CN112767021A - Data pricing method for shared automobile data market based on block chain - Google Patents

Data pricing method for shared automobile data market based on block chain Download PDF

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CN112767021A
CN112767021A CN202110041654.9A CN202110041654A CN112767021A CN 112767021 A CN112767021 A CN 112767021A CN 202110041654 A CN202110041654 A CN 202110041654A CN 112767021 A CN112767021 A CN 112767021A
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pricing
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朱琨
许成真
王然
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a data pricing method of a block chain-based shared automobile data market, which comprises the following steps of firstly, constructing a alliance chain-based shared automobile data transaction market, wherein the data transaction market comprises a data source, a data service provider and a data buyer; the data source carries out original data pricing, a data service provider purchases source data, data processing is carried out, data pricing is carried out, and a data buyer purchases data from the service provider; secondly, determining the value of the data according to three factors of privacy quality of the data, quantity quality of the data and accuracy quality of the data; and finally, the data source, the data service provider and the data buyer perform data pricing interaction through a three-layer Stackelberg game. The method ensures personal reasonability of buyers, ensures that the whole mechanism is very real, ensures that any participant can adopt an optimal strategy to improve the self benefit, achieves a Nash equilibrium state and ensures that the game keeps balance.

Description

Data pricing method for shared automobile data market based on block chain
Technical Field
The invention belongs to the field of data pricing, and particularly relates to a block chain-based data pricing method for a shared automobile data market.
Background
The block chain is a distributed account book, the block chain network system maintains a continuous and growing ordered data block without center, each data block is internally provided with a time stamp and a pointer which point to the previous block and cannot be changed once the data is linked. In this definition, a block chain is analogized to a distributed database technology, and by maintaining the chain structure of data blocks, a continuously growing and non-falsifiable data record can be maintained. The blockchain is used as a machine capable of leading trust, and can establish credit in a distributed system with nodes not needing mutual trust by means of Hash algorithm, digital signature, timestamp, distributed consensus, economic incentive and the like, so that point-to-point transaction and cooperation are realized, and a solution is provided for the problems of high cost, low efficiency, unsafe data storage and the like of a centralized mechanism.
Large data, i.e., a large amount of data generated by diverse data sources. The total amount of data in the world has increased explosively, with an estimated 2.5 gigabytes of data being produced each day. In fact, only the last two years have created nearly 90% of the world. Data sources are diverse, especially as the internet of things increasingly participate in our daily lives, supporting numerous intelligent world systems. These multivariate data also create a huge potential commercial value. We refer to this type of data as big data. Big data is massive, continuous and comprehensive, and has high potential commercial value.
Data pricing is a fraction of a large data period. Since data sets have significant commercial value, after data analysis is applied, data pricing models and methods are selected. At this stage, the data owner provides a reasonable price for each data set in order to push the data sets into the digital marketplace. Factors that affect price include data size and customer demand, among others. The owner can use various data pricing models to evaluate the data set and obtain the best profit.
The game theory refers to a process of selecting and implementing actions or strategies that are allowed to be selected by individuals, teams or other organizations under certain rules and one or more times simultaneously or successively in the face of certain environmental conditions, and obtaining corresponding results from the actions or strategies. That is, decision-making and balancing issues when a subject, say a person or business's choice, is influenced by other people or other business's choices, and in turn influences other people, other business's choices.
In the existing data pricing documents, on one hand, some data pricing only considers several mechanisms of a data pricing model, and the traditional data trading market has the defects of centralized control, data leakage and risk of user privacy leakage. On the other hand, some trading markets consider the combination with the blockchain, but for the shared automobile data trading market, pricing and research on data trading scenes are lacked, and the business requirements of the actual automobile trading market are not considered.
Therefore, how to design a point-to-point, credible and transparent trading market and research a reasonable data pricing mechanism aiming at two problems existing in a shared automobile data trading market is a problem which needs to be solved urgently.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a data pricing method of a block chain-based shared automobile data market, which converts the value of data from abstraction to concrete and ensures the personal rationality of buyers.
The technical scheme is as follows: the invention relates to a block chain-based data pricing method for a shared automobile data market, which specifically comprises the following steps:
(1) constructing a federation chain-based shared automobile data transaction market, wherein the data transaction market comprises a data source, a data service provider and a data buyer; the data source carries out original data pricing, a data service provider purchases source data, data processing is carried out, data pricing is carried out, and a data buyer purchases data from the service provider;
(2) determining the value of the data according to three factors of privacy quality of the data, quantity quality of the data and accuracy quality of the data;
(3) and the data source, the data service provider and the data buyer perform data pricing interaction through a three-layer Stackelberg game.
Further, the step (1) includes the steps of:
(11) constructing a point-to-point credible and transparent market environment based on a shared automobile data trading market of a alliance chain;
(12) based on a consensus mechanism of workload certification, stimulating miners to maintain a block chain;
(13) embedding pricing logic into the intelligent contract to automatically execute internal logic of transaction;
(14) after the transaction is completed, the two parties of the transaction evaluate and change the credit score of the two parties.
Further, the step (2) is realized as follows:
according to the characteristics of data in the Internet of vehicles, the data of the Internet of vehicles are divided into five types including automobile driving data, sensor data, automobile insurance data, lease information and automobile insurance information, and the expression of the data is as follows:
Ri＝{R1，R2，...，R5}
the privacy quality of the data is:
Figure BDA0002895549830000031
wherein alpha is1，α2，α3To adjust the parameters, rjThe data privacy quality;
the quantity and quality of the data are as follows:
D(x)＝ln(1+x)
wherein x is the number of data;
the accuracy quality of the data is:
Ki＝ksi+m
where k and m are tuning parameters.
Further, the step (3) includes the steps of:
(31) the data buyer selects an optimal purchasing strategy according to the income of the data buyer, the benefit maximization is taken as a target, and the income of the data buyer is as follows:
U3(x,pb)＝Aj·D(x)·Ki-pbx
wherein p isbPricing for service providers;
(32) buyer of data decides amount of purchase:
(33) according to the adjustment of the data buyer, the data service provider can correspondingly adjust so as to maximize the income of the data service provider, wherein the income of the data service provider is as follows:
U2(x,pb,ps)＝pbx-Qix-f-psx
wherein Q isiIs the processing fee of unit data, f is the transaction fee of the block chain;
(34) adjusting pricing by the data service provider;
(35) the data source adjusts the income of the data service provider according to the adjustment of the pricing strategy and the purchasing strategy of the data service provider so as to maximize the income of the data service provider:
U1(x,ps)＝(ps-c)·x
wherein c is the cost of unit data;
(36) the data source adjusts the selling price of the original data;
(37) and judging whether the Nash equilibrium is reached, if not, repeating (32) to (36) and starting to make corresponding adjustment until the Nash equilibrium is reached.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. the Stackelberg game is adopted in the pricing scheme, and participants obtain higher profits, so that the purchasing strategy and the pricing strategy of the participants are adjusted, and the personal rationality of buyers is guaranteed; 2. the whole mechanism is very real, and is embodied in that any participant can adopt an optimal strategy to improve the self-benefit, and finally a Nash equilibrium state can be achieved, so that the game keeps balance.
Drawings
FIG. 1 is a diagram of a federation chain-based shared automobile data trading market constructed in accordance with the present invention;
FIG. 2 is a flow chart of a pricing mechanism.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The increasing popularity of shared automobiles has produced a vast amount of potentially valuable vehicle data that can be traded on a data trading platform. The traditional trading market is centralized, and problems of data leakage, user privacy leakage and the like exist, and a trusted organization is lacked. In the mechanism, a data transaction framework based on a federation chain is designed to create a data transaction market of P2P and enhance the security of data transaction. In a data market consisting of data sources, service providers and buyers, the data sources provide raw data, and the service providers process and sell the data to data purchasers. Based on the interaction of the three parties, the data pricing problem is defined as a three-layer Stackelberg game problem, so that the optimal pricing strategy is researched.
The invention provides a block chain-based data pricing method for a shared automobile data market, which is used for constructing a alliance chain-based shared automobile data trading market based on a reasonable data pricing mechanism and converting the value of data from abstract to concrete and specifically comprises the following steps:
step 1: constructing a federation chain-based shared automobile data transaction market, as shown in FIG. 1, the data transaction market comprising a data source, a data service provider, and a data buyer; the data source carries out original data pricing, the data service provider purchases source data, data processing is carried out, data pricing is carried out, and a data buyer purchases data from the service provider.
Designing a shared automobile data trading market based on a alliance chain, and constructing a point-to-point credible and transparent market environment; utilizing a consensus mechanism of workload attestation (PoW) to motivate miners to maintain blockchains; embedding pricing logic into the intelligent contract to automatically execute internal logic of transaction; after the transaction is completed, the two transaction parties evaluate and change credit scores of the two transaction parties
Step 2: and determining the value of the data according to three factors of privacy quality of the data, quantity quality of the data and accuracy quality of the data.
Because different data have different values, according to the characteristics of the data in the internet of vehicles, the data of the internet of vehicles are divided into five types, including automobile driving data, sensor data, automobile insurance data, leasing information and automobile insurance information. The expression is as follows:
Ri＝{R1，R2，…，R5}
the privacy quality of the data is:
Figure BDA0002895549830000051
wherein alpha is1，α2，α3To adjust the parameters, rjThe data privacy quality;
the quantity and quality of the data are as follows:
D(x)＝ln(1+x)
wherein x is the number of data;
the accuracy quality of the data is:
Ki＝ksi+m
where k and m are tuning parameters.
And step 3: the data source, the data service provider, performs data pricing through a three-layer Stackelberg game, as shown in fig. 2.
1) The data buyer selects an optimal purchasing strategy according to the income of the data buyer, the benefit maximization is taken as a target, and the income of the data buyer is as follows:
U3(x,pb)＝Aj·D(x)·Ki-pbx
wherein p isbPricing for service providers.
2) Buyer of data decides amount of purchase:
3) according to the adjustment of the data buyer, the data service provider can correspondingly adjust so as to maximize the income of the data service provider, wherein the income of the data service provider is as follows:
U2(x,pb,ps)＝pbx-Qix-f-psx
wherein Q isiIs the processing fee of the unit data, and f is the transaction fee of the block chain.
4) Data service provider adjusting pricing: and adjusting pricing by the data service provider, and recalculating the pricing which enables the self income to be maximized by considering the optimal solution strategy of the data buyer.
5) The data source adjusts the income of the data service provider according to the adjustment of the pricing strategy and the purchasing strategy of the data service provider so as to maximize the income of the data service provider:
U1(x,ps)＝(ps-c)·x
where c is the cost of the unit data.
6) The data source adjusts the selling price of the original data, so that the income of the data source is maximized.
7) Judging whether Nash equilibrium is reached, if not, repeating 2) to 6) and starting to make corresponding adjustment until the Nash equilibrium is reached.
The invention designs a data trading market based on an alliance chain and excited by a workload certification (PoW) consensus mechanism, and simultaneously designs a pricing mechanism of a Stackelberg game according to the game among participants, and executes internal logic of trading by using an intelligent contract. A credible and transparent point-to-point data trading market based on a federation chain is constructed, and a reasonable mechanism is set for pricing data to digitize the value of the data.

Claims (4)

1. A data pricing method for a block chain-based shared automobile data market is characterized by comprising the following steps:
(1) constructing a federation chain-based shared automobile data transaction market, wherein the data transaction market comprises a data source, a data service provider and a data buyer; the data source carries out original data pricing, a data service provider purchases source data, data processing is carried out, data pricing is carried out, and a data buyer purchases data from the service provider;
(2) determining the value of the data according to three factors of privacy quality of the data, quantity quality of the data and accuracy quality of the data;
(3) and the data source, the data service provider and the data buyer perform data pricing interaction through a three-layer Stackelberg game.
2. The block chain based data pricing method of the shared automotive data market according to claim 1, characterized in that the step (1) comprises the steps of:
(11) constructing a point-to-point credible and transparent market environment based on a shared automobile data trading market of a alliance chain;
(12) based on a consensus mechanism of workload certification, stimulating miners to maintain a block chain;
(13) embedding pricing logic into the intelligent contract to automatically execute internal logic of transaction;
(14) after the transaction is completed, the two parties of the transaction evaluate and change the credit score of the two parties.
3. The block chain based data pricing method for the shared automobile data market according to claim 1, wherein the step (2) is implemented as follows:
according to the characteristics of data in the Internet of vehicles, the data of the Internet of vehicles are divided into five types including automobile driving data, sensor data, automobile insurance data, lease information and automobile insurance information, and the expression of the data is as follows:
Ri＝{R1，R2，……R5}
the privacy quality of the data is:
Figure FDA0002895549820000011
wherein alpha is1，α2，α3To adjust parameters，rjThe data privacy quality;
the quantity and quality of the data are as follows:
D(x)＝ln(1+x)
wherein x is the number of data;
the accuracy quality of the data is:
Ki＝ksi+m
where k and m are tuning parameters.
4. The blockchain-based shared automobile data market data pricing method according to claim 1, wherein the step (3) includes the steps of:
(31) the data buyer selects an optimal purchasing strategy according to the income of the data buyer, the benefit maximization is taken as a target, and the income of the data buyer is as follows:
U3(x,pb)＝Aj·D(x)·Ki-pbx
wherein p isbPricing for service providers;
(32) buyer of data decides amount of purchase:
(33) according to the adjustment of the data buyer, the data service provider can correspondingly adjust so as to maximize the income of the data service provider, wherein the income of the data service provider is as follows:
U2(x,pb,ps)＝pbx-Qix-f-psx
wherein Q isiIs the processing fee of unit data, f is the transaction fee of the block chain;
(34) adjusting pricing by the data service provider;
(35) the data source adjusts the income of the data service provider according to the adjustment of the pricing strategy and the purchasing strategy of the data service provider so as to maximize the income of the data service provider:
U1(x,ps)＝(ps-c)·x
wherein c is the cost of unit data;
(36) the data source adjusts the selling price of the original data;
(37) and judging whether the Nash equilibrium is reached, if not, repeating (32) to (36) and starting to make corresponding adjustment until the Nash equilibrium is reached.
CN202110041654.9A 2021-01-13 2021-01-13 Data pricing method for shared automobile data market based on block chain Pending CN112767021A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115017548A (en) * 2022-08-04 2022-09-06 湖南工商大学 Data pricing method and device and related equipment

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Publication number Priority date Publication date Assignee Title
CN106846031A (en) * 2016-11-18 2017-06-13 大连理工大学 Credible P 2 P Streaming Media bandwidth pricing method based on credit system and Stackelberg games
CN110363628A (en) * 2019-07-12 2019-10-22 电子科技大学 A kind of data pricing model for single seller's over-bought man
CN111402043A (en) * 2020-03-03 2020-07-10 中山大学 Internet of vehicles data transaction method based on block chain

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115017548A (en) * 2022-08-04 2022-09-06 湖南工商大学 Data pricing method and device and related equipment
CN115017548B (en) * 2022-08-04 2022-11-08 湖南工商大学 Data pricing method and device and related equipment

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