CN114491636A - Data use result transaction method based on scene - Google Patents

Data use result transaction method based on scene Download PDF

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CN114491636A
CN114491636A CN202210108820.7A CN202210108820A CN114491636A CN 114491636 A CN114491636 A CN 114491636A CN 202210108820 A CN202210108820 A CN 202210108820A CN 114491636 A CN114491636 A CN 114491636A
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data
transaction
buyer
seller
platform
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蒋炜
王志远
沈浙
郑志强
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Shanghai Jiaotong University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6272—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database by registering files or documents with a third party
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0605—Supply or demand aggregation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0611—Request for offers or quotes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0613—Third-party assisted
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The invention discloses a transaction method of a data use result based on a scene, which is applied to a data transaction platform and comprises the following steps: establishing data use logic and a data use scene; at least one data seller, which is suitable for a user who owns data ownership and sells data; at least one data buyer, which is suitable for the user who owns the use logic and the use scene, and the buyer purchases data; the data transaction platform is used for protecting the privacy of both parties, calculating the data use result, estimating the data value and guaranteeing the transaction process; the data transaction platform utilizes prior information provided by the data buyer to screen data meeting requirements in a data pool of the data seller. The data demanders of the present invention can agree on the value of the data. For convenience, it is assumed that the data exchange center and the various nodes therein can be trusted with the support of blockchain technology. To ensure the security of the data, the data will be homomorphically encrypted before transmission.

Description

Data use result transaction method based on scene
Technical Field
The invention relates to the technical field of data transaction, in particular to a transaction method of a data use result based on a scene.
Background
The fire heat of big data promotes the development of wide data transaction activities in various countries and various industries. The progress of information technology makes more and more data content and forms available for transaction, and the use scenes of the data are continuously expanded and personalized. The data is used as the basic production data of the future digital economic development, and the estimation and pricing of the data are the core problems. In order to solve the above information asymmetry problem, pricing of data depends on data of a data seller and a usage scenario (e.g., a model, an algorithm, etc.) of a data buyer, so that a desired value of the data is comprehensively and stereoscopically evaluated.
To achieve the above objective, the conventional centralized data transaction platform has the following disadvantages:
1) data pricing issues. There are many different data valuation pricing models in the existing data transaction, and the common data pricing models include six types, namely fixed charge, charge-by-number, charge-by-query, charge-by-time, buyer pricing and seller pricing. However, pricing by the amount of data or time of use alone does not reflect the benefit of the data to its customers, and the benefits of the same data in different models may be far from each other. The buyer pricing and the seller pricing are from the perspective of the pricing party, and the pricing is often subjective. Thus, traditional data pricing approaches result in a lack of consensus on the value of the data for buyers and sellers of the data.
2) Data and model privacy issues. During the course of data transactions, both buyers and sellers of data want to know how much value the data may generate after a transaction. However, the seller of the data does not want to leak the data during the transaction, and at the same time, the buyer of the data does not want to publish its own application scenario to the buyer, thereby creating a dilemma. In addition, principals in data transactions are concerned about the potential risk of data or models being stolen by the platform.
3) Centralizing platform trust issues. To realize data valuation and pricing based on use value, a data transaction platform of a third party needs to deal with various challenges. Firstly, the problem of data leakage is that if data is delivered to a data demander in the form of original data after transaction is completed, data security becomes uncontrollable, and the process of data sharing also introduces a data transaction platform so that the channel of data leakage becomes more. Secondly, a usage scenario (model) to the data demander needs to be used in the usage-based data valuation process, and the information may be business confidentiality of the data demander, so that specific technologies and mechanisms are needed to guarantee confidentiality of the data demander model. In addition, the new evaluation method needs to prevent the members (including the data trading center) participating in data sharing from communicating with each other, for example, the data sharing party can exaggerate the value of the data by cooperating with the data trading center, or conversely, the data demanding party and the data trading center can communicate with each other to hide the real value of the data, thereby inducing arbitrage.
With the increasing convergence and fusion of various information technologies and human production and life, the explosive growth and massive aggregation of global data and knowledge, data information becomes the most basic raw material of the information society. Only if the infrastructure of data transaction is created and data resources are used, the future development opportunity can be created for the Chinese digital economy.
Disclosure of Invention
The technical problem solved by the invention is as follows: in the face of the problems that data value is difficult to estimate, uncertainty of data value in use, data privacy protection and the like, how to design a data transaction method and a non-central platform architecture can fairly predict the data value before transaction, the execution terms are agreed in advance by two transaction parties in transaction, the data value can be redistributed after transaction, and the data transaction relates to the whole life cycle of the data transaction.
Aiming at the existing problems, the invention provides a scene-based data use result transaction method, which adopts a usage-based data value evaluation mechanism, and transaction objects are results obtained by using data by a buyer in a specific scene. The transaction flow gives consideration to data ownership tracking (in advance), data valuation and pricing (in advance), and extra revenue loss generated by data in the actual use scene of a buyer is redistributed (in the future), so that the full-life-cycle management of data transaction is realized. And the trust problem between the participants is maintained by punishing the violation behavior through the HashExpress certificate. The invention adopts the encryption algorithm and the transaction flow design to realize 'available and invisible' of data to a certain extent. The invention designs a non-centralized structural design to prevent the acts of collusion and the like of transaction participants including a transaction platform.
The invention is realized by the following technical scheme:
a transaction method of data use results based on scenes is applied to a data transaction platform and comprises the following steps:
establishing data use logic and a data use scene;
at least one data seller, which is suitable for a user who owns data ownership and sells data;
at least one data buyer, which is suitable for the user who owns the use logic and the use scene, and the buyer purchases data;
the data transaction platform is suitable for matching the buyer and the seller, protecting the privacy of both parties, calculating a data use result, estimating a data value and guaranteeing a transaction flow;
the data transaction platform screens data meeting requirements in a data pool of a data seller by using prior information provided by a data buyer, and the data buyer carries out prior judgment on statistical characteristics of the data;
the data transaction platform calculates the use result of the data by utilizing the seller data and the data use logic, calculates the use value of the data by utilizing the use result and the use scene, and assists data pricing;
carrying out full life cycle tracking and management on data use;
and the data transaction platform automatically settles the data transaction according to the prior agreement by utilizing the data full-life tracking result.
As a preferred embodiment, the data includes digital features and information, including numbers, text, graphs, tables, audio, video forms, owned by, or generated and observed by individuals, businesses, and other organizations;
the data use logic is logic for the data buyer to use the data to obtain a target result, and an operation result obtained by using the logic and the data comprises parameters in an algorithm and decisions in a model, wherein the data use logic is usually represented by a set of finite regular computer logic code sets;
the scenario includes economic benefits obtained by a data buyer through purchasing data, thereby improving algorithm results and optimizing model decisions, and is represented by a set of finite regular computer logic code sets.
As a preferred embodiment, the data trading platform measures data usage values using the usage logic and the usage scenario, including:
the data transaction platform estimates and records performance benefits or expected performance benefits of a data buyer when the data use result is not adopted in a given data use scene;
the data transaction platform calculates and records the performance benefit or expected performance benefit of a data buyer when adopting a data use result in a given data use scene;
and the data transaction platform is used as the use value of the data in the scene based on the benefit change of whether the data use result is adopted or not, and the use value is the basis of data pricing and subsequent value distribution.
As a preferred embodiment, tracking data life cycle usage and data usage value, and automatically settling data transactions according to a prior appointment, includes:
when the data transaction platform receives a data transaction request, the data transaction platform requests an authentication mechanism to perform identity authentication, adds an authentication result returned by the authentication mechanism to a digital signature of the data transaction platform to form a digital certificate and writes the digital certificate into a block chain system, meanwhile, the data transaction platform judges whether the data belongs to a data seller according to data watermarks or other technical information, if the data seller meets the conditions, the data seller inputs the encrypted data into a format, the data uploading platform specifies an external storage interplanetary file system, and continues to execute a data transaction process, and if the data transaction platform does not receive the data transaction request, the data transaction platform terminates the data transaction;
the data transaction platform stores the data use logic and the use scene and writes the data use logic and the use scene into a block chain system, judges whether the data use logic and the use scene provided by the data buyer are true or not according to the historical record, if the conditions are met, the data buyer provides the fuzzified data use logic, the data use scene, encrypted parameters and prior information, the uploading platform appoints an external storage interplanetary file system IPFS, the hashed information is uploaded to the block chain to be stored as the proof, the data transaction process is continuously executed, and if the data transaction process is not ended, the data transaction is ended;
the data transaction platform tracks the whole life cycle of data transaction (the use scene condition of a data buyer before transaction, the use scene condition after using data in transaction, the actually generated income of the data value after transaction and the like), calculates and records the change of the use value of the data, writes the information of the data buyer and the data seller, various transaction information and use process information into a block chain system, and prevents algorithm data from being falsified;
the platform calculates expected gain or loss of each data source for a data buyer in a specific scene by using a plurality of methods of a leave-one-out method and a Shapril value method based on the full life cycle tracking record of data transaction, redistributes the data value according to the contribution size and the prior agreement, and dynamically adjusts the value of the data by using a standard fund form.
As a preferred embodiment, the trading method takes the use value of data as a pricing basis and the contribution size of the data as a value allocation basis on the premise of protecting the privacy of both parties of the trading, and the trading method includes the following steps:
s1, submitting a data transaction intention application to the data trading bidirectional trading center, uploading encrypted data by a data seller, uploading data use logic, use scenes and prior information to a trading platform by a data buyer;
the S2 platform takes expected use value generated after the data is combined with the buyer scene as the basis of buying and selling matching and data evaluation, and gives comprehensive suggested pricing in combination with the data market condition;
s3, the data buyer and the data seller determine the data transaction rule, determine the logic of the verification program, generate an intelligent contract, achieve consensus through the consensus network, form blocks, add into the block chain platform, and the data seller authorizes the transaction platform to transmit the data use result to the data buyer;
s4, the data use state is recorded and uploaded to the block in real time, the increment or loss generated after data transaction is directly distributed through an intelligent contract, and the platform performs rear accountability on the participators under abnormal conditions;
s5, constructing a decentralized data transaction platform architecture;
s6 designs a data secret mechanism and a data safety transaction mechanism.
As a preferred embodiment, the S2 platform estimates the data based on the expected use value generated by combining the data with the buyer scenario as a match for buying and selling:
s2-1, the data transaction platform intelligently matches buyers and sellers by using an intelligent matching engine based on data prior use value, specifically, the data transaction platform estimates the prior use result of the data according to the prior information and use logic provided by the data buyer, then determines the prior use value of the data according to the use scene, and matches suitable data buyers and data sellers from a buyer pool and a seller pool;
s2-2, the data trading platform estimates the value of the data use result by using an estimation method based on the expected use value of the data, and specifically, the data trading platform estimates the expected use result of the data according to data use logic provided by a data buyer, determines the expected use value of the data as the estimation of the data according to the data use scene of the buyer, and gives comprehensive suggested pricing by combining the quality of the data and the supply-demand relationship of the data market.
As a preferred embodiment, the S3 data buyer and seller determine the data transaction rules, determine the verification program logic, generate the intelligent contract, achieve the consensus through the consensus network, form the blocks, and add the block chain platform.
S3-1, automatically starting to execute the intelligent contract according to the time requirement, and calculating the data use result by the transaction platform by combining the encrypted data and the confused use scene;
s3-2, the verification node executes the program for verifying the data validity in the intelligent contract; if the verification is passed, the data seller authorizes the transaction platform to transmit the data use result, and updates the execution state; otherwise, informing the buyer node and the seller node that the transaction is failed, and updating the execution state;
and after the condition is met by S3-3, the data transaction platform transmits the data use result to the data buyer through the authorization of the data seller.
As a preferred embodiment, the data usage status of S4 is recorded and uploaded to the block in real time, and the increment or loss generated after data transaction is directly distributed through an intelligent contract, so as to perform post-incident accountability for the abnormal situation to the participating party:
the using state of the S4-1 data is recorded in real time by the verification node and uploaded to the block;
s4-2, account numbers of a data buyer and a data seller participating in the transaction, a time stamp of the transaction, a model of the data buyer, data of the data seller, various terms of the transaction process and calculation results, wherein the recorded hash values are used for verifying whether members participating in the transaction follow transaction rules;
s4-3, directly distributing additional value or loss generated after data transaction through an intelligent contract, wherein the intelligent contract automatically calculates profit or loss brought by the data transaction, and if the verified data after transaction has poor effect or counterfeit phenomenon, the data seller is automatically deducted the amount of the appointment before transaction;
s4-4 the invention adopts a mechanism of tracing after affairs, after the improper action happens, punishment is carried out to the offender of the improper action through the record tracing without tampering, in the process of data transaction, the actions of all participants are recorded in an encryption form without tampering, the records after encryption are recombined into the form of Mercker tree and uploaded to a block chain, and the data transaction platform carries out tracing to the improper action in the transaction process.
As a preferred embodiment, the S5 non-central data transaction platform architecture includes:
the processing node is used as a point-to-point central node to assist the transaction behavior of a certain group of data buyers and data sellers in the transaction center;
the computing node: data usage results and expected usage values for independently computing data;
s5-1, the data transaction platform randomly selects a communication node in a block chain as a processing node, processes the appointed data transaction, and avoids mutual collusion of members participating in the transaction;
s5-2, the processing node uploads the obfuscated model and the encrypted data to a random computing node in a trading platform;
s5-3, the computing node completes the data value estimation or the computation of the data use result;
and S5-4, the computing nodes execute the intelligent contract, verify the data value, and if the data value is identified, upload the result of the value calculation agreed by more than two thirds of the computing nodes to the intelligent contract.
As a preferred embodiment, the S6 data encryption mechanism and data secure transaction mechanism are designed as follows:
s6-1 Datavendor generates homomorphically encrypted one-time asymmetric key pairs (Pub)seller，Priseller) And use the public key PubsellerEncrypting the data for sale;
s6-2 data seller will encrypt the public key Pub in the data homomorphic processsellerTransmitted to the buyer node through PubsellerHomomorphic encryption is carried out on parameters in the model;
s6-3 data seller homomorphically encrypted Key PrisellerSubmitting to a computing node;
s6-4 computing node by PrisellerAnd decrypting the operation result of the model parameters and the data, and finishing the data value estimation and the calculation of the data use result.
Compared with the prior art, the invention has the beneficial effects that:
(1) the value consensus is as follows: the data consumers can agree on the value of the data. The data transaction center adopts a data value estimation method based on usage, the estimation result of the method depends on the data of a data seller, the prior judgment of the data by a data buyer and the data usage scene of the data seller, the expected value of the data is evaluated three-dimensionally from the three aspects, and the difference between the data pricing and the actual generated value is redistributed according to the prior agreement.
(2) And (3) trustable: for convenience, it is assumed that the data exchange center and the various nodes therein can be trusted with the support of blockchain technology. The block chain technology comprises an encryption algorithm and an intelligent contract, and ensures the transparency and the non-tamper property of data in the transmission and use processes. When a buyer of data and a seller of data need to agree on the use condition and value of the data, a third-party platform which can be trusted is a precondition for guaranteeing the privacy and safety of shared data. The invention designs a targeted functional module to ensure the credibility of the data transaction center.
(3) Data and model transmission security: to ensure the security of the data, the data will be homomorphically encrypted before transmission. The buyer of the data can see the encrypted data but cannot obtain the original data. The data buyer and the data seller carry out data transaction through an intelligent contract interface calling module arranged in the transaction center. Other nodes in the platform can only observe or update the transaction records between the data buyer and the data seller, and cannot see the actual transaction content between the two.
Drawings
FIG. 1 is a general architecture diagram of a value-of-use based data trading platform designed in accordance with the present invention.
Fig. 2 is a flow chart of a transaction in an embodiment.
FIG. 3 is a supply chain data transaction scenario.
FIG. 4 is a Merck tree structure formed by transaction-related information in a supply chain data transaction.
Fig. 5 is a block diagram of an decentralized trading platform.
Fig. 6 is a data encryption flow chart.
Detailed Description
The embodiments of the invention are described in detail below with reference to the drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
Unlike common ownership-based data transactions, the transaction object in the method is the result of data generated in the usage scenario of the buyer. For example, a data buyer needs to purchase data from a seller to update parameters in the algorithm, which are the objects of transaction, so as to improve the accuracy of the algorithm. Traditional assets have exclusive, general trade ownership. And the data reproducibility is strong, the transfer is easy, and the data ownership can not be simply traded. The method is essentially trading the use of the data and circumventing the risk of leakage or abuse of the data by the buyer.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 and 2 are flow charts of transaction of a data transaction platform based on use value in an embodiment of the present invention, including the steps of:
s1, submitting a data transaction intention application to the data trading bidirectional trading center, uploading encrypted data by a data seller, uploading data use logic, use scenes and prior information to a trading platform by a data buyer;
the S2 platform gives out comprehensive suggested pricing according to the data market condition, wherein expected use value generated by combining data and data use logic and data use scenes of the buyer is used as a buying and selling matching basis and data evaluation value;
s3, the buyer and seller determine the rule of data transaction, determine the logic of verification program, and generate intelligent contract. The common identification is achieved through the common identification network, the blocks are formed, and the block chain platform is added. The data seller authorizes the transaction platform to transmit the data use result to the data buyer;
and S4, the data use state is recorded and uploaded to the block in real time, and the increment or loss generated after data transaction is directly distributed through an intelligent contract. Participants with misbehaving are chased and penalized by non-tamperable records.
The preferred embodiment of the present invention applies the technical solution of the present invention to the supply chain data transaction process, and the transaction structure thereof is shown in fig. 3. In the embodiment of the invention, an enterprise (data buyer) at the upstream of a supply chain intends to purchase historical sales data of an enterprise (data seller) at the downstream of the supply chain at a data transaction platform, and the decision (data use logic) of an order model is optimized, so that the operation cost and the inventory cost (data use scene) are reduced. The transaction target of the embodiment of the invention is a decision of the model, so that an upstream enterprise of the supply chain cannot directly obtain the sales data of a downstream enterprise of the supply chain, and the data security of the downstream enterprise of the supply chain is protected; the data evaluation and pricing method based on use is adopted, so that the data value is more open and transparent, and the consensus of buyers and sellers on the data value is enhanced; tracking the whole life of the data use, more fairly distributing the value generated by the data, and disposing the violation; the rights of the data transaction platform are limited by adopting a non-central platform architecture and a homomorphic encryption transaction process.
The following is detailed by the specific steps:
s1-1, the data buyer (supply chain upstream enterprise) applies for data transaction intention to the transaction platform;
the S1-2 platform requests the certification authority to carry out identity certification to both the buyer and the seller of the data, adds the digital signature of the platform to the certification result returned by the certification authority to form a digital certificate and writes the digital certificate into the block chain system, and meanwhile, the platform verifies the data ownership of the seller node. If the identity authentication is not passed, the transaction is interrupted immediately;
s1-3, the data seller (supply chain downstream enterprise) provides the data transaction platform with the input format of the historical sales data and the data sample;
s1-4, the data buyer provides the fuzzified newborn model decision (data use logic), the inventory cost optimization function, the encrypted model parameters and the prior information to the trading platform. Wherein, the decision of the child reporting model is as follows:
Figure BDA0003494684420000131
Figure BDA0003494684420000132
q is order quantity, x is downstream retailer demand, cuAnd coRespectively representing the backorder cost (understage cost) and the overbooking cost (override cost). Furthermore, t+Denoted max (t, 0), and E (-) denotes the desired operation. Uploading the hashed information to a block chain, and reserving the hash value as a deposit certificate;
the S1-5 platform verifies the data use logic and the authenticity of the use scene according to the history of the upstream enterprise and other information, and verifies and writes the data into the blockchain system. If the condition is not satisfied, the transaction is immediately interrupted.
The S2-1 data trading platform utilizes an intelligent matching engine based on data prior use values to intelligently match buyers and sellers. The data transaction platform estimates the prior decision of the supply chain upstream enterprise according to the prior estimation and the newborn model decision provided by the supply chain upstream enterprise, then determines the prior use value of the data according to the inventory cost, and matches a proper data seller from the seller pool;
the S2-2 data trading platform estimates the value of the data usage results using an evaluation method based on the expected usage value of the data. The specific operation is as follows, according to the historical sales data y of the downstream retailer, the prior distribution pi is updated to the posterior distribution pi through the Bayesian formulaYIn the posterior distribution of piYUnder the condition(s) of (a) calculating a corresponding optimal Bayesian decision q*(πY) Calculating the demand distribution and the optimal decision q after the update*(πY) Bayesian Risk of time R (μ)0，q*(πY) Compare the inventory cost of a business upstream in the supply chain when purchasing and not purchasing data as the expected use value of the data:
Figure BDA0003494684420000141
wherein μ is the mean of the demand X;
and (4) giving comprehensive suggested pricing by combining the quality of the data and the supply-demand relationship of the data market.
S3-1, automatically starting to execute the intelligent contract according to the time requirement, and calculating the data use result by the transaction platform by combining the encrypted data and the confused use scene;
s3-2, the verification node executes the program for verifying the data validity in the intelligent contract; if the verification is passed, the data seller authorizes the transaction platform to transmit the data use result, and updates the execution state; otherwise, informing the buyer node and the seller node that the transaction is failed, and updating the execution state;
and after the S3-3 meets the condition, the data transaction platform transmits the data use result to the data buyer through the authorization of the data seller.
The using state of the S4-1 data is recorded in real time by the verification node and uploaded to the block;
s4-2 is involved in the account addresses of the data buyer and the data seller of the transaction, the time stamp of the transaction, the model of the data buyer, the data of the data seller, various terms and calculation results of the transaction process, etc. The hash values of these records will be used to verify that the members involved in the transaction have followed the transaction rules;
s4-3 directly distributes the added value or loss generated after the data transaction through the smart contract. The intelligent contract will automatically calculate the profit or loss from the data transaction. If the data sold by the downstream enterprise is found to have poor effect or counterfeit phenomenon after the transaction, the downstream enterprise is automatically deducted the amount agreed before the transaction;
in order to still ensure the traceability of the data transaction center, the invention adopts a post-transaction traceability mechanism, and after the occurrence of the improper behavior, punishment is carried out on the person who causes the improper behavior through the non-falsifiable record traceability;
s4-4 penalizes the offender of the misbehavior by irrevocable record accountability after the misbehavior occurs. During the data transaction, the behavior of all participants is recorded in a non-tamperable encrypted form, and the transaction behavior record comprises: account addresses of upstream enterprises and downstream enterprises in a supply chain participating in transaction, time stamps of transaction, a child reporting model of the upstream enterprises, historical sales data of the downstream enterprises, various terms and calculation results of a transaction process and the like. These encrypted records will be reassembled into the form of the mercker tree. The merkel tree is a special data structure, and each non-leaf node is obtained by hashing the hash value of the child node of the non-leaf node again. When the relevant information is uploaded by the data buyer and the data seller, the corresponding Mercker tree is generated in the data transaction. The mercker tree records the key transaction behaviors of each participant with very low storage cost, so that the data transaction platform can trace the inappropriate behaviors in the transaction process (including the inappropriate behaviors of the data transaction platform), and the mercker tree structure in the embodiment refers to fig. 4;
s4-5 records the above information through the Mercker tree structure and uploads to the block chain. The intelligent contract will broadcast some key behaviors of the participants to the entire blockchain network in the form of a merkel tree. These records are used to verify that the members involved in the transaction have followed the rules of the transaction, preventing potentially fraudulent activity like counterfeit data or fictitious data usage scenarios. Misbehavior in the transaction also includes the necessary information to miss or deny the upload of the prescription. After the misbehavior is identified, the data trafficking platform has the right to terminate the traffic and penalize the implementer of the misbehavior according to the prior agreement. All important behavior information can be recorded and verified through the non-tamper property of the public account book in the block chain, the bulletin board and the Mercker tree, so that the behavior of each data sharing participant is regulated, and the responsibility can be pursued afterwards;
the data transaction platform is based on the decentralized structure, and the structure diagram of the data transaction platform refers to fig. 5, the transaction process based on the decentralized structure is as follows:
s5-1, the data transaction platform randomly selects a communication node in a block chain as a processing node, processes the appointed data transaction, and avoids mutual collusion of members participating in the transaction;
s5-2, the processing node uploads the obfuscated model and the encrypted data to a random computing node in a trading platform;
s5-3, the computing node completes the data value estimation or the computation of the data use result;
and the computing node of S5-4 executes the intelligent contract and verifies the data value. And if the data values are identified, the results of the value calculation are uploaded to the intelligent contract after two thirds of the calculation nodes agree.
The embodiment adopts a classical asymmetric encryption algorithm Paillier encryption system to explain how data is encrypted and protected in the transaction process. Please refer to fig. 6, it should be noted that the secure data transaction process proposed by the present invention not only protects the data privacy of the data seller, but also ensures that the model parameters of the data buyer are not revealed. The detailed key generation and encryption flow is as follows:
and (3) key generation: a pair of one-time public and private keys (pk, sk) is generated in each data transaction.
(1) Two large prime numbers P and Q are randomly selected, and the P and Q satisfy gcd (PQ, (P-1) (Q-1)) ═ 1. Where gcd denotes the greatest common divisor.
(2) N ═ PQ and λ ═ lcm (P-1, Q-1) were calculated. lcm represents the least common multiple.
(3) Note the book
Figure BDA0003494684420000171
Is a set of non-negative integers with a multiplicative inverse model. Randomly selecting integers from the set
Figure BDA0003494684420000172
Satisfy gcd (L (g)λmod N2) N) 1, wherein
Figure BDA0003494684420000173
(4) Calculating psi ═ L (g)λmod N2))-1mod N。
(5) The public key pk is chosen as (N, G) and the private key is chosen as (λ, ψ).
And (3) encryption process: the plaintext data is encrypted into ciphertext.
1. Assume that a is plaintext that needs to be encrypted. A is more than 0 and less than N.
2. Randomly choosing an integer w between (0, N), and
Figure BDA0003494684420000174
ciphertext form of a C ═ Epk(A)＝gA·wNmod N2。
And (3) decryption process: and decrypting the ciphertext data into plaintext.
1. Assume C is the ciphertext that needs to be decrypted. A is more than 0 and less than N.
Plain text form of C is a ═ Dsk(C)＝L(Cλmod N2)·ψmod N。
Suppose EpkAnd (A, w) is a ciphertext obtained by encrypting A by the public key pk, and w is a random integer of the second part in the encryption process. v is a parameter in plain text. The Paillier encryption system supports homomorphic encryption characteristics in the form:
homomorphic addition: the product of two ciphertexts is equal to the sum of their corresponding plaintexts, i.e. encrypted
D(Epk(A1，w1)×(A2，w2)mod N2)＝A1+A2 mod N.
Plaintext multiplication: the v power of a ciphertext is equal to the plaintext corresponding to the ciphertext multiplied by k and then encrypted, i.e. the ciphertext is encrypted
D(Epk(A，w)v)mod N2＝vA mod N.
The secure data encryption process is as follows:
s6-1 Datavendor generates homomorphically encrypted one-time asymmetric key pairs (Pub)seller，Priseller) Encrypting the data to be sold by using a public key;
s6-2 data seller is to encrypt data homomorphicallyPublic key Pub in (1)sellerTransmitted to the buyer node through PubsellerHomomorphic encryption is carried out on parameters in the model;
s6-3 data seller homomorphically encrypted Key PrisellerSubmitting to a computing node;
s6-4 computing node by PrisellerAnd decrypting the operation result of the model parameters and the data, and finishing the data value estimation and the calculation of the data use result.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A transaction method of data use results based on scenes is applied to a data transaction platform and is characterized by comprising the following steps:
establishing data use logic and a data use scene;
at least one data seller, which is suitable for a user who owns data ownership and sells data;
at least one data buyer, which is suitable for the user who owns the use logic and the use scene, and the buyer purchases data;
the data transaction platform is suitable for matching the buyer and the seller, protecting the privacy of both parties, calculating a data use result, estimating a data value and guaranteeing a transaction flow;
the data transaction platform screens data meeting requirements in a data pool of a data seller by using prior information provided by a data buyer, and the data buyer performs prior judgment on statistical characteristics of the data;
the data transaction platform calculates the use result of the data by utilizing the seller data and the data use logic, calculates the use value of the data by utilizing the use result and the use scene, and assists data pricing;
carrying out full life cycle tracking and management on data use;
and the data transaction platform utilizes the data full-life tracking result to automatically settle the data transaction according to the prior agreement.
2. The method of claim 1, wherein said data includes digital features and information, including numbers, text, graphs, tables, audio, video forms, owned by individuals, businesses, and other organizations, or generated and observed;
the data use logic is logic for the data buyer to use the data to obtain a target result, and an operation result obtained by using the logic and the data comprises parameters in an algorithm and decisions in a model, wherein the data use logic is usually represented by a set of finite regular computer logic code sets;
the scenario includes economic benefits obtained by a data buyer through purchasing data, thereby improving algorithm results and optimizing model decisions, and is represented by a set of finite regular computer logic code sets.
3. The method of claim 1, wherein the data transaction platform uses the usage logic and the usage scenario to determine a data usage value, comprising:
the data transaction platform estimates and records the performance benefit or expected performance benefit of a data buyer when the data buyer does not adopt the data use result in a given data use scene;
the data transaction platform calculates and records the performance benefit or expected performance benefit of a data buyer when adopting a data use result in a given data use scene;
and the data transaction platform is used as the use value of the data in the scene based on the benefit change of whether the data use result is adopted or not, and the use value is the basis of data pricing and subsequent value distribution.
4. The method as claimed in claim 1, wherein tracking the life cycle usage process and the data usage value of the data and automatically settling the data transaction according to the prior agreement comprises:
when the data transaction platform receives a data transaction request, the data transaction platform requests an authentication mechanism to perform identity authentication, adds an authentication result returned by the authentication mechanism to a digital signature of the data transaction platform to form a digital certificate and writes the digital certificate into a block chain system, meanwhile, the data transaction platform judges whether the data belongs to a data seller according to data watermarks or other technical information, if the data seller meets the conditions, the data seller inputs the encrypted data into a format, the data uploading platform specifies an external storage interplanetary file system, and continues to execute a data transaction process, and if the data transaction platform does not receive the data transaction request, the data transaction platform terminates the data transaction;
the data transaction platform stores the data use logic and the use scene and writes the data use logic and the use scene into a block chain system, judges whether the data use logic and the use scene provided by the data buyer are true or not according to the historical record, if the conditions are met, the data buyer provides the fuzzified data use logic, the data use scene, encrypted parameters and prior information, the uploading platform appoints an external storage interplanetary file system IPFS, the hashed information is uploaded to the block chain to be stored as the proof, the data transaction process is continuously executed, and if the data transaction process is not ended, the data transaction is ended;
the data transaction platform tracks the whole life cycle of data transaction, calculates and records the change of the use value of the data, and writes the information of both parties of the data transaction, various transaction information and use process information into the block chain system to prevent algorithm data from being tampered;
the platform calculates expected gain or loss of each data source for a data buyer in a specific scene by using a plurality of methods of a leave-one-out method and a Shapril value method based on the full life cycle tracking record of data transaction, redistributes the data value according to the contribution size and the prior agreement, and dynamically adjusts the value of the data by using a standard fund form.
5. The transaction method of the data usage result based on the scene as claimed in claim 1, wherein the transaction method takes the usage value of the data as a pricing basis and the contribution size of the data as a value distribution basis on the premise of protecting the privacy of both parties of the transaction, and the transaction method comprises the following steps:
s1, submitting a data transaction intention application to the data trading bidirectional trading center, uploading encrypted data by a data seller, uploading data use logic, use scenes and prior information to a trading platform by a data buyer;
the S2 platform takes expected use value generated after the data is combined with the buyer scene as the basis of buying and selling matching and data evaluation, and gives comprehensive suggested pricing in combination with the data market condition;
s3, the buyer and seller determine the data transaction rule, determine the logic of the verification program, generate the intelligent contract, reach the consensus through the consensus network, form the block, add the block chain platform, the seller authorizes the transaction platform to transmit the data using result to the buyer;
s4, the data use state is recorded and uploaded to the block in real time, the increment or loss generated after data transaction is directly distributed through an intelligent contract, and the platform performs rear accountability on the participators under abnormal conditions;
s5, constructing a decentralized data transaction platform architecture;
s6 designs a data secret mechanism and a data safety transaction mechanism.
6. The method of claim 5, wherein the S2 platform evaluates the data according to expected use value generated by combining the data with the buyer scenario as a match between buying and selling:
s2-1, the data transaction platform intelligently matches buyers and sellers by using an intelligent matching engine based on data prior use value, specifically, the data transaction platform estimates the prior use result of the data according to the prior information and use logic provided by the data buyer, then determines the prior use value of the data according to the use scene, and matches suitable data buyers and data sellers from a buyer pool and a seller pool;
s2-2, the data trading platform estimates the value of the data use result by using an estimation method based on the expected use value of the data, and specifically, the data trading platform estimates the expected use result of the data according to data use logic provided by a data buyer, determines the expected use value of the data as the estimation of the data according to the data use scene of the buyer, and gives comprehensive suggested pricing by combining the quality of the data and the supply-demand relationship of the data market.
7. The method of claim 5, wherein the S3 data buyer and seller determine the data transaction rules, determine the verification program logic, generate the intelligent contract, achieve the consensus through the consensus network, form the block, and add the block chain platform, the data seller authorizes the transaction platform to transmit the data usage results to the data buyer:
s3-1, automatically starting to execute the intelligent contract according to the time requirement, and calculating the data use result by the transaction platform by combining the encrypted data and the confused use scene;
s3-2, the verification node executes the program for verifying the data validity in the intelligent contract; if the verification is passed, the data seller authorizes the transaction platform to transmit the data use result, and updates the execution state; otherwise, informing the buyer node and the seller node that the transaction is failed, and updating the execution state;
and after the condition is met by S3-3, the data transaction platform transmits the data use result to the data buyer through the authorization of the data seller.
8. The transaction method of data usage result based on scene as claimed in claim 5, wherein the data usage status of S4 is recorded and uploaded to the block in real time, the increment or loss generated after data transaction is directly distributed through the intelligent contract, and the participant is asked about the abnormal situation afterwards:
the using state of the S4-1 data is recorded in real time by the verification node and uploaded to the block;
s4-2, account numbers of a data buyer and a data seller participating in the transaction, a time stamp of the transaction, a model of the data buyer, data of the data seller, various terms of the transaction process and calculation results, wherein the recorded hash values are used for verifying whether members participating in the transaction follow transaction rules;
s4-3, additional value or loss generated after data transaction is directly distributed through an intelligent contract, the intelligent contract automatically calculates profit or loss brought by the data transaction, and if the verified data after transaction has poor effect or counterfeit phenomenon, the data seller is automatically deducted the amount of money contracted before transaction;
s4-4 the invention adopts a mechanism of tracing after affairs, after the improper action happens, punishment is carried out to the offender of the improper action through the record tracing without tampering, in the process of data transaction, the actions of all participants are recorded in an encryption form without tampering, the records after encryption are recombined into the form of Mercker tree and uploaded to a block chain, and the data transaction platform carries out tracing to the improper action in the transaction process.
9. The method for trading of scenario-based data usage results of claim 5, wherein the S5 non-central data trading platform architecture comprises:
the processing node is used as a point-to-point central node to assist the transaction behavior of a certain group of data buyers and data sellers in the transaction center;
the computing node: data usage results and expected usage values for independently computing data;
s5-1, the data transaction platform randomly selects a communication node in a block chain as a processing node, processes the appointed data transaction, and avoids mutual collusion of members participating in the transaction;
s5-2, the processing node uploads the obfuscated model and the encrypted data to a random computing node in a trading platform;
s5-3, the computing node completes the data value estimation or the computation of the data use result;
and S5-4, the computing nodes execute the intelligent contract, verify the data value, and if the data value is identified, upload the result of the value calculation agreed by more than two thirds of the computing nodes to the intelligent contract.
10. The transaction method of data usage result based on scene as claimed in claim 5, wherein said S6 data encryption mechanism and data security transaction mechanism are designed to:
s6-1 Datavendor generates homomorphically encrypted one-time asymmetric key pairs (Pub)seller，Priseller) And use the public key PubsellerEncrypting the data for sale;
s6-2 data seller will encrypt the public key Pub in the data homomorphic processsellerTransmitted to the buyer node through PubsellerHomomorphic encryption is carried out on parameters in the model;
s6-3 data seller homomorphically encrypted Key PrisellerSubmitting to a computing node;
s6-4 computing node by PrisellerAnd decrypting the operation result of the model parameters and the data, and finishing the data value estimation and the calculation of the data use result.
CN202210108820.7A 2022-01-28 2022-01-28 Data use result transaction method based on scene Pending CN114491636A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114710297A (en) * 2022-05-25 2022-07-05 深圳天谷信息科技有限公司 Block chain evidence storing method, device and equipment based on aggregated signature and storage medium
CN115439256A (en) * 2022-11-10 2022-12-06 杭州费尔斯通科技有限公司 Cloud computing big data computing result transaction method based on block chain

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114710297A (en) * 2022-05-25 2022-07-05 深圳天谷信息科技有限公司 Block chain evidence storing method, device and equipment based on aggregated signature and storage medium
CN115439256A (en) * 2022-11-10 2022-12-06 杭州费尔斯通科技有限公司 Cloud computing big data computing result transaction method based on block chain

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