CN116823417A - Block chain supply chain financial risk control method, system, storage medium and electronic terminal based on federal learning - Google Patents
Block chain supply chain financial risk control method, system, storage medium and electronic terminal based on federal learning Download PDFInfo
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Abstract
The invention relates to a block chain supply chain financial risk control method and system based on federal learning, a storage medium and an electronic terminal, and belongs to the technical field of block chains. The method comprises the following steps: s1, connecting all the participants together based on a blockchain to form a network, wherein all the participants in a supply chain become single member nodes in the network; s2, acquiring related information of each participant of a supply chain, and uploading the related information; s3, constructing a supply chain information transmission system through a block chain based on the information of each participant of the supply chain of the uplink; s4, a data classification module is pre-established by a data owner, and data inspection is carried out on the common public data and the privacy protection data of the uplink again; s5, the privacy protection data are shared after federal learning training and homomorphic encryption algorithm. The invention ensures the information privacy of the transaction party behind the data while realizing information sharing, reduces the maintenance cost of the information and improves the service efficiency.
Description
Technical Field
The invention relates to a block chain supply chain financial risk control method and system based on federal learning, a storage medium and an electronic terminal, and belongs to the technical field of block chains.
Background
Supply chain finance can play a role in solving the problem of difficult financing of small and medium enterprises because the requirement on borrowing qualification of the small and medium enterprises is reduced under the conditions of credit sharing and risk sharing of core enterprises.
However, in the actual development process of the financial business of the supply chain, the phenomena of information hiding, false information uploading and unpublished transparency of the information exist in the supply chain, and on one hand, the phenomena can lead each participant to be incapable of accurately knowing the transaction information and the process, increase the coordination difficulty, not deal with the problems in time, reduce the operation efficiency of the supply chain and lead the financing risk to be uncontrollable. On the other hand, as the information is maliciously hidden, tampered and leaked, financial institutions such as banks and the like cannot acquire important information of financing agents at reasonable cost, the enthusiasm of supply chain financial audiences is influenced, meanwhile, the risk that main body data of each participant are leaked is possible, and the development of main bodies of each participant is restrained, so that the development of supply chain finances is influenced.
The information among enterprises is opaque, so that the risk of tampering exists in the information, and the enterprises are difficult to trust each other; the data is completely transparent and privacy protection is difficult to complete, and if financial risk control is to be realized, on one hand, enterprises of all parties need to upload data to the system to the greatest extent so as to facilitate global management and control of core enterprises, and on the other hand, business confidentiality of the enterprises of all parties needs to be protected.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a block chain supply chain financial risk control method, a system, a storage medium and an electronic terminal based on federal learning, wherein each participant main body of a supply chain encrypts and uploads respective characteristic data to a block chain platform, and the uploaded characteristic data is subjected to data classification and is divided into privacy protection data and common public data; for privacy protection data, a transverse federal learning technology in machine learning is used, model training is carried out according to a federal learning model defined in a blockchain, and a homomorphic encryption algorithm Gentry algorithm is used for sharing. The method ensures the information privacy of the transaction party behind the data while realizing information sharing, so that the information flow formed by the main bodies of all the participants of the supply chain is smoothly transmitted in the network and is not transparent and difficult to tamper and leak, the information security is ensured, the high efficiency and low cost of system operation are ensured, the maintenance cost of the information is reduced, the service efficiency is improved, and the financial risk of the supply chain is also reduced.
The technical scheme of the invention is as follows:
a block chain supply chain financial risk control method based on federal learning comprises the following steps:
s1, connecting a financial institution, a core enterprise, a provider group, a logistics enterprise and a dealer group together based on a blockchain to form a network, wherein each participant in the supply chain becomes a single member node in the network;
s2, data uplink operation, namely acquiring related information of each participant of a supply chain, uplink the related information, and storing the related information in corresponding participant nodes of a block chain;
s3, constructing a supply chain information transmission system through a block chain based on information of each participant of the supply chain of the upper chain;
s4, a data classification module is pre-established by the data owner, data inspection is carried out on the common public data and the privacy protection data in the uplink again, whether the common public data and the privacy protection data are consistent with the classification of the data owner or not is judged, if so, the next step is carried out, if not, the data information is returned to the data owner, and whether the conventional data classification is continuously executed is determined by the data owner;
s5, the privacy protection data are shared after federal learning training and homomorphic encryption algorithm.
According to the present invention, in step S2, the associated data and the core information of each party in the supply chain are obtained, and the associated data and the core information are subjected to the uplink operation, which specifically includes:
acquiring fund information, sponsoring policy and credit evaluation standard of a financial institution (such as a bank), and lending information, repayment condition and credit information of a core enterprise, a supplier, a logistics enterprise and a distributor, and uploading the fund information, the sponsor policy and the credit evaluation standard to a participant node corresponding to the financial institution;
acquiring the operation information, financial status, development planning, fund support requirements and cooperative credit information of suppliers, logistics enterprises and distributors of the core enterprise, and uploading the operation information, the financial status, the development planning, the fund support requirements and the cooperative credit information to participant nodes corresponding to the core enterprise;
obtaining market demand, product self information, production environment information, supply quantity of products, product price, external evaluation data and financing demand and scheme of products of a provider, and linking the market demand, the product self information, the production environment information, the supply quantity of the products, the product price, the external evaluation data and the financing demand and scheme to a participant node corresponding to the provider;
obtaining logistics cost, delivery timing rate, satisfaction rate, transportation information, transaction information, logistics evaluation information and financing requirements and schemes of products of a logistics enterprise, and uploading the products to corresponding participant nodes of the logistics enterprise;
and obtaining sales data of products of the dealer, wherein the sales data comprise the price of the products, the selling price, the selling quantity, after-sales product refund information, evaluation information, financing requirements and schemes, and the sales data are uploaded to the corresponding participant nodes of the dealer.
According to the preferred embodiment of the present invention, in step S4, the step of re-checking the data of the uplink data specifically includes checking each group of public data according to the first classification of the data owner, judging whether the private protection data can be decrypted by the public data, if so, converting the associated public data into the private protection data, re-checking, and re-feeding the checking information back to the data owner, and determining the classification of the public data and the private protection data again independently by the data owner; if not, the uplink data classification condition is consistent with the classification condition autonomously determined by the data owner, and the next step is carried out.
For example, when the dealer data owner sets the product price and the selling number as privacy protection data and sets the product selling price and the total profit rate as publicable data, the data inspection is performed after the data inspection is performed, the total profit rate of the product in the publicable data obtained by the algorithm inspection is calculated by the product price, the selling number and the selling price of the publicable data, so that the total profit rate of the product is judged to contain the product price and the selling number of the privacy protection data, and the data is triggered to be fed back to the dealer data owner to make the re-judgment, and the dealer data owner can select whether to convert the total profit rate of the product from the publicable data into the privacy protection data and re-perform the data uplink operation.
According to a preferred embodiment of the invention, step S5 comprises the following steps:
s51, a party A sends a data sharing request to a party B, and after receiving the sharing request, the party B utilizes a federal learning model to train data M B (i.e., privacy-preserving data of party B) local training, including initializing training data by a local server and training result N B Encryption uploading to the block chain information storage node X corresponding to the party B B ；
S52, because of user privacy and data security reasons, the party A and the party B cannot directly exchange data, in order to ensure the data confidentiality in the training process, a coordinator C of a third party is added, the party C generates a public key and a private key by using a homomorphic encryption algorithm, the public key is sent to the party B, and the party B adopts the homomorphic public key sent by the party C to train the result N B Encryption to obtain an encryption training result Q B ；
S53, party B encrypts the training result Q B And sending the party A, wherein the party A decrypts by using the private key sent by the party C to obtain the needed data information.
According to the present invention, the federal learning training model used in step S51 is a lateral federal learning training model, and the homomorphic encryption algorithm used in step S52 is a Gentry algorithm.
The block chain supply chain financial risk control system based on federal learning comprises:
the data acquisition module is used for acquiring uplink data;
the information transmission module is used for constructing a supply chain information transmission system;
the data classification checking module is used for establishing data classification for the data owner and carrying out data checking on the common public data and the privacy protection data of the uplink;
and the federal learning training and encrypting module is used for performing federal learning training and homomorphic encryption sharing on the privacy protection data.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the blockchain supply chain financial risk control method based on federal learning as described above.
The invention also provides an electronic terminal, comprising: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the terminal executes the block chain supply chain financial risk control method based on federal learning.
The invention has the beneficial effects that:
financial transactions often involve multiple parties such as banks, enterprises, third party platforms, etc., data stored by each institution has a certain overlap and redundancy in user dimension and feature dimension, and because it is difficult to promote information sharing among institutions due to the business confidentiality involved, machine learning models built based on single institution data are often incomplete. The method has the advantages that the 'federation learning + blockchain' can fully integrate the advantages of the blockchain and the federation learning, and under the premise that original data circulation is not needed, original data can not be out of the local place only through intermediate parameters of an interaction model, so that data sharing is realized on the basis of guaranteeing data privacy safety and legal compliance, and the method is used together, balance of data privacy protection and data sharing analysis is realized, and the problems of data tamper resistance and leakage resistance in supply chain finance and the problem of data safety under a big data background are solved.
Drawings
Fig. 1 is a diagram of the relationship of supply chain participants in embodiment 1 of the invention.
Fig. 2 is a flow chart of embodiment 1 of the present invention.
Fig. 3 is a schematic diagram of the system configuration of embodiment 2 of the present invention.
Detailed Description
The invention will now be further illustrated by way of example, but not by way of limitation, with reference to the accompanying drawings.
Example 1:
a block chain supply chain financial risk control method based on federal learning, as shown in figure 1, is applied to a steel trade enterprise supply chain financial system, and comprises the following steps:
s1, connecting a financial institution, a core enterprise, a provider group, a logistics enterprise and a dealer group together based on a blockchain to form a network, wherein each participant in the supply chain becomes a single member node in the network;
s2, data uplink operation, namely acquiring related information of each participant of a supply chain, performing uplink operation on the related information, and storing the related information in corresponding participant nodes of a block chain;
the obtaining relevant information of each party of the supply chain specifically comprises the following steps:
acquiring fund information, sponsoring policy and credit evaluation standard of a bank, and performing a uplink operation;
acquiring the operation information, financial status, development planning, fund support requirements of a core enterprise, cooperation with suppliers, logistics enterprises and distributors and credit information thereof, and performing uplink operation;
acquiring market demand, self information, production environment information, supply quantity, price, external evaluation data, financing demand and scheme of iron ore suppliers on the iron ore, and performing uplink operation;
obtaining logistics cost, delivery timing rate, satisfaction rate, transportation information, transaction information, logistics evaluation information and financing requirements and schemes of products of a logistics enterprise, and performing uplink operation;
obtaining sales data of products of a building dealer, wherein the sales data comprise price, selling quantity, after-sales product refund information, evaluation information, financing requirements and schemes of the products, and performing uplink operation;
s3, constructing a supply chain information transmission system through a block chain based on information of each participant of the supply chain of the upper chain;
s4, a data classification module is pre-established by the data owner, the data inspection is carried out on the common public data and the privacy protection data of the uplink again, whether the common public data and the privacy protection data are consistent with the classification of the data owner is judged, if so, the next step is carried out, if not, the data information is returned to the data owner, whether the conventional data classification is continuously executed is determined by the data owner,
the step of carrying out data inspection again on the data of the uplink data specifically comprises the steps of inspecting each group of public data according to the first classification condition of a data owner, judging whether the private protection data can be decrypted by the public data, if so, converting the associated public data into the private protection data, carrying out inspection again, feeding inspection information back to the data owner again, and independently determining the classification conditions of the public data and the private protection data again by the data owner; if not, the uplink data classification condition is consistent with the classification condition autonomously determined by the data owner, and the next step is carried out. Classification testing may be performed specifically by the softmax algorithm, logistic (Logistic) and full connectivity layer
S5, the privacy protection data are shared through federal learning model training and homomorphic encryption algorithm;
in this embodiment, at least two participants are involved, any two of the supply chain participants, denoted as A and B, which are stored in the information storage nodes of the blockchain, denoted asX A And X B . Each participant node contains own source information data which are respectively marked as first training data M A And second training data M B 。
In this embodiment, for reasons of user privacy and data security, party a and party B cannot directly exchange data, and in order to ensure data confidentiality in the training process, a third party coordinator C is added. It is mainly used to help participants to perform secure federal learning, and party C, independent of each participant, will collect intermediate results and forward the results to each participant. The information received by party C from the parties is process encrypted so that the original source information data of the parties are not exposed to each other and the parties only receive model parameters related to the features they possess.
The specific steps of the step S5 are as follows:
s51, a party A sends a data sharing request to a party B, and after receiving the sharing request, the party B trains a model to train data M by utilizing the existing transverse federal learning mode B Performing local training, wherein the local training comprises initializing training data by a local server, and setting training result N B Encryption upload to blockchain information storage node X B 。
S52, the party C generates a public key and a private key by using a Gentry algorithm, and sends the public key to the party B, and the party B adopts the homomorphic public key sent by the party C to train the result N B Encryption to obtain an encryption training result Q B 。
S53, party B encrypts the training result Q B Transmitting a party A, wherein the party A decrypts by using a private key transmitted by a party C to obtain the needed data information;
by the method, on the premise of ensuring data interaction and data sharing, original source information data of all the participants can be effectively protected, on one hand, privacy information of all the participants of a supply chain cannot be leaked randomly through encryption, on the other hand, authenticity of the data cannot be tampered randomly through blockchain storage, and not only is data safety improved, but also data accuracy and reliability are ensured.
Example 2:
a blockchain supply chain financial risk control system based on federal learning applied to embodiment 1, comprising:
the data acquisition module is used for acquiring uplink data;
the information transmission module is used for constructing a supply chain information transmission system;
the data classification checking module is used for establishing data classification for the data owner and carrying out data checking on the common public data and the privacy protection data of the uplink;
and the federal learning training and encrypting module is used for performing federal learning training and homomorphic encryption sharing on the privacy protection data.
Example 3:
a computer readable storage medium applied to embodiment 1, having stored thereon a computer program which when executed by a processor implements the federal learning-based blockchain supply chain financial risk control method of embodiment 1.
Example 4:
an electronic terminal, comprising: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, so that the terminal performs the federal learning-based blockchain supply chain financial risk control method according to embodiment 1.
In addition, the specific embodiments described in the present specification may differ in terms of parts, shapes of components, names, and the like. All equivalent or simple changes of the structure, characteristics and principle according to the inventive concept are included in the protection scope of the present invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions in a similar manner without departing from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. A blockchain supply chain financial risk control method based on federal learning, comprising the steps of:
s1, connecting a financial institution, a core enterprise, a provider group, a logistics enterprise and a dealer group together based on a blockchain to form a network, wherein each participant in the supply chain becomes a single member node in the network;
s2, data uplink operation, namely acquiring related information of each participant of a supply chain, uplink the related information, and storing the related information in corresponding participant nodes of a block chain;
s3, constructing a supply chain information transmission system through a block chain based on information of each participant of the supply chain of the upper chain;
s4, a data classification module is pre-established by the data owner, data inspection is carried out on the common public data and the privacy protection data in the uplink again, whether the common public data and the privacy protection data are consistent with the classification of the data owner or not is judged, if so, the next step is carried out, if not, the data information is returned to the data owner, and whether the conventional data classification is continuously executed is determined by the data owner;
s5, the privacy protection data are shared after federal learning training and homomorphic encryption algorithm.
2. The federally learned based blockchain supply chain financial risk control method according to claim 1, wherein the step S2, the uplink operation, specifically, includes:
acquiring fund information, sponsoring policy and credit evaluation standard of a financial institution, and lending information, repayment condition and credit information of a core enterprise, a supplier, a logistics enterprise and a distributor, and uploading the fund information, the sponsor policy and the credit evaluation standard to a participant node corresponding to the financial institution;
acquiring the operation information, financial status, development planning, fund support requirements and cooperative credit information of suppliers, logistics enterprises and distributors of the core enterprise, and uploading the operation information, the financial status, the development planning, the fund support requirements and the cooperative credit information to participant nodes corresponding to the core enterprise;
obtaining market demand, product self information, production environment information, supply quantity of products, product price, external evaluation data and financing demand and scheme of products of a provider, and linking the market demand, the product self information, the production environment information, the supply quantity of the products, the product price, the external evaluation data and the financing demand and scheme to a participant node corresponding to the provider;
obtaining logistics cost, delivery timing rate, satisfaction rate, transportation information, transaction information, logistics evaluation information and financing requirements and schemes of products of a logistics enterprise, and uploading the products to corresponding participant nodes of the logistics enterprise;
sales data of products of the dealer and financing requirements and schemes are obtained and are uploaded to the corresponding participant nodes of the dealer.
3. The method for controlling financial risk of a blockchain supply chain based on federal learning according to claim 1, wherein in step S4, the data of the uplink data is checked again, specifically, each group of publicly available data is checked according to the first classification of the data owner, whether the privacy-preserving data can be decrypted by the publicly available data is judged, if so, the associated publicly available data is converted into the privacy-preserving data, the checking is performed again, the checking information is fed back to the data owner again, and the classification of the publicly available data and the privacy-preserving data is determined again independently by the data owner; if not, the uplink data classification condition is consistent with the classification condition autonomously determined by the data owner, and the next step is carried out.
4. The federally learned based blockchain supply chain financial risk control method according to claim 1, wherein step S5 specifically comprises the steps of:
s51, a party A sends a data sharing request to a party B, and after receiving the sharing request, the party B utilizes a transverse federal learning model to train data M B Performing local training and collecting training result N B Encryption uploading to the block chain information storage node X corresponding to the party B B ；
S52, due to user privacy and data securityFor all reasons, the party A and the party B cannot directly exchange data, a coordinator C of a third party is added to ensure the confidentiality of the data in the training process, the party C generates a public key and a private key by using a homomorphic encryption algorithm and sends the public key to the party B, and the party B adopts the homomorphic public key sent by the party C to train the result N B Encryption to obtain an encryption training result Q B ；
S53, party B encrypts the training result Q B And sending the party A, wherein the party A decrypts by using the private key sent by the party C to obtain the needed data information.
5. The federal learning-based blockchain supply chain financial risk control method according to claim 4, wherein the federal learning training model used in step S51 is a lateral federal learning mode training model, and the homomorphic encryption algorithm used in step S52 is a Gentry algorithm.
6. A blockchain supply chain financial risk control system based on federal learning, comprising:
the data acquisition module is used for acquiring uplink data;
the information transmission module is used for constructing a supply chain information transmission system;
the data classification checking module is used for establishing data classification for the data owner and carrying out data checking on the common public data and the privacy protection data of the uplink;
and the federal learning training and encrypting module is used for performing federal learning training and homomorphic encryption sharing on the privacy protection data.
7. A computer readable storage medium, characterized in that the storage medium has stored thereon a computer program which when executed implements the steps of the federally learning based blockchain supply chain financial risk control method of any of the preceding claims 1-5.
8. An electronic terminal, comprising: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory to enable an electronic terminal to execute the steps of the federal learning-based blockchain supply chain financial risk control method according to any one of claims 1 to 5.
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