Business data sharing method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a business data sharing method and system, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: at a first data node, performing hash calculation on first service data according to a hash function to obtain a first service hash value, encrypting a random integer value according to a one-way encryption function to obtain a first service encryption value, and at the first data node, establishing a mapping relation from the first service hash value to the first service encryption value, performing model construction at the first data node according to the second business data, the hash function, the one-way encryption function and the first privacy calculation model to obtain a second privacy calculation model, and performing one-way encryption on the first data node according to the first business hash value, the one-way encryption function, the first business encryption value and the second privacy calculation model to obtain a second privacy calculation model; and sharing the first business data to the second data node, thereby realizing the sharing of the business data while ensuring the security of the business data.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a business data sharing method and system, electronic device and storage medium. Background Art

[0002] Due to data security and privacy concerns, business data between financial institutions is often not interoperable. In order to improve the business efficiency and quality of financial institutions, financial institutions need to share data resources and achieve interoperability of business data on the basis of ensuring the security of business data. For example, in the credit field, in order to improve credit efficiency, financial institutions share borrowers' credit information to assess borrowers' credit risks. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a business data sharing method and system, an electronic device and a storage medium, aiming to achieve business data sharing while ensuring data security.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a business data sharing method, which is applied to a business data sharing system, wherein the business data sharing system includes a first data node and a second data node, wherein the first data node stores first business data, and the second data node stores second business data, and the first data node and the second data node share a hash function and a shared one-way encryption function, and the method includes:

[0005] At the first data node, performing hash calculation on the first service data according to the hash function to obtain a first service hash value, and encrypting a preset random integer value according to the one-way encryption function to obtain a first service encrypted value;

[0006] At the first data node, a mapping relationship between the first business hash value and the first business encryption value is established to obtain a first privacy computing model, and the first privacy computing model is sent to the second data node;

[0007] At the second data node, a model is constructed according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and the second privacy computing model is sent to the first data node;

[0008] At the first data node, data access rights are determined according to the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and the first business data is shared with the second data node according to the data access rights; the data access rights are used to indicate whether the first business data is shared data or private data.

[0009] In some embodiments, constructing a model according to the second business data, the hash function, the one-way encryption function, and the first privacy computing model to obtain a second privacy computing model includes:

[0010] Performing hash calculation on the second service data according to the hash function to obtain a second service hash value;

[0011] Performing privacy calculation on the second business hash value through the first privacy calculation model to obtain initial privacy data;

[0012] Encrypting the initial private data according to the one-way encryption function to obtain candidate private data;

[0013] A mapping relationship between the second business hash value and the candidate privacy data is established to obtain the second privacy calculation model.

[0014] In some embodiments, determining the data access permission according to the first business hash value, the one-way encryption function, the first business encryption value, and the second privacy computing model includes:

[0015] Encrypting the first business hash value according to the second privacy computing model to obtain a reference business encrypted value;

[0016] Encrypting the first service encryption value according to the one-way encryption function to obtain a second service encryption value;

[0017] If the reference service encryption value and the second service encryption value are equal, then the data access permission indicates that the first service data is shared data;

[0018] If the reference service encryption value and the second service encryption value are not equal, the data access permission indicates that the first service data is private data.

[0019] In some embodiments, the business data sharing system further includes a data verification node, the second data node includes a first subnode and a second subnode, the hash function includes a hash modulus, the one-way encryption function includes an encryption exponent, and after sharing the first business data with the second data node according to the data access permission, the method further includes:

[0020] At the first data node, construct a model according to the preset random integer value to obtain a reference privacy computing model, and send the reference privacy computing model to the first child node and the second child node respectively;

[0021] At the first child node, the hash modulus is obtained to obtain a first modulus, the encryption exponent is obtained to obtain a first exponent, the reference privacy computing model is obtained to obtain a first candidate model, the first business data is obtained to obtain first reference data, and the first modulus, the first exponent, the first candidate model, the first reference data, and the first privacy computing model are sent to the data verification node;

[0022] At the second child node, the hash modulus is obtained to obtain a second modulus, the encryption exponent is obtained to obtain a second exponent, the reference privacy computing model is obtained to obtain a second candidate model, the first business data is obtained to obtain second reference data, and the second modulus, the second exponent, the second candidate model, the second reference data and the second privacy computing model are sent to the data verification node;

[0023] At the data verification node, performing a modulus consistency check on the first modulus and the second modulus to obtain a reference modulus, performing an exponent consistency check on the first index and the second index to obtain a reference index, performing a model consistency check on the first candidate model and the second candidate model to obtain a benchmark calculation model, and performing a data consistency check on the first reference data and the second reference data to obtain benchmark service data;

[0024] At the data verification node, the accuracy of the benchmark business data is verified according to the reference modulus, the reference index, the benchmark calculation model, the first privacy calculation model and the second privacy calculation model to obtain the target business data.

[0025] In some embodiments, the performing accuracy verification on the benchmark business data according to the reference modulus, the reference index, the benchmark computing model, the first privacy computing model, and the second privacy computing model to obtain the target business data includes:

[0026] Constructing a target computing model according to the reference index, the first privacy computing model, and the second privacy computing model;

[0027] Determining reference business data according to the reference modulus, the reference index, the benchmark computing model, the target computing model, and the second privacy computing model;

[0028] A consistency check is performed on the benchmark business data and the reference business data to obtain the target business data.

[0029] In some embodiments, determining reference business data according to the reference modulus, the reference index, the benchmark computing model, the target computing model, and the second privacy computing model includes:

[0030] Solving the target computing model according to the reference modulus to obtain first candidate business data;

[0031] Determining second candidate service data according to the reference modulus, the reference index and the benchmark calculation model;

[0032] Performing privacy calculation on the first candidate business data according to the second privacy calculation model to obtain third candidate business data;

[0033] The first candidate business data is screened according to the second candidate business data and the third candidate business data to obtain the reference business data.

[0034] In some embodiments, determining the second candidate service data according to the reference modulus, the reference index and the benchmark calculation model includes:

[0035] Solving the benchmark calculation model according to the reference modulus to obtain initial business data;

[0036] constructing a reference encryption function based on the reference modulus and the reference exponent;

[0037] The initial service data is encrypted according to the reference encryption function to obtain the second candidate service data.

[0038] To achieve the above purpose, a second aspect of an embodiment of the present application proposes a business data sharing system, the business data sharing system comprising:

[0039] A first data node, wherein the first data node stores first business data;

[0040] A second data node, wherein the second data node stores second business data, and the first data node and the second data node share a hash function and a one-way encryption function;

[0041] The first data node is used to perform a hash calculation on the first business data according to the hash function to obtain a first business hash value, and to encrypt a preset random integer value according to the one-way encryption function to obtain a first business encrypted value; establish a mapping relationship between the first business hash value and the first business encrypted value to obtain a first privacy computing model, and send the first privacy computing model to the second data node;

[0042] The second data node is used to construct a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and send the second privacy computing model to the first data node;

[0043] The first data node is used to determine the data access permission based on the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and share the first business data with the second data node based on the data access permission; the data access permission is used to indicate that the first business data is shared data or private data.

[0044] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0045] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0046] The business data sharing method, business data sharing system, electronic device and computer-readable storage medium proposed in the embodiment of the present application, by performing hash calculation on the first business data according to the hash function at the first data node, converting the first business data into a hash value, and obtaining a first business hash value, so as to confuse the first business data and protect the privacy of the first business data. The preset random integer value is encrypted according to the one-way encryption function, ensuring the irreversibility of the preset random integer value, and obtaining the first business encryption value. In order to safely calculate the data intersection between the first data node and the second data node without leaking the business data of the first data node, a mapping relationship from the first business hash value to the first business encryption value is established, and a first privacy calculation model is obtained. The data distribution characteristics of the first business data are represented by the first privacy calculation model, and the first privacy calculation model is sent to the second data node. By sending the model instead of the business data, the privacy of the business data on the first data node can be effectively protected, the risk of data leakage is reduced, and the network resources required for data transmission are reduced. In order to capture and represent the data distribution characteristics of the business data on the second data node, and establish the connection between the first business data and the second business data, at the second data node, a model is constructed according to the second business data, the hash function, the one-way encryption function and the first privacy calculation model to obtain a second privacy calculation model. In order to obtain the data intersection between the first data node and the second data node, the second privacy computing model is sent to the first data node. At the first data node, the data access right is determined according to the first business hash value, the one-way encryption function, the first business encryption value, and the second privacy computing model, so as to determine that the first business data is shared by the first data node and the second data node according to the data access right, and the first business data is shared with the second data node, so as to realize the sharing of shared data without leaking other business data, thereby ensuring the data security of business data sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a business data sharing method provided in an embodiment of the present application;

[0048] Figure 2 yes Figure 1 Flow chart of step S130 in FIG.

[0049] Figure 3 yes Figure 1 Flow chart of step S140 in FIG.

[0050] Figure 4 is another flow chart of the business data sharing method provided in an embodiment of the present application;

[0051] Figure 5 yes Figure 4 Flow chart of step S450 in FIG.

[0052] Figure 6 yes Figure 5 Flow chart of step S520 in FIG.

[0053] Figure 7 yes Figure 6 Flowchart of step S620 in FIG.

[0054] Figure 8 It is a structural diagram of a business data sharing system provided in an embodiment of the present application;

[0055] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] Due to data security and privacy concerns, business data between financial institutions is often not interoperable. In order to improve the business efficiency and quality of financial institutions, financial institutions need to share data resources and achieve interoperability of business data on the basis of ensuring the security of business data. For example, in the credit field, in order to improve credit efficiency, financial institutions share borrowers' credit information to assess borrowers' credit risks.

[0060] Based on this, the embodiments of the present application provide a business data sharing method, a business data sharing system, an electronic device and a computer-readable storage medium, aiming to achieve business data sharing while ensuring data security.

[0061] The business data sharing method, business data sharing system, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the business data sharing method in the embodiments of the present application is described.

[0062] The business data sharing method provided in the embodiment of the present application relates to the field of financial technology. The business data sharing method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the business data sharing method, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0065] Figure 1 is an optional flow chart of the business data sharing method provided in the embodiment of the present application. Figure 1 The method is applied to a business data sharing system, which includes a first data node and a second data node, the first data node and the second data node are business servers of different financial institutions, the first data node stores first business data, the second data node stores second business data, the first business data and the second business data are business data generated by business services provided by different financial institutions, and the business data may be transaction data, customer data, product data, etc. In order to realize the sharing of business data on different data nodes, a hash function and a one-way encryption function are shared between the first data node and the second data node. The business data sharing method may include but is not limited to steps S110 to S140.

[0066] Step S110: At the first data node, a hash calculation is performed on the first service data according to a hash function to obtain a first service hash value, and a preset random integer value is encrypted according to a one-way encryption function to obtain a first service encrypted value;

[0067] Step S120: at the first data node, establish a mapping relationship between the first business hash value and the first business encryption value, obtain a first privacy computing model, and send the first privacy computing model to the second data node;

[0068] Step S130: at the second data node, construct a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and send the second privacy computing model to the first data node;

[0069] Step S140, at the first data node, determine the data access permission according to the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and share the first business data with the second data node according to the data access permission; the data access permission is used to indicate that the first business data is shared data or private data.

[0070] In steps S110 to S140 shown in the embodiment of the present application, the data distribution characteristics of the first data node are represented by a first privacy computing model, and the second privacy computing model is determined by using the first privacy computing model, so as to capture the data correlation relationship between the first data node and the second data node by using the second privacy computing model, and determine the common business data between the first data node and the second data node, thereby achieving sharing of the common business data without leaking other business data, and ensuring the security of the business data.

[0071] In step S110 of some embodiments, a hash function is a function that maps input data of any length to an output value of a fixed length, and the output value is a hash value. The hash function is one-way, and the original input cannot be derived from the hash value. The hash function is a hash-based message authentication code function (Hash-based Message Authentication Code, HMAC), including a target key and a hash modulus, and the target key is obtained by the first data node and the second data node using a key agreement protocol such as the Diffie-Hellman key exchange protocol or generated using a key derivation function. The business data sharing system also includes a data verification node, which is a server responsible for verifying the correctness of the data. The hash modulus is a positive integer, and the hash modulus is generated by the data verification node calling a modulus generation algorithm. Specifically, the first data node sends a first modulus generation request to the data verification node, and the second data node sends a second modulus generation request to the data verification node. The data verification node extracts a first parameter from the first modulus generation request and a second parameter from the second modulus generation request. The first parameter and the second parameter are both even numbers. The largest parameter of the first parameter and the second parameter is selected as the target parameter. The target parameter is used to specify the key length of the asymmetric key, such as 1024 bits or 2048 bits, which can determine the security of the asymmetric key. The larger the target parameter, the longer the key length, the more difficult it is to crack the asymmetric key, and the security of the asymmetric key is higher. Randomly generate two prime numbers with a length of half the target parameter, and multiply the two prime numbers to obtain a hash modulus. It should be noted that in order to ensure security, the difference between the two prime numbers should be large enough. Determine the encryption exponent based on the two prime numbers. If the two prime numbers are represented by p and q, the encryption exponent is a number that is relatively prime to (p-1)*(q-1), * represents a multiplication operation, and the encryption exponent is an integer greater than 1 and less than (p-1)*(q-1). The data verification node saves two prime numbers p, q and a hash modulus N, and sends the hash modulus and the encryption exponent to the first data node and the second data node respectively.

[0072] At the first data node, the first business data is hashed according to the hash function to obtain the first business hash value, which is the hash value of the first business data. The first business data may be an order for selling insurance products, insurance claim records, customer information, etc. The one-way encryption function is determined according to the hash modulus and the encryption index, and the one-way encryption function is expressed as:

[0073] F e,N (x) = x e mod N = y,

[0074] Among them, x represents input data; y represents output data; e represents encryption exponent; and N represents hash modulus.

[0075] The one-way encryption function is one-way and can obtain output according to the given input, hash modulus and encryption exponent. The input cannot be reversely calculated according to the output. The residual class space is determined according to the hash modulus and the integer space, and integers are uniformly randomly selected in the residual class space to generate corresponding preset random integer values ​​for each first business data. The residual class space is a set of integers with the same remainder after each integer in the integer space is divided by the hash modulus. The preset random integer value is input into the one-way encryption function for encryption calculation to obtain the first business encryption value.

[0076] In step S120 of some embodiments, at the first data node, a Lagrange interpolation algorithm is used to perform polynomial fitting on the first business hash value of each first business data and its corresponding first business encryption value, a mapping relationship from the first business hash value to the first business encryption value is established, a first privacy computing model is obtained, and the first privacy computing model is sent to the second data node.

[0077] The Lagrange interpolation algorithm is represented by Interpoly({(x i ,y i )})→f, where (x i ,y i ) is the data point, x i is the input value, y i is the output value, f is the polynomial fitted according to the data points, and satisfies f(x i )=y i .

[0078] The first business data is taken as an element, and a data set is constructed according to each first business data. The first business hash value of the data set is expressed as The preset random integer values ​​of the dataset are represented as |S R | is the number of first business data in the data set. The first business data and the first business encrypted value obtained by encrypting the preset random integer value constitute a data pair, and the first privacy computing model is a polynomial fitted by all data pairs. The first privacy computing model is expressed as:

[0079] f R =Interpoly({(x i ,F e,N (r i ))}),

[0080] Among them, f R represents the first privacy computing model; Interpoly represents the Lagrangian interpolation algorithm.

[0081] See also Figure 2In some embodiments, step S130 may include but is not limited to steps S210 to S240:

[0082] Step S210, performing hash calculation on the second service data according to the hash function to obtain a second service hash value;

[0083] Step S220, performing privacy calculation on the second business hash value through the first privacy calculation model to obtain initial privacy data;

[0084] Step S230, encrypting the initial private data according to a one-way encryption function to obtain candidate private data;

[0085] Step S240: Establish a mapping relationship between the second business hash value and the candidate privacy data to obtain a second privacy calculation model.

[0086] In step S210 of some embodiments, at the second data node, a hash calculation is performed on the second business data according to the hash function to obtain a second business hash value. The hash function used by the second data node is the same as that of the first data node. The second business hash value is the hash value of the second business data.

[0087] In step S220 of some embodiments, the second business hash value is used as an input value and input into the first privacy computing model for privacy computing, and the output value of the first privacy computing model is used as initial privacy data.

[0088] In step S230 of some embodiments, the initial private data is input into a one-way encryption function for encryption to obtain candidate private data.

[0089] In step S240 of some embodiments, at the second data node, a Lagrange interpolation algorithm is used to perform polynomial fitting on the second business hash value of each second business data and its corresponding candidate privacy data, a mapping relationship from the second business hash value to the candidate privacy data is established, a second privacy calculation model is obtained, and the second privacy calculation model is sent to the first data node.

[0090] The second data node stores a plurality of second business data, and the second business hash value of each second business data is expressed as: |S1| is the number of second business data, and the initial privacy data of the i-th second business hash value is represented by f R (x′ i ), the candidate private data is represented as F e,N (f R (x′ i )), the second privacy computing model is expressed as:

[0091] f1=Interpoly({(x′ i,F e,N (f R (x′ i )))}),

[0092] Among them, f1 is the second privacy computing model.

[0093] Through the above steps S210 to S240, a second privacy computing model can be obtained, so as to use the second privacy computing model to capture the data distribution characteristics shared by the second business data and the first business data.

[0094] See also Figure 3 In some embodiments, step S140 may include but is not limited to steps S310 to S340:

[0095] Step S310, encrypting the first business hash value according to the second privacy computing model to obtain a reference business encrypted value;

[0096] Step S320, encrypting the first service encryption value according to a one-way encryption function to obtain a second service encryption value;

[0097] Step S330: If the reference service encryption value and the second service encryption value are equal, the data access permission indicates that the first service data is shared data;

[0098] Step S340: If the reference business encryption value and the second business encryption value are not equal, the data access permission indicates that the first business data is private data.

[0099] In step S310 of some embodiments, at the first data node, the first business hash value is input into the second privacy computing model, the privacy computing model is used to perform privacy computing on the first business hash value, and the output value of the second privacy computing model is used as a reference business encryption value.

[0100] In step S320 of some embodiments, the first service encryption value is one-way encrypted according to the one-way encryption function to obtain the second service encryption value. It should be noted that the number of one-way encryption calculations performed on the preset random integer value according to the one-way encryption function is equal to the number of data nodes. If the number of data nodes is 2, the number of one-way encryption calculations is also 2.

[0101] In step S330 of some embodiments, if the reference business encryption value and the second business encryption value are equal, it means that the first business data is data shared by the first data node and the second data node obtained by privacy intersection calculation, then the data access permission indicates that the first business data is shared data, and the first business data is shared with the second data node using a secure channel.

[0102] For example, when a customer applies for a credit card at a financial structure, the first data node will store the first credit card user list. When the customer applies for a credit card at another financial structure, the second data node will store the second credit card user list. Through the privacy computing model, the common customer information of the first data node and the second data node can be obtained without touching the plaintext data, but without leaking their respective customer lists.

[0103] In step S340 of some embodiments, if the reference business encryption value and the second business encryption value are not equal, it means that the first business data is the business data of the first data node, not the business data of the second data node, and the data access permission indicates that the first business data is private data, then the first data node does not provide the first business data to the second data node.

[0104] Through the above steps S310 to S340, the shared data between the first data node and the second data node can be shared without leaking other business data of each node. While ensuring data security, cross-financial institution sharing of business data is achieved, enabling financial institutions to better allocate business resources and improve the efficiency of business execution.

[0105] The second data node includes a first subnode and a second subnode, the second business data includes the first subdata and the second subdata, the first subnode stores the first subdata, and the second subnode stores the second subdata. The first subnode and the second subnode share a hash function and a one-way encryption function. The business data sharing method of the embodiment of the present application includes:

[0106] At the first data node, the first business data is hashed according to the hash function to obtain a first business hash value. The preset random integer value is encrypted according to the one-way encryption function to obtain a first business encrypted value. The first business hash value and the first business encrypted value are polynomially fitted using the Lagrange interpolation algorithm to obtain a first privacy computing model, and the first privacy computing model is sent to the first child node.

[0107] At the first child node, the first child data is hashed according to the hash function to obtain the second business hash value. The second business hash value is privacy-calculated using the first privacy calculation model to obtain initial privacy data. The initial privacy data is encrypted according to the one-way encryption function to obtain candidate privacy data. The second business hash value and the candidate privacy data are polynomially fitted using the Lagrange interpolation algorithm to obtain an intermediate privacy calculation model, and the intermediate privacy calculation model is sent to the second child node.

[0108] At the second child node, the second child data is hashed according to the hash function to obtain the target child hash value. The target child hash value is privacy-computing calculated using the intermediate privacy computing model to obtain the first child private data. The first child private data is encrypted according to the one-way encryption function to obtain the second child private data. The target child hash value and the second child private data are polynomially fitted using the Lagrange interpolation algorithm to obtain the second privacy computing model, which is then sent to the first data node.

[0109] At the first data node, the first business hash value is encrypted according to the second privacy computing model to obtain a reference business encryption value, the first business encryption value is encrypted according to the one-way encryption function to obtain an intermediate business encryption value, and the intermediate business encryption value is encrypted according to the one-way encryption function to obtain a second business encryption value. If the reference business encryption value and the second business encryption value are equal, the first business data is shared with the first child node and the second child node.

[0110] The business data sharing method of the embodiment of the present application can realize the sharing of multi-party business data by executing the multi-party privacy intersection task. And what is exchanged between any two data nodes is the privacy computing model, not the original business data, which can avoid the leakage of the intermediate intersection and ensure the security of business data sharing. Credit is a monetary loan service provided by financial institutions to borrowers. In the field of credit, based on data security and privacy considerations, the credit records between financial institutions are often not interoperable, resulting in financial institutions being unable to understand the credit status of borrowers and issuing loans to borrowers with lower credit, making the credit risk of financial institutions higher. The business data sharing method of the embodiment of the present application enables various financial institutions to share borrower credit information to assess the borrower's credit risk, while improving credit accuracy and efficiency, protecting the privacy of other business data of various financial institutions.

[0111] In the embodiment of the present application, the first data node informs the second data node of the shared business data obtained by the privacy intersection calculation. In order to prevent the first data node from ignoring the privacy intersection result and arbitrarily fabricating the intersection business data, the present invention introduces a data verification node, and verifies the privacy intersection result through the data verification node to improve the reliability, accuracy and security of business data sharing.

[0112] See also Figure 4 In some embodiments, the business data sharing system further includes a data verification node, the second data node includes a first subnode and a second subnode, the hash function includes a hash modulus, and the one-way encryption function includes an encryption exponent. After step S140, the business data sharing method may further include but is not limited to steps S410 to S450:

[0113] Step S410: at the first data node, construct a model according to a preset random integer value to obtain a reference privacy computing model, and send the reference privacy computing model to the first child node and the second child node respectively;

[0114] Step S420: at the first child node, obtain a hash modulus to obtain a first modulus, obtain an encryption exponent to obtain a first exponent, obtain a reference privacy computing model to obtain a first candidate model, obtain first business data to obtain first reference data, and send the first modulus, the first exponent, the first candidate model, the first reference data, and the first privacy computing model to a data verification node;

[0115] Step S430: at the second child node, obtain the hash modulus to obtain the second modulus, obtain the encryption exponent to obtain the second exponent, obtain the reference privacy computing model to obtain the second candidate model, obtain the first business data to obtain the second reference data, and send the second modulus, the second exponent, the second candidate model, the second reference data and the second privacy computing model to the data verification node;

[0116] Step S440: At the data verification node, a modulus consistency check is performed on the first modulus and the second modulus to obtain a reference modulus, an index consistency check is performed on the first index and the second index to obtain a reference index, a model consistency check is performed on the first candidate model and the second candidate model to obtain a benchmark calculation model, and a data consistency check is performed on the first reference data and the second reference data to obtain benchmark business data;

[0117] Step S450, at the data verification node, the accuracy of the benchmark business data is verified according to the reference modulus, the reference index, the benchmark calculation model, the first privacy calculation model and the second privacy calculation model to obtain the target business data.

[0118] In step S410 of some embodiments, at the first data node, a polynomial is constructed according to a preset random integer value to obtain a reference privacy computing model. The reference privacy computing model is expressed as:

[0119]

[0120] Where f(x) is the reference privacy computing model; x is the independent variable; r i is the i-th preset random integer value; |S R | is the number of preset random integer values, that is, the number of first business data; ∏ represents a continuous multiplication operation.

[0121] The first data node directly sends the reference privacy computing model to the first child node and the second child node. Alternatively, the first data node sends the reference privacy computing model to the first child node, the first child node saves the reference privacy computing model, and the first child node sends the reference privacy computing model to the second child node.

[0122] In step S420 of some embodiments, the first child node and the second child node both store a hash function and a one-way encryption function locally. In order to ensure the accuracy of the privacy intersection calculation result and thus improve the accuracy of business data sharing, the present application introduces a data verification node, which is used to determine whether the first child node and the second child node share the hash function and the one-way encryption function, and whether the business data intersection claimed by the first data node is consistent with the result obtained by the real privacy calculation.

[0123] Specifically, at the first child node, obtain the hash modulus of its local hash function to obtain the first modulus, obtain the encryption exponent of its local one-way encryption function to obtain the first exponent. At the first child node, receive the reference privacy computing model sent by the first data node to obtain the first candidate model, receive the first business data shared by the first data node to obtain the first reference data. Send the first modulus, the first exponent, the first candidate model, the first reference data and the first privacy computing model to the data verification node.

[0124] In step S430 of some embodiments, at the second child node, the hash modulus of its local hash function is obtained to obtain a second modulus, and the encryption exponent of its local one-way encryption function is obtained to obtain a second exponent. At the second child node, a reference privacy computing model sent from the first data node or the first child node is received to obtain a second candidate model, and the first business data shared by the first data node is received to obtain second reference data. The second modulus, the second exponent, the second candidate model, the second reference data, and the second privacy computing model are sent to the data verification node.

[0125] In step S440 of some embodiments, at the data verification node, a modulus consistency check is performed on the first modulus and the second modulus. If the first modulus and the second modulus are equal, it means that the first subnode and the second subnode share the same hash function, and the first modulus or the second modulus is used as the reference modulus. If the first modulus and the second modulus are not equal, data verification failure information is returned to the first subnode and the second subnode. The data verification failure information is used to indicate that the first business data shared by the first data node to the first subnode and the second subnode is inaccurate.

[0126] At the data verification node, an index consistency check is performed on the first index and the second index. If the first index and the second index are equal, it means that the first child node and the second child node share the same one-way encryption function, and the first index or the second index is used as the reference index; if the first index and the second index are not equal, data verification failure information is returned to the first child node and the second child node.

[0127] At the data verification node, in order to determine whether the reference privacy calculation models sent by the first data node to the first child node and the second child node are consistent, a model consistency check is performed on the first candidate model and the second candidate model. If the first candidate model and the second candidate model are the same, the first candidate model or the second candidate model is used as the benchmark calculation model; if the first candidate model and the second candidate model are different, data verification failure information is returned to the first child node and the second child node.

[0128] At the data verification node, in order to determine whether the business data shared by the first data node to the first child node and the second child node are consistent, a data consistency check is performed on the first reference data and the second reference data. If the first reference data and the second reference data are the same, the first reference data or the second reference data is used as the benchmark business data; if the first reference data and the second reference data are not the same, data verification failure information is returned to the first child node and the second child node.

[0129] In step S450 of some embodiments, at the data verification node, the accuracy of the benchmark business data is verified according to the reference modulus, the reference index, the benchmark calculation model, the first privacy calculation model, and the second privacy calculation model to determine whether the shared business data claimed by the first data node to the first sub-node and the second sub-node is correct. If the benchmark business data is correct, the benchmark business data is used as the target business data, and data verification success information is returned to the first sub-node and the second sub-node. The data verification success information is used to indicate that the first business data shared by the first data node to the first sub-node and the second sub-node is correct. If the benchmark business data is incorrect, data verification failure information is returned to the first sub-node and the second sub-node.

[0130] The above steps S410 to S450, through the data verification node, can enhance the trust of the first sub-node and the second sub-node in the first data node, ensure that the business data shared between different data nodes is accurate and reliable, and promote the intercommunication of financial data between different financial institutions.

[0131] See also Figure 5 In some embodiments, step S450 may include but is not limited to steps S510 to S530:

[0132] Step S510, constructing a target computing model according to the reference index, the first privacy computing model and the second privacy computing model;

[0133] Step S520, determining reference business data according to the reference modulus, the reference index, the benchmark calculation model, the target calculation model, and the second privacy calculation model;

[0134] Step S530: Perform consistency check on the benchmark business data and the reference business data to obtain target business data.

[0135] In step S510 of some embodiments, the square of the reference index is used as the target index, the power of the first privacy computing model is calculated according to the target index, and an equation model is constructed according to the power of the first privacy computing model and the second privacy computing model to obtain the target computing model. The target computing model is expressed as:

[0136]

[0137] Among them, f R is the first privacy computing model; x represents the independent variable; e is the reference index; e 2 is the target index; f1 is the second privacy calculation model.

[0138] In step S520 of some embodiments, a standard privacy intersection protocol is determined according to the reference modulus, the reference index, the benchmark calculation model, the target calculation model, and the second privacy calculation model, and the standard privacy intersection protocol is run to obtain reference business data. The reference business data is business data obtained by performing a standard privacy intersection calculation on the data verification node.

[0139] In step S530 of some embodiments, the reference business data is used as a standard to perform consistency check on the benchmark business data and the reference business data. If the benchmark business data and the reference business data are the same, the benchmark business data or the reference business data is used as the target business data; if the benchmark business data and the reference business data are not the same, data verification failure information is returned to the first subnode and the second subnode.

[0140] Through the above steps S510 to S530, it is possible to determine whether the business data shared by the first data node to the first sub-node and the second sub-node is correct, so as to improve the reliability of business data sharing.

[0141] See also Figure 6 In some embodiments, step S520 may include but is not limited to steps S610 to S640:

[0142] Step S610, solving the target calculation model according to the reference modulus to obtain first candidate business data;

[0143] Step S620, determining second candidate service data according to the reference modulus, the reference index and the benchmark calculation model;

[0144] Step S630, performing privacy calculation on the first candidate business data according to the second privacy calculation model to obtain third candidate business data;

[0145] Step S640: Screen the first candidate business data according to the second candidate business data and the third candidate business data to obtain reference business data.

[0146] In step S610 of some embodiments, the target computing model is solved to obtain business solution data, and the business solution data is modulo-calculated according to the reference modulus to obtain the remainder of the business solution data, and the remainder is used as the first candidate business data. The target computing model is a polynomial equation, and there may be multiple solutions. The business solution data is the solution set of the target computing model.

[0147] In step S620 of some embodiments, the second candidate business data is a solution set determined based on a reference modulus, a reference index and a benchmark calculation model.

[0148] In step S630 of some embodiments, the first candidate business data is input into the second privacy computing model for privacy computing, and the output value of the second privacy computing model is obtained, and the output value is used as the third candidate business data. Since the first candidate business data is a solution set composed of multiple solutions, the third candidate business data is also a solution set composed of multiple solutions.

[0149] If the first service data is represented by {t1,...,t i ,...,t n}, n represents the number of solutions, t i Denotes the ith solution. Perform privacy calculation on each solution in the first business data according to the second privacy calculation model to obtain third candidate business data.

[0150] In step S640 of some embodiments, the first candidate business data includes a first solution, and the third candidate business data includes a second solution, and the second solution is an output value of the privacy calculation model performing privacy calculation on the first solution. If the second solution belongs to the second candidate business data, the first solution is included in the preset set to obtain reference business data.

[0151] If the first service data is represented by {t1,...,t i ,...,t n}, n represents the number of first solutions, t i represents the i-th first solution. Traverse the first solution of the first business data and calculate the first solution t according to the second privacy calculation model i Perform privacy calculation and get the second solution f1(t i ). The second candidate service data is represented as a set S r , if the second solution f1(t i ) belongs to the set S r , then the first solution t i The preset set is included until the first business data is traversed and the preset set is used as reference business data.

[0152] Through the above steps S610 to S640, reference business data with standard privacy intersection can be obtained, so as to verify the accuracy of the benchmark business data according to the reference business data, thereby ensuring the accuracy of the data shared between data nodes.

[0153] See also Figure 7 In some embodiments, step S620 may include but is not limited to steps S710 to S730:

[0154] Step S710, solving the benchmark calculation model according to the reference modulus to obtain initial business data;

[0155] Step S720, constructing a reference encryption function according to the reference modulus and the reference exponent;

[0156] Step S730: encrypt the initial service data according to the reference encryption function to obtain second candidate service data.

[0157] In step S710 of some embodiments, a benchmark computing model is solved to obtain a model solution set, multiple solutions in the model solution set are respectively subjected to modulus calculations according to a reference modulus to obtain remainders of the multiple solutions, and the remainders of the multiple solutions are used as initial business data.

[0158] In step S720 of some embodiments, a reference encryption function is constructed according to the reference modulus and the reference exponent, and the reference encryption exponent is expressed as:

[0159] F e,N (x) = x e mod N,

[0160] Wherein, x represents the independent variable; e is the reference index; and N is the reference modulus.

[0161] In step S730 of some embodiments, the initial business data is encrypted according to the reference encryption function to obtain first encrypted business data, the first encrypted business data is encrypted according to the reference encryption function to obtain second encrypted business data, and the second encrypted business data is encrypted according to the reference encryption function to obtain second candidate business data. It should be noted that the number of times the reference encryption function encrypts the initial business data is equal to the number of data nodes participating in the sharing.

[0162] Through the above steps S710 to S730, the second candidate business data can be obtained, and based on the second candidate business data, it is determined whether the first business data claimed by the first data node to be shared with the first sub-node and the second sub-node is correct, thereby improving the accuracy and security of business data sharing.

[0163] See also Figure 8The embodiment of the present application also provides a business data sharing system, which can implement the above-mentioned business data sharing method, and the business data sharing system includes:

[0164] A first data node 810, where the first data node 810 stores first business data;

[0165] A second data node 820, where the second data node 820 stores second business data, and the first data node 810 and the second data node 820 share a hash function and a shared one-way encryption function;

[0166] The first data node 810 is used to perform hash calculation on the first business data according to the hash function to obtain a first business hash value, encrypt the preset random integer value according to the one-way encryption function to obtain a first business encrypted value; establish a mapping relationship from the first business hash value to the first business encrypted value to obtain a first privacy computing model, and send the first privacy computing model to the second data node 820;

[0167] The second data node 820 is used to construct a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and send the second privacy computing model to the first data node 810;

[0168] The first data node 810 is used to determine data access rights based on the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and share the first business data with the second data node 820 based on the data access rights; the data access rights are used to indicate that the first business data is shared data or private data.

[0169] The specific implementation of the business data sharing system is substantially the same as the specific implementation of the business data sharing method described above, and will not be described in detail herein.

[0170] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned business data sharing method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0171] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0172] The processor 910 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0173] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the business data sharing method of the embodiment of this application;

[0174] Input / output interface 930, used to implement information input and output;

[0175] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0176] bus 950 , which transmits information between the various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0177] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0178] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned business data sharing method is implemented.

[0179] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The business data sharing method, business data sharing system, electronic device and computer storage medium provided in the embodiments of the present application represent the data distribution characteristics of the first data node through a first privacy computing model, and use the first privacy computing model to determine the second privacy computing model, so as to use the second privacy computing model to capture the data correlation relationship between the first data node and the second data node, determine the common business data between the first data node and the second data node, and realize the sharing of common business data without leaking other business data, thereby ensuring the security of business data.

[0181] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0182] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0183] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0185] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0186] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0187] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0188] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0191] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A business data sharing method, characterized in that: Applied to a business data sharing system, the business data sharing system includes a first data node and a second data node, the first data node stores first business data, the second data node stores second business data, the first data node and the second data node share a hash function and a shared one-way encryption function, the method includes: At the first data node, performing hash calculation on the first service data according to the hash function to obtain a first service hash value, and encrypting a preset random integer value according to the one-way encryption function to obtain a first service encrypted value; At the first data node, a mapping relationship between the first business hash value and the first business encryption value is established to obtain a first privacy computing model, and the first privacy computing model is sent to the second data node; At the second data node, construct a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and send the second privacy computing model to the first data node; At the first data node, data access rights are determined according to the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and the first business data is shared with the second data node according to the data access rights; the data access rights are used to indicate whether the first business data is shared data or private data.

2. The method according to claim 1, characterized in that The constructing a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model includes: Performing hash calculation on the second service data according to the hash function to obtain a second service hash value; Performing privacy calculation on the second business hash value through the first privacy calculation model to obtain initial privacy data; Encrypting the initial private data according to the one-way encryption function to obtain candidate private data; A mapping relationship between the second business hash value and the candidate privacy data is established to obtain the second privacy calculation model.

3. The method according to claim 1, characterized in that The determining of data access rights according to the first business hash value, the one-way encryption function, the first business encryption value, and the second privacy computing model includes: Encrypting the first business hash value according to the second privacy computing model to obtain a reference business encrypted value; Encrypting the first service encryption value according to the one-way encryption function to obtain a second service encryption value; If the reference service encryption value and the second service encryption value are equal, then the data access permission indicates that the first service data is shared data; If the reference service encryption value and the second service encryption value are not equal, the data access permission indicates that the first service data is private data.

4. The method according to any one of claims 1 to 3, characterized in that: The business data sharing system further includes a data verification node, the second data node includes a first subnode and a second subnode, the hash function includes a hash modulus, the one-way encryption function includes an encryption exponent, and after sharing the first business data with the second data node according to the data access permission, the method further includes: At the first data node, construct a model according to the preset random integer value to obtain a reference privacy computing model, and send the reference privacy computing model to the first child node and the second child node respectively; At the first child node, the hash modulus is obtained to obtain a first modulus, the encryption exponent is obtained to obtain a first exponent, the reference privacy computing model is obtained to obtain a first candidate model, the first business data is obtained to obtain first reference data, and the first modulus, the first exponent, the first candidate model, the first reference data, and the first privacy computing model are sent to the data verification node; At the second child node, the hash modulus is obtained to obtain a second modulus, the encryption exponent is obtained to obtain a second exponent, the reference privacy computing model is obtained to obtain a second candidate model, the first business data is obtained to obtain second reference data, and the second modulus, the second exponent, the second candidate model, the second reference data and the second privacy computing model are sent to the data verification node; At the data verification node, performing a modulus consistency check on the first modulus and the second modulus to obtain a reference modulus, performing an exponent consistency check on the first index and the second index to obtain a reference index, performing a model consistency check on the first candidate model and the second candidate model to obtain a benchmark calculation model, and performing a data consistency check on the first reference data and the second reference data to obtain benchmark service data; At the data verification node, the accuracy of the benchmark business data is verified according to the reference modulus, the reference index, the benchmark calculation model, the first privacy calculation model and the second privacy calculation model to obtain the target business data.

5. The method according to claim 4, characterized in that The step of performing accuracy verification on the benchmark business data according to the reference modulus, the reference index, the benchmark calculation model, the first privacy calculation model, and the second privacy calculation model to obtain target business data includes: Constructing a target computing model according to the reference index, the first privacy computing model, and the second privacy computing model; Determining reference business data according to the reference modulus, the reference index, the benchmark computing model, the target computing model, and the second privacy computing model; A consistency check is performed on the benchmark business data and the reference business data to obtain the target business data.

6. The method according to claim 5, characterized in that The determining of reference business data according to the reference modulus, the reference index, the benchmark computing model, the target computing model, and the second privacy computing model includes: Solving the target computing model according to the reference modulus to obtain first candidate business data; Determining second candidate service data according to the reference modulus, the reference index and the benchmark calculation model; Performing privacy calculation on the first candidate business data according to the second privacy calculation model to obtain third candidate business data; The first candidate business data is screened according to the second candidate business data and the third candidate business data to obtain the reference business data.

7. The method according to claim 6, characterized in that The determining the second candidate service data according to the reference modulus, the reference index and the benchmark calculation model includes: Solving the benchmark calculation model according to the reference modulus to obtain initial business data; constructing a reference encryption function based on the reference modulus and the reference exponent; The initial service data is encrypted according to the reference encryption function to obtain the second candidate service data.

8. A business data sharing system, characterized in that: The business data sharing system comprises: A first data node, wherein the first data node stores first business data; A second data node, wherein the second data node stores second business data, and the first data node and the second data node share a hash function and a one-way encryption function; The first data node is used to perform a hash calculation on the first business data according to the hash function to obtain a first business hash value, and to encrypt a preset random integer value according to the one-way encryption function to obtain a first business encrypted value; establish a mapping relationship between the first business hash value and the first business encrypted value to obtain a first privacy computing model, and send the first privacy computing model to the second data node; The second data node is used to construct a model according to the second business data, the hash function, the one-way encryption function and the first privacy computing model to obtain a second privacy computing model, and send the second privacy computing model to the first data node; The first data node is used to determine the data access permission based on the first business hash value, the one-way encryption function, the first business encryption value and the second privacy computing model, and share the first business data with the second data node based on the data access permission; the data access permission is used to indicate that the first business data is shared data or private data.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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