and a second data providing node among at least two target data providing nodes, wherein the first data providing node is any target data providing node among the at least two target data providing nodes, and the second data providing node is a target data providing node among the at least two target data providing nodes other than the first data providing node. The risk assessment device then generates data transmission configuration information and a multi-party computing instruction based on the data of the second data providing node, generates a multi-party computing subtask based on the data transmission configuration information and the multi-party computing instruction, and sends the multi-party computing subtask to the first data providing node.

[0059] The data transmission configuration information is used to instruct the first data providing node to split the target user data corresponding to the target representation and send it to the second data providing node. The data transmission configuration information may include the number of the second data providing nodes and the Internet Protocol (IP) address of the second data providing node. The first data providing node can split the target user data it stores according to the number of the second data providing nodes. For example, if the number of the second data providing nodes is 3, the target user data needs to be split into 4 parts, and then each part of the data obtained after the split processing is sent to each second data providing node according to the IP address of each second data providing node. It can be understood that the first data providing node will split the target user data and send it to the second data providing node, and the first data providing node will also receive the data sent by the second data providing node. The multi-party computing instruction is used to instruct the first data providing node to perform computing on the data sent by the second data providing node to obtain the risk parameter sub-data.

[0060] S210: Receive risk parameter sub-data returned by each target data providing node based on the multi-party computing sub-task.

[0061] Specifically, each target data providing node can calculate and process the data sent by other target data providing nodes based on the multi-party computing instructions in the multi-party computing sub-task to obtain risk parameter sub-data, so each target data providing node can send the calculated risk parameter sub-data to the risk assessment device, and the risk assessment device can receive the risk parameter sub-data returned by each target data providing node based on the multi-party computing sub-task.

[0062] Please refer to Figure 5, which provides an example schematic diagram of a multi-party computing process for an embodiment of the present application. If there are three target data providing nodes corresponding to the user identifier of the target user, namely data providing node A, data providing node B and data providing node C, the risk assessment device can send multi-party computing subtasks to the three target data providing nodes respectively. The target user data can be the user deposit of the user on each data providing node. If the user deposit on data providing node A can be 5W, then data providing node A can send configuration information based on the data in the multi-party computing subtask to split the target user data into three parts, namely -20W and -20W. , 40W, and -15W, then sending 40W to data provider node B and -15W to data provider node C. Similarly, if the user deposit on data provider node B is 40W, data provider node B can split the target user data into -34W, 17W, and -17W, then send -34W to data provider node A and -17W to data provider node C. If the user deposit on data provider node C is 10W, data provider node C can split the target user data into -32W, 40W, and 2W, then send -15W to data provider node A and 17W to data provider node B. The multi-party computation instruction in the multi-party computation subtask can instruct the data provider node to compute the data sent by data provider node B and data provider node C, that is, to sum -20W, -34W, and -32W to obtain the risk parameter sub-data of -86W. Similarly, the risk parameter sub-data calculated by data provider node B is 97W, and the risk parameter sub-data calculated by data provider node C is 4W. Data providing node A, data providing node B and data providing node C can send the calculated risk parameter sub-data to the risk assessment device, and the risk assessment device can obtain the risk parameter sub-data returned by each target data providing node based on the multi-party computing sub-task.

[0063] S212: Integrate and process the risk parameter sub-data to obtain the risk parameter information of the target user.

[0064] Specifically, the risk assessment device may perform integration processing on all risk parameter sub-data. The integration processing may be adding up all risk parameter sub-data to obtain the risk parameter information of the target user.

[0065] It can be understood that risk parameter information contains one or more parameters, so the risk parameter sub-data can also contain one or more parameter sub-data. When performing integration processing, the risk assessment device can add the parameter sub-data of the same item to obtain a parameter, and integrate all parameters to obtain risk parameter information.

[0066] S214: Perform risk assessment on the target user based on the risk assessment file and risk parameter information of the target user to obtain a risk assessment result of the target user.

[0067] Specifically, the risk assessment device obtains the target user's risk assessment file in the target block corresponding to the user identifier according to the target user's user identifier, and then performs risk assessment processing on the target user based on the target user's risk assessment file and risk parameter information to obtain the target user's risk assessment result. The risk assessment result is used to reflect the user's risk tolerance.

[0068] Optionally, the risk assessment result may be a risk tolerance score, where a higher risk tolerance score indicates a stronger risk tolerance of the target user, and conversely, a lower risk tolerance score indicates a weaker risk tolerance of the target user. The risk assessment device may classify the target user based on the risk tolerance score to obtain an investor risk level corresponding to the target user. For example, the risk tolerance score may range from 0 to 100, and the target user may be classified into six investor risk levels, from 1 to 6, based on the tolerance score. A higher investor risk level indicates a stronger risk tolerance of the target user, and conversely, a lower investor risk level indicates a weaker risk tolerance of the target user.

[0069] Optionally, the risk assessment device can use a risk assessment model to perform a risk assessment on the target user based on the target user's risk assessment profile and risk parameter information to obtain the target user's risk tolerance score. The risk assessment model is used to assess the target user's risk tolerance. The risk assessment device can create an initial risk assessment model and obtain a sample risk assessment profile and sample risk parameter information of a sample user, wherein the sample risk assessment profile is the sample user's risk assessment profile, and the sample risk parameter information is the sample user's risk parameter information. The risk assessment device also obtains a sample risk tolerance score obtained by a staff member performing a risk assessment on the sample user. The sample risk assessment profile and sample risk parameter information are input into the initial risk assessment model for at least one round of model training to obtain a predicted risk tolerance score. The risk tolerance score loss of the initial risk assessment model is then calculated based on the predicted risk tolerance score and the sample risk tolerance score. Based on the risk tolerance score loss, the parameters of the initial risk assessment model are adjusted during the back-propagation training process until the initial risk assessment model completes model training to obtain a risk assessment model. The risk tolerance score loss can be the difference between the sample risk tolerance score and the predicted risk tolerance score.

[0070] Optionally, the risk assessment device can return the risk assessment results of the target user to the data providing node that initiates the risk assessment request. The data providing node can evaluate the risk tolerance of the target user based on the risk assessment results, and generate an investment plan, recommend investment products, etc. for the target user based on the risk tolerance of the target user.

[0071] S216, saving the risk assessment result in the target block in the blockchain network.

[0072] Specifically, the risk assessment device can also save the target user's risk assessment results in the target block in the blockchain network for retention, which also makes it convenient for the risk assessment device and the data providing node to query the target user's risk assessment results in the target block, thereby avoiding the user from frequently performing risk assessment processing.

[0073] In an embodiment of the present application, blockchain storage authorization information is received from a target user. Based on the target user's user ID, a target block corresponding to the user ID is created in the blockchain network. A risk assessment profile of the target user sent by a third data provider node is obtained. The risk assessment profile is stored in the target block in the blockchain network. Due to the decentralized and tamper-resistant nature of blockchain, the confidentiality and security of user data are enhanced. Furthermore, authorized data provider nodes can directly query the user's risk assessment profile in the blockchain network, avoiding the need for users to repeatedly fill out their user assessment profiles. A risk assessment request for the target user is received. At least two target data provider nodes corresponding to the target user's user ID are determined. A multi-party computation subtask is sent to each of the at least two target data provider nodes. Risk parameter subdata returned by each target data provider node based on the multi-party computation subtask is received. The risk parameter subdata is integrated and processed to obtain risk parameter information of the target user. A risk assessment is performed on the target user based on the target user's risk assessment profile and risk parameter information to obtain a risk assessment result for the target user. By adopting multi-party computing to obtain risk parameter information and integrating user data in multiple data provision nodes under the condition of information security, the comprehensiveness of risk assessment results is improved, thereby improving the accuracy of risk assessment results. The risk assessment results can also be saved in the target block in the blockchain network for retention.

[0074] The following, in conjunction with Figures 6 and 7, provides a detailed description of the multi-party computation-based risk assessment device provided in the embodiments of this application. It should be noted that the risk assessment device in Figures 6 and 7 is used to execute the method of the embodiments shown in Figures 2 and 3 of this application. For ease of explanation, only the portions relevant to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the embodiments shown in Figures 2 and 3 of this application.

[0075] Please refer to Figure 6, which shows a schematic diagram of the structure of a multi-party computation-based risk assessment device provided by an exemplary embodiment of the present application. The risk assessment device can be implemented as all or part of the device through software, hardware, or a combination of both. The device 1 includes a request receiving module 11, a multi-party computation processing module 12, and a risk assessment module 13.

[0076] The initiation request receiving module 11 is configured to receive a wind measurement initiation request for a target user and determine at least two target data providing nodes corresponding to a user identifier of the target user.

[0077] The multi-party computing processing module 12 is configured to use the at least two target data providing nodes to perform multi-party computing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user.

[0078] The risk assessment module 13 is configured to perform risk assessment on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

[0079] In this embodiment, a risk assessment request for a target user is received, at least two target data providing nodes corresponding to the target user's user identifier are determined, and multi-party computation is performed on the target user data corresponding to the user identifier on each target data providing node using the at least two target data providing nodes to obtain risk parameter information for the target user. Based on the risk parameter information, a risk assessment is performed on the target user to obtain a risk assessment result for the target user. By using multi-party computation to obtain risk parameter information and integrating user data from multiple data providing nodes while ensuring information security, the comprehensiveness of the risk assessment results is improved, thereby improving the accuracy of the risk assessment results.

[0080] Please refer to Figure 7, which shows a schematic diagram of the structure of a multi-party computing-based risk assessment device provided by an exemplary embodiment of the present application. The risk assessment device can be implemented as all or part of the device through software, hardware, or a combination of both. The device 1 includes an authorization storage module 14, a risk assessment file storage module 15, an initiation request receiving module 11, a multi-party computing processing module 12, a risk assessment module 13, a result storage module 16, a file query module 17, and a level classification module 18.

[0081] The authorization storage module 14 is used to receive the blockchain storage authorization information sent by the target user; based on the user identification of the target user, create a target block corresponding to the user identification in the blockchain network.

[0082] The risk assessment file saving module 15 is used to obtain the risk assessment file of the target user sent by the third data providing node, where the risk assessment file is provided by the target user to the third data providing node; and save the risk assessment file in the target block in the blockchain network.

[0083] The initiation request receiving module 11 is configured to receive a wind measurement initiation request for a target user and determine at least two target data providing nodes corresponding to a user identifier of the target user.

[0084] The multi-party computing processing module 12 is configured to use the at least two target data providing nodes to perform multi-party computing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user.

[0085] Optionally, the multi-party computing processing module 12 is specifically used to send a multi-party computing sub-task to each target data providing node among the at least two target data providing nodes, and the multi-party computing sub-task is used to instruct the target data providing node to perform multi-party computing processing on the target user data corresponding to the user identifier; receive the risk parameter sub-data returned by each target data providing node based on the multi-party computing sub-task; and integrate the risk parameter sub-data to obtain the risk parameter information of the target user.

[0086] Optionally, the multi-party computing processing module 12 is specifically used to determine a first data providing node and a second data providing node among the at least two target data providing nodes, the first data providing node being any target data providing node among the at least two target data providing nodes, and the second data providing node being a target data providing node among the at least two target data providing nodes other than the first data providing node; generate data sending configuration information and a multi-party computing instruction based on the second data providing node, the data sending configuration information being used to instruct the first data providing node to split the target user data corresponding to the target representation and send it to the second data providing node, and the multi-party computing instruction being used to instruct the first data providing node to perform computing processing on the data sent by the second data providing node to obtain risk parameter sub-data; generate a multi-party computing sub-task based on the data sending configuration information and the multi-party computing instruction, and send the multi-party computing sub-task to the first data providing node.

[0087] The risk assessment module 13 is configured to perform risk assessment on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

[0088] Optionally, the risk assessment module 13 is specifically configured to perform risk assessment processing on the target user based on the risk assessment file of the target user and the risk parameter information to obtain a risk assessment result of the target user.

[0089] The result storage module 16 is used to store the risk assessment result in the target block in the blockchain network.

[0090] The file query module 17 is used to receive the file query request for the target user sent by the fourth data providing node; if there is query authorization information of the target user for the fourth data providing node, the fourth data providing node is allowed to obtain the risk assessment file.

[0091] The risk assessment result is a risk tolerance score; the level classification module 18 is used to classify the target user based on the risk tolerance score to obtain the investor risk level corresponding to the target user.

[0092] In this embodiment, blockchain storage authorization information is received from a target user. Based on the target user's user ID, a target block corresponding to the user ID is created in the blockchain network. The target user's risk assessment profile is obtained from a third data provider node, and the risk assessment profile is stored in the target block in the blockchain network. Leveraging the decentralized and tamper-resistant nature of blockchain, this improves the confidentiality and security of user data and facilitates authorized data provider nodes to directly query the user's risk assessment profile in the blockchain network, eliminating the need for users to repeatedly fill out their user assessment profiles. A risk assessment request for the target user is received, at least two target data provider nodes corresponding to the target user's user ID are determined, a multi-party computation subtask is sent to each of the at least two target data provider nodes, risk parameter subdata returned by each target data provider node based on the multi-party computation subtask is received, the risk parameter subdata is integrated and processed to obtain risk parameter information of the target user, and a risk assessment is performed on the target user based on the target user's risk assessment profile and risk parameter information, resulting in a risk assessment result for the target user. By adopting multi-party computing to obtain risk parameter information and integrating user data in multiple data provision nodes under the condition of information security, the comprehensiveness of risk assessment results is improved, thereby improving the accuracy of risk assessment results. The risk assessment results can also be saved in the target block in the blockchain network for retention.

[0093] It should be noted that the risk assessment device provided in the above embodiment only uses the division of the above functional modules as an example when executing the risk assessment method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the risk assessment device provided in the above embodiment and the risk assessment method embodiment are based on the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0094] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0095] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions, and the instructions are suitable for being loaded by a processor and executing the risk assessment method of the embodiment shown in Figures 1 to 5 above. The specific execution process can be found in the specific description of the embodiment shown in Figures 1 to 5, and will not be repeated here.

[0096] The present application also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded by the processor and executes the risk assessment method of the embodiment shown in Figures 1 to 5 above. The specific execution process can be found in the specific description of the embodiment shown in Figures 1 to 5, and will not be repeated here.

[0097] Please refer to Figure 8, which shows a block diagram of an electronic device provided by an exemplary embodiment of the present application. The electronic device in the present application may include one or more of the following components: a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, the memory 120, the input device 130, and the output device 140 may be connected via the bus 150.

[0098] The processor 110 may include one or more processing cores. The processor 110 utilizes various interfaces and circuits to connect various components within the electronic device. It executes instructions, programs, code sets, or instruction sets stored in the memory 120, as well as accesses data stored in the memory 120, to perform various functions and process data for the terminal 100. Optionally, the processor 110 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 110 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interfaces, and applications; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 110 and may be implemented separately via a communication chip.

[0099] The memory 120 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 120 includes a non-transitory computer-readable storage medium. The memory 120 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The operating system may be an Android system, including a system deeply developed based on the Android system, an iOS system developed by Apple, including a system deeply developed based on the iOS system or other systems.

[0100] The memory 120 can be divided into an operating system space and a user space. The operating system runs in the operating system space, and native and third-party applications run in the user space. In order to ensure that different third-party applications can achieve better operating results, the operating system allocates corresponding system resources to different third-party applications. However, the requirements for system resources in different application scenarios in the same third-party application are also different. For example, in the local resource loading scenario, the third-party application has higher requirements for disk reading speed; in the animation rendering scenario, the third-party application has higher requirements for GPU performance. The operating system and the third-party application are independent of each other, and the operating system often cannot perceive the current application scenario of the third-party application in a timely manner, resulting in the operating system being unable to perform targeted system resource adaptation according to the specific application scenario of the third-party application.

[0101] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to open up data communication between third-party applications and the operating system so that the operating system can obtain the current scenario information of third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0102] The input device 130 is used to receive input commands or data and includes, but is not limited to, a keyboard, a mouse, a camera, a microphone, or a touch-sensitive device. The output device 140 is used to output commands or data and includes, but is not limited to, a display device and a speaker. In one example, the input device 130 and the output device 140 may be combined, and the input device 130 and the output device 140 may be a touch-sensitive display.

[0103] The touch display screen can be designed as a full screen, a curved screen or a special-shaped screen. The touch display screen can also be designed as a combination of a full screen and a curved screen, or a combination of a special-shaped screen and a curved screen, which is not limited in the present embodiment.

[0104] In addition, those skilled in the art will understand that the structures of the electronic devices shown in the above figures do not limit the electronic devices. The electronic devices may include more or fewer components than shown, or may combine certain components, or arrange the components differently. For example, the electronic devices may also include radio frequency circuits, input units, sensors, audio circuits, wireless fidelity (WiFi) modules, power supplies, Bluetooth modules, and other components, which will not be described in detail here.

[0105] In the electronic device shown in Figure 8, the processor 110 can be used to call the risk assessment application based on multi-party computing stored in the memory 120, and specifically perform the following operations: receive a risk assessment initiation request for a target user, and determine at least two target data providing nodes corresponding to the user identifier of the target user; use the at least two target data providing nodes to perform multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user; perform risk assessment processing on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

[0106] In one embodiment, when the processor 110 executes multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node using the at least two target data providing nodes to obtain the risk parameter information of the target user, it specifically performs the following operations: sending a multi-party computing subtask to each target data providing node among the at least two target data providing nodes, the multi-party computing subtask is used to instruct the target data providing node to perform multi-party computing processing on the target user data corresponding to the user identifier; receiving the risk parameter sub-data returned by each target data providing node based on the multi-party computing subtask; and integrating the risk parameter sub-data to obtain the risk parameter information of the target user.

[0107] In one embodiment, when executing the multi-party computing subtask of sending a multi-party computing subtask to each target data providing node among the at least two target data providing nodes, the processor 110 specifically performs the following operations: determining a first data providing node and a second data providing node among the at least two target data providing nodes, where the first data providing node is any target data providing node among the at least two target data providing nodes, and the second data providing node is a target data providing node among the at least two target data providing nodes other than the first data providing node; generating data sending configuration information and a multi-party computing instruction based on the second data providing node, where the data sending configuration information is used to instruct the first data providing node to split the target user data corresponding to the target representation and send it to the second data providing node, and the multi-party computing instruction is used to instruct the first data providing node to perform computing on the data sent by the second data providing node to obtain risk parameter sub-data; generating a multi-party computing subtask based on the data sending configuration information and the multi-party computing instruction, and sending the multi-party computing subtask to the first data providing node.

[0108] In one embodiment, before executing receiving a wind test initiation request for a target user and determining at least two target data providing nodes that store the target user data of the target user, the processor 110 further performs the following operations: receiving blockchain storage authorization information sent by the target user; and creating a target block corresponding to the user identifier in the blockchain network based on the user identifier of the target user.

[0109] In one embodiment, before executing the receipt of a risk assessment request for a target user, the processor 110 further performs the following operations: obtaining a risk assessment profile of the target user sent by a third data providing node, where the risk assessment profile is provided by the target user to the third data providing node; and saving the risk assessment profile in a target block in the blockchain network.

[0110] In one embodiment, when the processor 110 performs risk assessment processing on the target user based on the risk parameter information and obtains the risk assessment result of the target user, it specifically performs the following operations: performing risk assessment processing on the target user based on the risk assessment file of the target user and the risk parameter information and obtaining the risk assessment result of the target user.

[0111] In one embodiment, when executing the risk assessment method, the processor 110 further performs the following operations: saving the risk assessment result in the target block in the blockchain network.

[0112] In one embodiment, when executing the risk assessment method, the processor 110 further performs the following operations: receiving a file query request for the target user sent by the fourth data providing node; if there is query authorization information of the target user for the fourth data providing node, allowing the fourth data providing node to obtain the risk assessment file.

[0113] In one embodiment, the risk assessment result is a risk tolerance score; when executing the risk assessment method, the processor 110 further performs the following operations: classifying the target user based on the risk tolerance score to obtain the investor risk level corresponding to the target user.

[0114] In this embodiment, blockchain storage authorization information is received from a target user. Based on the target user's user ID, a target block corresponding to the user ID is created in the blockchain network. The target user's risk assessment profile is obtained from a third data provider node, and the risk assessment profile is stored in the target block in the blockchain network. Leveraging the decentralized and tamper-resistant nature of blockchain, this improves the confidentiality and security of user data and facilitates authorized data provider nodes to directly query the user's risk assessment profile in the blockchain network, eliminating the need for users to repeatedly fill out their user assessment profiles. A risk assessment request for the target user is received, at least two target data provider nodes corresponding to the target user's user ID are determined, a multi-party computation subtask is sent to each of the at least two target data provider nodes, risk parameter subdata returned by each target data provider node based on the multi-party computation subtask is received, the risk parameter subdata is integrated and processed to obtain risk parameter information of the target user, and a risk assessment is performed on the target user based on the target user's risk assessment profile and risk parameter information, resulting in a risk assessment result for the target user. By adopting multi-party computing to obtain risk parameter information and integrating user data in multiple data provision nodes under the condition of information security, the comprehensiveness of risk assessment results is improved, thereby improving the accuracy of risk assessment results. The risk assessment results can also be saved in the target block in the blockchain network for retention.

[0115] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0116] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

[0117] 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 used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this specification are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the user identification, user data, and risk assessment files involved in this specification are all obtained with full authorization.

Claims

1. A risk assessment method based on multi-party computing, the method comprising: Receive a wind measurement initiation request for a target user, and determine at least two target data providing nodes corresponding to a user identifier of the target user; Using the at least two target data providing nodes, performing multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user; A risk assessment process is performed on the target user based on the risk parameter information to obtain a risk assessment result of the target user.

2. The method according to claim 1, wherein the at least two target data providing nodes are used to perform multi-party computing on the target user data corresponding to the user identifier on each target data providing node to obtain the risk parameter information of the target user, comprising: Sending a multi-party computing subtask to each of the at least two target data providing nodes, wherein the multi-party computing subtask is used to instruct the target data providing node to perform multi-party computing processing on the target user data corresponding to the user identifier; Receiving the risk parameter sub-data returned by each target data providing node based on the multi-party computing sub-task; The risk parameter sub-data are integrated and processed to obtain the risk parameter information of the target user.

3. The method according to claim 2, wherein sending a multi-party computing subtask to each of the at least two target data providing nodes comprises: Determine a first data providing node and a second data providing node among the at least two target data providing nodes, wherein the first data providing node is any target data providing node among the at least two target data providing nodes, and the second data providing node is a target data providing node among the at least two target data providing nodes except the first data providing node; Generate data transmission configuration information and multi-party computing instructions based on the second data providing node, wherein the data transmission configuration information is used to instruct the first data providing node to split the target user data corresponding to the target representation and send it to the second data providing node, and the multi-party computing instructions are used to instruct the first data providing node to perform computing processing on the data sent by the second data providing node to obtain risk parameter sub-data; A multi-party computing subtask is generated based on the data transmission configuration information and the multi-party computing instruction, and the multi-party computing subtask is sent to the first data providing node.

4. The method according to claim 1, before receiving a wind measurement initiation request for a target user and determining at least two target data providing nodes storing target user data of the target user, further comprises: Receive blockchain storage authorization information sent by the target user; Based on the user identification of the target user, a target block corresponding to the user identification is created in the blockchain network.

5. The method according to claim 4, before receiving the wind measurement initiation request for the target user, further comprising: Acquire a risk assessment file of the target user sent by a third data providing node, where the risk assessment file is provided by the target user to the third data providing node; The risk assessment file is stored in a target block in the blockchain network.

6. The method according to claim 5, wherein the step of performing risk assessment on the target user based on the risk parameter information to obtain the risk assessment result of the target user comprises: A risk assessment process is performed on the target user based on the risk assessment file of the target user and the risk parameter information to obtain a risk assessment result of the target user.

7. The method according to claim 4 or 6, further comprising: The risk assessment result is stored in the target block in the blockchain network.

8. The method according to claim 5, further comprising: receiving a profile query request for the target user sent by a fourth data providing node; If there is query authorization information of the target user for the fourth data providing node, the fourth data providing node is allowed to obtain the risk assessment file.

9. The method according to claim 1 or 6, wherein the risk assessment result is a risk tolerance score; The method further comprises: The target users are classified based on the risk tolerance scores to obtain investor risk levels corresponding to the target users.

10. A risk assessment device based on multi-party computing, the device comprising: An initiation request receiving module, used to receive a wind measurement initiation request for a target user, and determine at least two target data providing nodes corresponding to the user identifier of the target user; A multi-party computing processing module, configured to use the at least two target data providing nodes to perform multi-party computing processing on the target user data corresponding to the user identifier on each target data providing node to obtain risk parameter information of the target user; The risk assessment module is used to perform risk assessment processing on the target user based on the risk parameter information to obtain the risk assessment result of the target user.

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 as claimed in any one of claims 1 to 9.

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