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

Abstract

The embodiments of the present application provide a business data sharing method and system, an electronic device, and a storage medium, which belong to the field of financial technology. The method includes: at a first data node, performing a hash calculation on first business data according to a hash function to obtain a first business hash value, encrypting a random integer value according to a one-way encryption function to obtain a first business encryption value, establishing a mapping relationship from the first business hash value to the first business encryption value at the first data node to obtain a first privacy calculation model, constructing a model at a second 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 sharing the first business data with the second data node at 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, thereby ensuring the security of the business data while achieving business data sharing.

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 equipment and storage medium. Background Art

[0002] Due to concerns about data security and privacy, business data between financial institutions is often not interoperable. To improve business efficiency and quality, financial institutions need to share data resources and achieve interoperability while ensuring data security. For example, in the credit sector, to improve lending efficiency, financial institutions share borrowers' credit information to assess their credit risk. 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, electronic device and storage medium, aiming to achieve business data sharing while ensuring data security.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application provides a business data sharing method, which is 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, and 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:

[0005] At the first data node, performing a 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-preserving computing model, and the first privacy-preserving computing model is sent to the second data node;

[0007] At the second data node, construct a model based on the second business data, the hash function, the one-way encryption function, and the first privacy-preserving computing model to obtain a second privacy-preserving computing model, and send the second privacy-preserving computing model to the first data node;

[0008] At the first data node, data access rights are determined based on 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 based on 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 based on 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 a 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 using 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 private 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, 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 child node and a second child node, the hash function includes a hash modulus, and the one-way encryption function includes an encryption exponent. 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-preserving computing model, and send the reference privacy-preserving computing model to the first child node and the second child node respectively;

[0021] At the first child node, obtaining the hash modulus to obtain a first modulus, obtaining the encryption exponent to obtain a first exponent, obtaining the reference privacy computing model to obtain a first candidate model, obtaining the first business data to obtain first reference data, and sending 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;

[0022] At the second child node, obtaining the hash modulus to obtain a second modulus, obtaining the encryption exponent to obtain a second exponent, obtaining the reference privacy computing model to obtain a second candidate model, obtaining the first business data to obtain second reference data, and sending 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;

[0023] 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 exponent consistency check is performed on the first exponent and the second exponent 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 service data;

[0024] At the data verification node, the accuracy of the benchmark business data is verified based on 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, performing accuracy verification on the benchmark business data based on the reference modulus, the reference index, the benchmark computing model, the first privacy computing model, and the second privacy computing model to obtain 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 calculation model, the target calculation model, and the second privacy calculation 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 based on the reference modulus, the reference index, the baseline 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-mentioned purpose, a second aspect of an embodiment of the present application provides a business data sharing system, the business data sharing system comprising:

[0039] a first data node storing 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 shared one-way encryption function;

[0041] The first data node is configured to perform a hash calculation on the first business data according to the hash function to obtain a first business hash value, 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, obtain a first privacy computing model, and send the first privacy computing model to the second data node;

[0042] The second data node is configured to construct a model based on 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 whether the first business data is shared data or private data.

[0044] To achieve the above-mentioned purpose, the 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 the processor implements the method described in the first aspect when executing the computer program.

[0045] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0046] The business data sharing method, business data sharing system, electronic device, and computer-readable storage medium proposed in the embodiments of the present application perform a hash calculation on first business data at a first data node according to a hash function, converting the first business data into a hash value to obtain a first business hash value, thereby obfuscating the first business data and protecting the privacy of the first business data. A preset random integer value is encrypted using a one-way encryption function, ensuring the irreversibility of the preset random integer value, to obtain a first business encryption value. To securely 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 between the first business hash value and the first business encryption value is established to obtain a first privacy calculation model. 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 is effectively protected, the risk of data leakage is reduced, and the network resources required for data transmission are reduced. To capture and represent the data distribution characteristics of the business data on the second data node and establish a connection between the first business data and the second business data, a model is constructed at the second data node based on 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. 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, data access permissions are determined based on the first business hash value, the one-way encryption function, the first business encryption value, and the second privacy computing model. Based on the data access permissions, the first business data is determined to be shared by the first and second data nodes. The first business data is then shared with the second data node, enabling the sharing of shared data without leaking other business data, thereby ensuring the security of shared business data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of the business data sharing method provided by an embodiment of the present application;

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

[0049] Figure 3 yes Figure 1 Flowchart 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 Flowchart of step S450 in FIG.

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

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

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

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

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

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

[0059] Due to concerns about data security and privacy, business data between financial institutions is often not interoperable. To improve business efficiency and quality, financial institutions need to share data resources and achieve interoperability while ensuring data security. For example, in the credit sector, to improve lending efficiency, financial institutions share borrowers' credit information to assess their credit risk.

[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, which aim 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 a terminal, can be applied to a server side, or can be software running in a terminal or a 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 as a server cluster or distributed system composed of multiple physical servers, or 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, and the like. 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, and the like 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 via 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 embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's 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 such 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 This is an optional flowchart 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, and the second data node stores second business data. The first business data and the second business data are business data generated by the different financial institutions when providing business services. The business data can be transaction data, customer data, product data, etc. To enable sharing of business data between 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, performing a hash calculation on the first service data according to a hash function to obtain a first service hash value, and encrypting a preset random integer value according to a one-way encryption function to obtain a first service encrypted value;

[0067] Step S120: 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-preserving computing model, and the first privacy-preserving computing model is sent to the second data node;

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

[0069] In step S140, at the first data node, data access rights are determined based on 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 based on the data access rights; the data access rights are used to indicate whether 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. Without leaking other business data, the common business data is shared, thereby 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 arbitrary length to an output value of 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. The target key is obtained by negotiation between 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. Both the first and second parameters are even numbers. The largest of the first and second parameters is selected as the target parameter. The target parameter specifies the key length of the asymmetric key, such as 1024 bits or 2048 bits, and determines the security of the asymmetric key. The larger the target parameter, the longer the key length, the more difficult the asymmetric key is to crack, and the more secure the asymmetric key is. Two prime numbers with a length half that of the target parameter are randomly generated and multiplied to obtain the hash modulus. It should be noted that to ensure security, the difference between the two prime numbers should be sufficiently large. An encryption exponent is determined based on these two prime numbers. If the two prime numbers are represented as p and q, the encryption exponent is a number that is relatively prime to (p-1)*(q-1), where * represents a multiplication operation. The encryption exponent is an integer greater than 1 and less than (p-1)*(q-1). The data check node saves two prime numbers p and 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, a hash function is used to calculate the first business data to obtain a first business hash value. The first business hash value is the hash value of the first business data. The first business data may be an order for selling insurance products, an insurance claim record, or customer information. A one-way encryption function is determined based on the hash modulus and the encryption exponent. 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] A one-way encryption function is unidirectional and can generate an output based on a given input, a hash modulus, and an encryption exponent. The input cannot be reversed from the output. A residual class space is determined based on the hash modulus and the integer space. Integers are uniformly randomly drawn from the residual class space to generate corresponding preset random integer values ​​for each first service data item. The residual class space is the set of integers in the integer space that have the same remainder after dividing each integer by the hash modulus. The preset random integer value is input into the one-way encryption function for encryption calculation to obtain the first service encrypted 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 expressed as 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 used 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 dataset. The first business data and the first business encrypted value obtained by encryption calculation using a preset random integer value constitute a data pair. The first privacy calculation model is a polynomial fitted to all data pairs. The first privacy calculation 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 Lagrange 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 a hash function to obtain a second service hash value;

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

[0084] Step S230, encrypting the initial private data using 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 private 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 used by the first data node. The second business hash value is a 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 calculation model for privacy calculation, and the output value of the first privacy calculation model is used as the 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 private 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-preserving computing model can be obtained, so as to use the second privacy-preserving 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: encrypt the first service encrypted value according to a one-way encryption function to obtain a second service encrypted 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 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.

[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 is performed on the first business hash value through the second privacy computing model, 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, a one-way encryption calculation is performed on the first service encryption value according to the one-way encryption function to obtain a 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 through 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 child node and a second child node, the second business data includes the first sub-data and the second sub-data, the first sub-node stores the first sub-data, and the second sub-node stores the second sub-data. The first sub-node and the second sub-node 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, a hash function is used to hash the first service data to obtain a first service hash value. A preset random integer value is encrypted using a one-way encryption function to obtain a first service encrypted value. A Lagrange interpolation algorithm is used to perform a polynomial fit on the first service hash value and the first service encrypted value to obtain a first privacy-preserving computation model. The first privacy-preserving computation model is then sent to the first child node.

[0107] At the first child node, a hash function is used to calculate the first sub-data to obtain a second business hash value. A privacy calculation is performed on the second business hash value using the first privacy calculation model to obtain initial private data. The initial private data is encrypted using a one-way encryption function to obtain candidate private data. A Lagrange interpolation algorithm is used to perform polynomial fitting on the second business hash value and the candidate private data to obtain an intermediate privacy calculation model, which is then sent to the second child node.

[0108] At the second child node, a hash function is used to calculate the second sub-data, yielding a target sub-hash value. A privacy-preserving computation is then performed on the target sub-hash value using the intermediate privacy-preserving computation model, yielding the first sub-private data. The first sub-private data is encrypted using a one-way encryption function, yielding the second sub-private data. A Lagrange interpolation algorithm is used to perform a polynomial fit on the target sub-hash value and the second sub-private data, yielding a second privacy-preserving computation model. This second privacy-preserving computation model is then sent to the first data node.

[0109] At the first data node, the first service hash value is encrypted according to the second privacy computing model to obtain a reference service encrypted value. The first service encrypted value is then encrypted using a one-way encryption function to obtain an intermediate service encrypted value. The intermediate service encrypted value is then encrypted using the one-way encryption function to obtain a second service encrypted value. If the reference service encrypted value and the second service encrypted value are equal, the first service 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. Moreover, 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 of various financial institutions are often not interoperable, resulting in the financial institutions being unable to understand the credit status of the borrowers and issuing loans to borrowers with lower credit, which makes the credit risk of financial institutions higher. The business data sharing method of the embodiment of the present application can enable various financial institutions to share the credit information of borrowers to assess the credit risk of borrowers, while improving the accuracy and efficiency of credit, and protecting the privacy of other business data of various financial institutions.

[0111] In this embodiment, a first data node informs a second data node of the shared business data obtained by performing a private intersection calculation. To prevent the first data node from ignoring the private intersection result and arbitrarily fabricating the intersection business data, the present invention introduces a data verification node to verify the private intersection result, thereby improving 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 child node and a second child node, 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, a model is constructed based on a preset random integer value to obtain a reference privacy-preserving computing model, and the reference privacy-preserving computing model is sent 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-preserving computing model to obtain a first candidate model, obtain first business data to obtain first reference data, and send the first modulus, first exponent, first candidate model, first reference data, and first privacy-preserving computing model to a data verification node.

[0115] Step S430: At the second child node, obtain the hash modulus to obtain a second modulus, obtain the encryption exponent to obtain a second exponent, obtain the reference privacy computing model to obtain a second candidate model, obtain the first business data to obtain second reference data, and send the second modulus, second exponent, second candidate model, second reference data, and 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 exponent consistency check is performed on the first exponent and the second exponent to obtain a reference exponent, 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 based on a preset random integer value to obtain a reference privacy computing model. The reference privacy computing model is represented as:

[0119]

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

[0121] The first data node directly sends the reference privacy-preserving computing model to the first child node and the second child node. Alternatively, the first data node sends the reference privacy-preserving computing model to the first child node, which then stores the reference privacy-preserving computing model and sends it to the second child node.

[0122] In step S420 of some embodiments, both the first and second child nodes locally store a hash function and a one-way encryption function. To ensure the accuracy of the privacy intersection calculation results and thereby improve the accuracy of business data sharing, this application introduces a data verification node. The data verification node determines whether the first and second child nodes share the same hash function and one-way encryption function, and determines whether the business data intersection claimed by the first data node is consistent with the actual privacy calculation result.

[0123] Specifically, the first child node obtains the hash modulus of its local hash function to obtain a first modulus, and obtains the encryption exponent of its local one-way encryption function to obtain a first exponent. The first child node receives the reference privacy-preserving computing model sent by the first data node to obtain a first candidate model, and receives the first business data shared by the first data node to obtain first reference data. The first modulus, first exponent, first candidate model, first reference data, and first privacy-preserving computing model are sent to the data verification node.

[0124] In step S430 of some embodiments, the second child node obtains the hash modulus of its local hash function to obtain a second modulus, and obtains the encryption exponent of its local one-way encryption function to obtain a second exponent. The second child node receives a reference privacy-preserving computing model from the first data node or the first child node to obtain a second candidate model, and receives the first business data shared by the first data node to obtain second reference data. The second modulus, second exponent, second candidate model, second reference data, and second privacy-preserving 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 sub-node and the second sub-node 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 sub-node and the second sub-node. The data verification failure 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 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, a data verification failure message 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, the first candidate model and the second candidate model are subjected to model consistency verification. 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 baseline calculation model; if the first candidate model and the second candidate model are different, the 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 different, 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 based on the reference modulus, the reference exponent, 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 child node and the second child 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 child node and the second child node. The data verification success information is used to indicate that the first business data shared by the first data node with the first child node and the second child node is correct. If the benchmark business data is incorrect, data verification failure information is returned to the first child node and the second child node.

[0130] The above steps S410 to S450, through the data verification node, can enhance the trust of the first child node and the second child 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 , performing 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 calculation model is calculated based on the target index, and an equation model is constructed based on the power of the first privacy calculation model and the second privacy calculation model to obtain the target calculation model. The target calculation model is expressed as:

[0136]

[0137] Among them, f R is the first privacy calculation 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-preserving intersection protocol is determined based on the reference modulus, the reference index, the baseline calculation model, the target calculation model, and the second privacy-preserving calculation model. The standard privacy-preserving intersection protocol is executed to obtain reference business data. The reference business data is business data obtained by performing a standard privacy-preserving 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 verification 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 sub-node and the second sub-node.

[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 child node and the second child node is correct, thereby improving 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, a target computing model is solved to obtain business solution data. A modulo calculation is performed on the business solution data according to a reference modulus to obtain a remainder of the business solution data, which is used as the first candidate business data. The target computing model is a polynomial equation and may have 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 the reference modulus, the reference index and the benchmark calculation model.

[0148] In step S630 of some embodiments, the first candidate business data is input into the second privacy-preserving computation model for privacy-preserving computation, and an output value of the second privacy-preserving computation model is obtained, which is used as the third candidate business data. Because the first candidate business data is a solution set constructed from multiple solutions, the third candidate business data is also a solution set constructed from 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 i-th 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 some embodiments, in step S640, the first candidate business data includes a first solution, and the third candidate business data includes a second solution, where the second solution is the output of a privacy-preserving computation performed by the second privacy-preserving computation model 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 the 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 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, the reference business data of the standard privacy intersection can be obtained, so as to verify the accuracy of the benchmark business data based on 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 model solution is performed on the benchmark calculation model to obtain a model solution set, and modulo calculations are performed on multiple solutions in the model solution set according to a reference modulus to obtain remainders of the multiple solutions, which are used as initial business data.

[0158] In step S720 of some embodiments, a reference encryption function is constructed based on the reference modulus and the reference exponent. The reference encryption exponent is expressed as:

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

[0160] Where 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 service data is encrypted using a reference encryption function to obtain first encrypted service data, the first encrypted service data is encrypted using the reference encryption function to obtain second encrypted service data, and the second encrypted service data is encrypted using the reference encryption function to obtain second candidate service data. It should be noted that the number of times the reference encryption function encrypts the initial service 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 can be determined whether the first business data claimed by the first data node to be shared with the first child node and the second child node is correct, thereby improving the accuracy and security of business data sharing.

[0163] See also Figure 8The embodiment of the present application further provides a business data sharing system that can implement the above-mentioned business data sharing method. 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. 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 configured to perform a hash calculation on the first business data according to a hash function to obtain a first business hash value, encrypt a preset random integer value according to a 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, obtain a first privacy-preserving computing model, and send the first privacy-preserving computing model to the second data node 820.

[0167] The second data node 820 is configured to construct a model based on 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 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 820 based on the data access permission; the data access permission is used to indicate whether the first business data is shared data or private data.

[0169] The specific implementation of the business data sharing system is basically the same as the specific embodiment of the above-mentioned business data sharing method, and will not be repeated here.

[0170] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned service data sharing method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0171] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0172] The processor 910 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured 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 solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and is called by the processor 910 to execute the service data sharing method of the embodiments of this application.

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

[0175] Communication interface 940, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0176] bus 950 , which transmits information between 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 further 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 arranged 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 use a first privacy computing model to represent the data distribution characteristics of the first data node, 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, and determine the common business data between the first data node and the second data node. Without leaking other business data, the common business data is shared, thereby ensuring the security of the business data.

[0181] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this 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 this application are also applicable to similar technical problems.

[0182] Those skilled in the art will understand 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. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0184] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations 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 sequential order. 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 variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes 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 this 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 previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items 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 this 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 merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or 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, which is stored in a storage medium and includes multiple instructions for enabling 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: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0191] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope 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 a 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-preserving computing model, and the first privacy-preserving computing model is sent to the second data node; At the second data node, construct a model based on the second business data, the hash function, the one-way encryption function, and the first privacy-preserving computing model to obtain a second privacy-preserving computing model, and send the second privacy-preserving computing model to the first data node; At the first data node, data access rights are determined based on 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 based on 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 a 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 using 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 private 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, 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 child node and a second child node, the hash function includes a hash modulus, and the one-way encryption function includes an encryption exponent. 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-preserving computing model, and send the reference privacy-preserving computing model to the first child node and the second child node respectively; At the first child node, obtaining the hash modulus to obtain a first modulus, obtaining the encryption exponent to obtain a first exponent, obtaining the reference privacy computing model to obtain a first candidate model, obtaining the first business data to obtain first reference data, and sending 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; At the second child node, obtaining the hash modulus to obtain a second modulus, obtaining the encryption exponent to obtain a second exponent, obtaining the reference privacy computing model to obtain a second candidate model, obtaining the first business data to obtain second reference data, and sending 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; 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 exponent consistency check is performed on the first exponent and the second exponent 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 service data; At the data verification node, the accuracy of the benchmark business data is verified based on 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 calculation model, the target calculation model, and the second privacy calculation 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 calculation model, the target calculation model, and the second privacy calculation 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, according to the reference modulus, the reference index, and the benchmark calculation model, second candidate service data 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 includes: a first data node storing 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 shared one-way encryption function; The first data node is configured to perform a hash calculation on the first business data according to the hash function to obtain a first business hash value, 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, obtain a first privacy computing model, and send the first privacy computing model to the second data node; The second data node is configured to construct a model based on 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 whether the first business data is shared data or private data.

9. An electronic device, characterized in that: The electronic device includes 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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