CN116596534A - Block chain safety fairness-based one-to-many data transaction method - Google Patents
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Abstract
The invention discloses a safe and fair one-to-many data transaction method based on a blockchain, which can solve the problem of low efficiency of the existing one-to-one transaction method and realize safe, fair, efficient and decentralized data transaction. The method firstly stores transaction information and data circulation records by using a blockchain technology, and realizes non-tampering and traceability of transactions. Secondly, an attribute-based searchable encryption technology is improved to construct a data circulation method, so that end-to-end encryption of data is realized, and data security and high-efficiency access are ensured. Then, a trusted goods checking mechanism based on zero knowledge proof is designed, so that malicious deception of buyers by sellers is avoided, and transaction fairness is guaranteed. And finally, constructing a pricing method based on the Stark primary game, providing uniform and fair pricing for multiple buyers, and simultaneously meeting the maximization of the income of a transaction entity. The method can effectively reduce transaction operation time, reduce the requirements of encryption computing resources and storage resources, and bring more benefits to transaction users.
Description
Technical Field
The invention belongs to the field of data sharing and transaction, and particularly relates to a one-to-many data transaction method based on blockchain security fairness.
Background
With the rapid development of the internet of things and artificial intelligence technology, the amount of data generated and collected in the human society smart life is explosively increased. However, data is often grasped by a small number of people and stored in a database of a company or organization, and a subject who can mine and use the value of the data often cannot obtain the required data. The data is taken as a novel production element, the data transaction can break the data island, and the value contained in the data can be fully released by converging the data with commercial value and constructing a trusted transaction platform.
The data is used as a digital resource, and is different from the traditional commodity, and has the characteristics of low replication cost, high growth speed, difficult value measurement and the like. On a traditional centralized data transaction platform, once a user uploads data, the user loses control right on the data, the data can be used maliciously, and the user rights and interests are seriously damaged; meanwhile, the centralized transaction platform has single-point fault risk, and the central server fails to cause data transaction stop and privacy leakage. The blockchain technology has the characteristics of decentralization, traceability, non-falsification and the like, and provides transaction transparency and auditability. Part of related work is based on a data transaction market of block chain structure decentralization, can maintain partial transaction fairness and gives a certain trust to a platform, but the data security protection is insufficient, and the data is stored in a traditional decentralization server or a cloud server and still has a certain single point failure probability. Meanwhile, the existing research work is generally limited to one-to-one transactions in transaction form. In the early stage of data market development, the transaction users are fewer, and one-to-one transaction can better meet the market demand, however, after the data market is developed to a certain scale, the data of one seller needs to be sold to a plurality of buyers, and in this case, the one-to-one transaction mode needs to be: the method comprises the steps that data are required to be encrypted and transmitted once for each buyer; the server stores a plurality of ciphertext with consistent original data; the blockchain records a plurality of transaction information. The first two points will cause additional system resource consumption in terms of bandwidth utilization, power supply, storage medium, etc., the third point will increase the blockchain load and prolong the transaction completion time, and the blockchain consensus mechanism will cause more resources to be consumed for every transaction.
In the data market, data sellers may be dishonest users who sell data assets that have an overall actual value that is lower than the purported value, thereby striving for more benefits. Reputation incentive mechanisms, while able to assist buyers in selecting sellers with higher reputation scores, the reputation metrics described by existing research methodologies depend primarily on the after-market ratings of buyers. On the one hand there may be dishonest buyers submitting low score ratings or sellers hiring buyers submitting high score ratings, and on the other hand, buyers cannot return after receiving goods due to the special commodity form of the data, so a fair, trusted third party is required to check the goods before delivery of the data.
Data pricing is a challenge in the field of data transactions. In a one-to-many data transaction scenario, multiple buyers should pay equal amounts to acquire the same data, however, the value of the data has uncertainty, and the same data can create different values for different buyers. The data market is used as a profit organization, a gap is earned between the selling price obtained from the seller and the purchasing price paid by the buyer, and the value of the gap affects the income of the data market and the attraction of the user, so that the market needs to determine a reasonably fair data price for the data to improve the income of each transaction entity.
Disclosure of Invention
Aiming at the problem that the existing data transaction method is limited to the low efficiency of a one-to-one transaction form, the invention provides a one-to-many data transaction method based on the safe fairness of a blockchain, which allows a data seller to sell one data to multiple buyers at the same time so as to optimize the transaction efficiency and the resource consumption in the scene of multiple buyers in the existing one-to-one method. In the data circulation process, the attribute base can be improved to realize one-to-many circulation and end-to-end encryption of data, fine granularity access control is provided, and the safety and high-efficiency access of the data are ensured. A trusted data market checking mechanism is constructed based on zero knowledge proof, and transaction fairness is guaranteed. By constructing a three-layer Stackelberg game architecture, a one-to-many data transaction pricing method is designed, unified and fair pricing is provided for multiple buyers, and the income of transaction entities is maximized.
In order to achieve the above object, the present invention provides a safe and fair one-to-many data transaction method based on blockchain, comprising the following steps:
1) Initializing a system: constructing a data transaction platform, namely a data market, deploying blockchains at all nodes of a cloud server, activating a DAPP for transaction market and key management, and registering accounts for users to participate in transactions;
2) After the data seller collects the instant data generated by the Internet of things equipment, the data are aggregated according to the type of the equipment and the data, and a sales request and corresponding commodity information CI are uploaded; after receiving the selling request, the data market generates a unique CI for the CI id ；
3) The data buyer checks a data commodity list, wherein the commodity list comprises commodity information CI uploaded by the data seller and credit scores of the data seller; the data buyer selects the interested commodity and uploads a purchase request;
4) According to the initial price set by the data seller and the number of data buyers uploading the purchase request, calculating an optimal pricing strategy by using a Stackelberg game and a reverse induction method to price the data in the data market;
5) The data seller encrypts data according to an encryption data stage in the improved attribute-based searchable encryption method, and invokes an intelligent contract to upload the data;
6) The data market calls intelligent contracts, data goods are checked based on a goods checking mechanism with zero knowledge proof, if the goods check is successful, the step 7) is carried out, and if the goods check is failed, the transaction is canceled, and the step 11) is carried out;
7) The data buyer calls intelligent contracts to pay to the data market, and the number is based on the optimal transaction data quantity x * And an optimal purchase price p b * Determining;
8) The data buyer downloads decrypted data according to a data buyer door-trapping stage and a data decrypting stage in the improved attribute-based searchable encryption method;
9) The buyer feeds back the transaction according to the acquired data D;
10 Data market and data seller invoking intelligent contract to make payment settlement, and the settlement amount is x according to the optimal transaction data amount * And an optimal selling price p s * Determining;
11 The data market synthesizes the feedback of each data buyer, updates the credit score of the data seller by combining the checking result, and stores the updated record of the credit score in the blockchain;
12 The data market writes the relevant details of the completed one-to-many data transaction into the blockchain ledger, providing transaction traceability for the user.
Further, the data market comprises a transaction market DAPP, a blockchain, a key management DAPP, a storage operator and a plurality of edge computing devices, wherein the blockchain is deployed on the edge computing devices, manages an account book to record all data transaction histories, and the data transaction histories are recorded in blocks in groups and are linked in time sequence; the transaction market DAPP is responsible for simulating a data transaction market and completing transaction flow, data pricing, data checking and user reputation management; the key management DAPP is used as a security component for monitoring communication among all entities, protecting privacy of data in the circulation process and mainly responsible for user identity verification and data encryption; the storage operator cluster is a distributed cluster formed by a plurality of peer nodes and is responsible for storing data encrypted by a data seller and delivering the data to a data buyer passing trapdoor inspection.
Further, the data seller mainly comprises manufacturers or owners of various internet of things devices, such as intelligent home devices for collecting home use data of clients, intelligent medical devices for collecting physical characteristics and health data of patients, and internet of vehicles for collecting driving data and road information of vehicles; the commodity information CI needs to include, but is not limited to, a theme, a preset price, a commodity description, and a data size.
Further, the data pricing process of the step 4) includes the following steps:
4.1 Deriving expected revenue functions SU, MU, BU for the data seller, data market, buyer, respectively, as shown in the following formulas:
SU(x,p s )＝n·p s ·x-C s ·x，
MU(x,p b ,p s )＝n·p b ·x-n·p s ·x-C m ·x，
wherein C is s For the unit cost of the cost, p s Initial selling price of unit data set for data seller, n is number of data buyers buying the batch of data, x represents number of units of transaction data, p b Initial purchase price of unit data paid to data market for data buyer, C m Storage cost and transaction cost of unit data, C b Cost for obtaining unit original data; the interests obtained by different data buyers from the same data are different, and the interests obtained by the data buyers i (i=1, 2, … n) are set as V i ＝b i Ln (1+x), where b i A benefit parameter representing buyer i;
4.2 Analyzing three-layer Stackelberg games by using a reverse induction method, and obtaining an optimal strategy by solving sub games among different layers so as to achieve the balance of the Stackelberg;
4.3 Data market adjusts data price p according to the optimal purchasing strategy of the data buyer b Maximizing the income of the game machine and forming a sub game of the data market; the purpose of the sub-game is to set an optimal purchase price under the condition that the data buyer adopts an optimal strategy, so x is as follows * Substituted into MU (x, p) b ,p s ) And (3) developing to obtain MU (x * ,p b ,p s ) The following is shown:
deriving its pair p b First order bias guide of (a)The number and second partial derivatives are also constant negative, so MU (x * ,p b ,p s ) Is a strict convex function whenWhen MU takes the maximum value, the optimal purchase price p b * The following are provided:
due to BU (x, p b ) And MU (x) * ,p b ,p s ) Is constant negative, so x * And p is as follows b * Are all globally unique and optimal, i.e. x * And p is as follows b * Is the equilibrium point of the low-layer Stackelberg game;
4.4 To maximize revenue, the data seller dynamically adjusts the data selling price according to the data market's policies, forming a sub-game for the data seller; will p b * And x * Substitution into SU (x, p) s ) Expanding to obtain SU (x) * ,p s ) The following is shown:
calculate its pair p s Is a constant negative number, SU (x) * ,p s ) Is a strict convex function, p s * Is of the equationIs globally unique and optimal;
4.5 According to the three sub-games, the optimal strategy point for achieving the balance of the Stackelberg can be expressed as:
in the constructed Stackelberg game architecture, an optimal strategy x is calculated according to a low-level game through a reverse induction method * Second, according to x * Generating an optimal policy p for a data market b * The method comprises the steps of carrying out a first treatment on the surface of the When the low-level Stackelberg game reaches an equilibrium point, an optimal strategy p can be obtained according to the sub-games of the data seller s * Global packbelg equalization is achieved at this point, i.e. (x * ,p b * ,p s * ) Is a global equilibrium point;
4.6 Data market will optimize policy (x) * ,p b * ,p s * ) To the data buyer and the data seller.
Further, the optimal strategy in the step 4.2) specifically includes the following steps:
4.2.1 From expected revenue functions of the data buyer and the data market, the buyer purchases data amount x and purchase price p b The game between the buyer and the data market is determined by determining the optimal x and p, which affects the revenues of both parties b To simultaneously meet the maximization of the benefits of both parties, and according to the calculation of the optimal data quantity x * Forming a sub-game for the buyer;
4.2.2 Calculating buyer revenue BU (x, p) b ) For the first and second partial derivatives of x, BU (x, p b ) Is a strict convex function whenAt the time of BU maximum value, at this time, the optimal data amount x * The following are provided:
further, the checking mechanism based on zero knowledge proof in the step 6) comprises the following steps:
6.1 Data vendor calculates the value component x in terms of multi-dimension, activity, information entropy, and acquisition cost 1 、x 2 、x 3 、x 4 Obtaining the total value of the dataIs x i Square and square root are first found and then recorded as +.>Randomly generating three random numbers r i 、r * 、r′ i ∈Z p And generates promise->Cm(0,r * ) And Cm (x) i ,t i ) Transmitting the promise and y to a data market;
6.2 Random challenge betae Z for data market delivery p Giving the data seller;
6.3 Data vendor replies to two certificatesGiving the data market;
6.4 Data market check Whether or not it is.
Further, the improved attribute set searchable encryption method of step 8) above includes the steps of:
8.1 Generating system parameters: the public parameter MPK and the master key MSK of the system are generated by the key management DAPP at the time of system initialization, respectively as shown in the following formulas
MPK＝{u,g,g α ,g β ,g γ ,H,(T i ,y i ) i∈{1,2,…,2n} }，
MSK＝{α,β,γ,(t i ,x i ) i∈{1,2,…,2n} }，
8.2 Encrypted data): the data seller generates a symmetric key ek, encrypts data D to generate ED, and then generates ciphertext CD and CT according to the access strategy and the data keyword index;
8.3 Generating a data buyer attribute key: after the data buyer has completed the payment, the key management DAPP generates an attribute key prv for the data buyer according to its attributes, and a partial decryption key SK of the storage operator o ；
8.4 Generating a data buyer trapdoor: when a data buyer obtains an attribute key and prepares to obtain data, the data buyer first randomly selects theta epsilon Z p Trapdoor component Trap 1= (g) is calculated α ·g β·H(bw) ) θ ，Trap2＝g θ·γ Trapdoor trap= (Trap 1, trap2, v) is then generated θ ,sig θ ,py θ ) Uploading to a key management DAPP;
8.5 Searching data: after the data buyer sends the trapdoor, the key management DAPP combines the trapdoor and the CT to judge whether the attribute of the data buyer meets the requirement or not, and calculates the search resultIf the property Battrs of the data buyer satisfies the access policy, equation +.>e(W 0 ,Trap1)·res＝e(W 1 Trap 2) is established, and a search result sr= (B, ED) is generated addr ) Sending to a storage operator; if the equation is not satisfied, returning an error identifier of 'T';
8.6 Decrypting the data: storage operators according to ED addr Retrieving encrypted data using SK o Partial decryption of keys, i.e.Partial data ciphertextAnd sending the PD and the CD to the buyer; DB uses SK b And PD to calculate the key ek=c·pd/e (C', SK) b ) Data D is obtained by decryption of d=sk-Dec (ED, ek).
Further, the generation process of the parameters in the step 8.1) is as follows:
8.1.1 Generating two p-orders (p) from the input security parameter lambda>2 λ ) Multiplying loop group G, G T And a bilinear map e G G.fwdarw.G T G is a generator of G;
8.1.2 Defining a collision-resistant hash function H {0,1} * →Z p Randomly select alpha, beta, gamma E Z p ；
8.1.3 Defining an attribute set u= { attr 1 ,attr 2 ,…,attr n Random selection of { t } 1 ,t 2 ,…,t 2n }∈Z p ，{x 1 ,x 2 ,…,x 2n E G, sety i ＝e(x i ，g)；
8.1.4 MPK and MSK are generated.
Further, the step 8.2) includes the steps of:
8.2.1 MPK and access policy= { Dattr) 1 ,Dattr 2 ,…,Dattr n The DS encrypts the data D by using the symmetric key ek to obtain encrypted data ED=SK-Enc (D, ek), and creates an index according to the data keyword w;
8.2.2 DS sends the encrypted data to the storage operator and obtains the returned storage address ED addr Then DS randomly selects s, r 1 ,r 2 ∈Z p Calculate ciphertext c=e (g, g) α·s ·ek，C′＝g s Generating cd= (ED, C') and sending to the storage operator;
8.2.3 If Dattr in policy i E U, setting a value v corresponding to the attribute i ' v is i And T is i ′＝T i Otherwise, is-v i And T is i ′＝T i+n Calculation of
8.2.4 Calculation of (c)B＝e(g γ·s ,g β ) GeneratingUploading to the key management DAPP.
Further, the above 8.3) attribute key prv and decryption key SK o The generation of (a) comprises the following steps:
8.3.1 Input MPK, MSK, and attribute set Batters of DB; the key management DAPP selects a random value alpha 1 ∈Z p Generating alpha 2 ＝α-α 1 Calculation of SK is used for o Sending to a storage operator;
8.3.2 V=g) α·γ If the ith attribute Battr of DB i E U, setting the value corresponding to the attribute as v i And py i ＝y i ，Otherwise, the value corresponding to the attribute is-v i And py i ＝y i+n ，/>
8.3.3 Calculation of (c)Generating prv= (v, sig, py, SK) b ) Prv is sent to SDB.
The invention has the following beneficial effects;
the method realizes a novel one-to-many data transaction, allows a data seller to sell one data to multiple buyers at the same time, effectively reduces transaction running time and blockchain load, reduces encryption computing resource and storage resource requirements, and adapts to large data volume transaction. The improved attribute-based searchable encryption ensures data security and efficient access, and the goods checking mechanism maintains transaction fairness. Meanwhile, the pricing method provides an optimal pricing strategy, and more benefits are brought to transaction users.
Drawings
FIG. 1 is a flow chart of a one-to-many data transaction method based on blockchain security fairness.
FIG. 2 is a schematic diagram of a one-to-many data transaction framework.
FIG. 3 is a schematic diagram of data pricing based on Stackelberg gaming.
Fig. 4 is a data inspection flow chart based on zero knowledge proof.
FIG. 5 is a flow chart of data flow for attribute-based searchable encryption.
Detailed Description
The invention will be further described with reference to the drawings and the specific embodiments, it being noted that the technical method and the design principle of the invention will be described in detail in only one optimized technical way, but the scope of the invention is not limited thereto.
As shown in fig. 1, the present invention relates to a one-to-many data transaction method based on blockchain security fairness, comprising the following steps:
1) In the system initialization stage, a data transaction platform, namely a data market, is constructed, a blockchain is deployed on each node in a cloud server, DAPP for transaction market and key management is activated, and finally, a user registers an account to participate in the transaction.
As a preferred embodiment of the present invention, in a one-to-many data transaction framework as shown in fig. 2, a data seller participates data as sales commodity in a data market as a data owner, and a data buyer mainly consists of a mechanism interested in the data commodity, which can acquire more profits through purchasing the obtained data, while multiple buyers purchase the same data commodity in a real mature market. The data market is mainly composed of a decentralised application program (DAPP), a blockchain, a storage operator and a plurality of edge computing devices, the blockchain is deployed on the edge computing devices, the ledgers are managed to record all data transaction histories, and the transaction data are recorded in blocks in groups and are linked in time sequence. The deployed intelligent contracts are composed of automated scripts for controlling payment settlement of orders and parties without manual interaction. The transaction market DAPP is responsible for simulating the data transaction market, completing transaction flow, data pricing, data checking and user reputation management. The key management DAPP is used as a security component for monitoring communication among all entities, protecting privacy of data in the circulation process and mainly responsible for user identity verification and data encryption. The storage operator cluster is a distributed cluster formed by a plurality of peer nodes and is responsible for storing data encrypted by a data seller and delivering the data to a data buyer passing trapdoor inspection. After the system initialization is completed, the user registers the account by calling the intelligent contract, and the data market gives the initial credit score to the account.
2) After the data seller collects the instant data generated by the Internet of things equipment, the data are aggregated according to the type of the equipment and the data, and a sales request and corresponding commodity information CI are uploaded; after receiving the selling request, the data market generates a unique CI for the CI id ；
As a preferred embodiment of the invention, the data seller mainly comprises manufacturers or owners of various Internet of things devices, such as intelligent household devices for collecting household use data of clients, intelligent medical devices for collecting physical characteristics and health data of patients, and Internet of vehicles for collecting driving data and road information of vehicles; the commodity information CI needs to include, but is not limited to, a theme, a preset price, a commodity description, and a data size.
3) The data buyer checks a data commodity list, wherein the commodity list comprises commodity information CI uploaded by the data seller and credit scores of the data seller; the data buyer selects the interested commodity and uploads a purchase request;
4) According to the initial price set by the data seller and the number of data buyers uploading the purchase request, calculating an optimal pricing strategy by using a Stackelberg game and a reverse induction method to price the data in the data market;
as a preferred embodiment of the present invention, as shown in fig. 3, the data pricing process is a process of calculating an optimal pricing strategy based on a jackberg game and a reverse induction method, and includes the following steps:
4.1 Deriving expected revenue functions SU, MU, BU for the seller, data market, buyer, respectively, as shown in the following formulas:
SU(x,p s )＝n·p s ·x-C s ·x，
MU(x,p b ,p s )＝n·p b ·x-n·p s ·x-C m ·x，
wherein C is s For the unit cost of the cost, p s Initial sales price for unit data set for seller, n is number of buyers purchasing the batch of data, x represents number of units of transaction data, p b Initial purchase price of unit data paid to data market for data buyer, C m Storage cost and transaction cost of unit data, C b To obtain the cost of the unit of raw data. The interests obtained from the same data by different buyers are different, and the interests obtained by the data buyer i (i=1, 2, … n) are set as V i ＝b i Ln (1+x), where b i A benefit parameter representing buyer i.
4.2 The three-layer Stackelberg game is analyzed by using a reverse induction method, and an optimal strategy can be obtained by solving sub games among different layers so as to achieve the balance of the Stackelberg.
As a preferred embodiment of the present invention, step 4.2) first analyzes the game between the buyer and the data market to form a sub-game for the buyer, as follows:
4.2.1 From the expected revenue function of the buyer and the data market, the buyer purchases the data amount x and the purchase price p b The game between the buyer and the data market is determined by determining the optimal x and p, which affects the revenues of both parties b To simultaneously meet the maximization of the benefits of both parties, and according to the calculation of the optimal data quantity x * Forms a sub-game for the buyer.
4.2.2 Calculating buyer revenue BU (x, p) b ) For the first and second partial derivatives of x, BU (x, p b ) Is a strict convex function whenAt the time of BU maximum value, at this time, the optimal data amount x * The following are provided:
4.3 Data market adjusts data price p according to the optimal purchasing strategy of the data buyer b And the income is maximized, so that the sub-game of the data market is formed. The purpose of this sub-game is to set an optimal purchase price under the data buyer's optimal strategy conditions, thus, x will be * Substituted into MU (x, p) b ,p s ) And (3) developing to obtain MU (x * ,p b ,p s ) The following is shown:
deriving its pair p b And the second partial derivative is also constant negative, so MU (x * ,p b ,p s ) Is a strict convex function whenWhen MU takes the maximum value, the optimal purchase price p b * The following are provided:
due to BU (x, p b ) And MU (x) * ,p b ,p s ) Is constant negative, so x * And p is as follows b * Are all globally unique and optimal, i.e. x * And p is as follows b * Is the equilibrium point for the low-level Stackelberg game.
4.4 To maximize revenue, the seller can dynamically adjust the data selling price to form the seller's sub-game based on the data market's policies. Will p b * And x * Substitution into SU (x, p) s ) Expanding to obtain SU (x) * ,p s ) The following is shown:
calculate its pair p s Is a constant negative number, SU (x) * ,p s ) Is a strict convex function, p s * Is of the equationIs globally unique and optimal.
4.5 According to the three sub-games, the optimal strategy point for achieving the balance of the Stackelberg can be expressed as:
in the constructed Stackelberg game architecture, an optimal strategy x is calculated according to a low-level game through a reverse induction method * Second, according to x * Generating an optimal policy p for a data market b * . When the low-level Stackelberg game reaches an equilibrium point, an optimal strategy p can be obtained according to the sub-games of the data seller s * Global packbelg equalization is achieved at this point, i.e. (x * ,p b * ,p s * ) Is a global equalization point.
4.6 Data market will optimize policy (x) * ,p b * ,p s * ) To the buyer and seller.
5) The data seller encrypts data according to an encryption data stage in the improved attribute-based searchable encryption method, and invokes an intelligent contract to upload the data; for a complete presentation of the data end-to-end flow path, this step is shown as part of it, and the implementation is shown in step 8).
6) The data market calls intelligent contracts, data goods are checked based on a goods checking mechanism with zero knowledge proof, if the goods check is successful, the step 7) is carried out, and if the goods check is failed, the transaction is canceled, and the step 11) is carried out;
as shown in fig. 4, as a preferred embodiment of the present invention, the specific steps of step 6) are as follows:
6.1 In a zero knowledge proof based checkstand mechanism, the data seller acts as a prover, the data market acts as a verifier, and the knowledge that needs to be verified is the value of the transaction data. The seller calculates the value component x in terms of multi-dimension, activity, information entropy and acquisition cost 1 、x 2 、x 3 、x 4 Then calculate the total value of the dataThen, is x i Square and square root are first found and then recorded as +.>Randomly generate r i 、r * 、r i ′∈Z p And generates promise-> And Cm (x) i ,r i ) The promise and y are sent to the data market.
6.2 Data market verifies data value based on zero knowledge proof,
as a preferred embodiment of the invention, the specific steps of step 6.2) are as follows:
6.2.1 Random challenge betae Z for data market delivery p Giving the seller;
6.2.2 Vendor replyGiving the data market;
6.2.3 Data market check Whether or not to establish;
6.2.4 If the checking result is successful, returning success information and turning to the step 7), otherwise turning to the step 11).
7) The data buyer calls intelligent contracts to pay to the data market, and the number is based on the optimal transaction data quantity x * And an optimal purchase price p b * Determining;
8) The data buyer can search the generation data buyer in the encryption method according to the improved attribute base, and download the decrypted data in the data buyer trapdoor stage and the data decryption stage.
As a preferred embodiment of the present invention, in connection with the data flow process formed in step 5), the attribute-based searchable encryption method and bilinear mapping theory are involved, and as shown in fig. 5, step 8) specifically includes the following:
8.1 This step is a generation phase of system parameters, which are executed by the key management DAPP during the system initialization period to generate the system public parameters MPK and the master key MSK, as shown in the following formulas
MPK＝{u,g,g α ,g β ,g γ ,H,(T i ,y i ) i∈{1,2,…,2n} }，
MSK＝{α,β,γ,(t i ,xi) i∈{1,2,…,2n} }，
As a preferred embodiment of the present invention, the generation process of each parameter is as follows:
8.1.1 Generating two p-orders (p) from the input security parameter lambda>2 λ ) Multiplying loop group G, G T And a bilinear map e G G.fwdarw.G T G is a generator of G;
8.1.2 Defining a collision-resistant hash function H {0,1} * →Z p Randomly select alpha, beta, gamma E Z p ；
8.1.3 Defining an attribute set u= { attr 1 ,attr 2 ,…,attr n Random selection of { t } 1 ,t 2 ,…,t 2n }∈Z p ，{x 1 ,x 2 ,…,x 2n E G, sett i ＝e(x i ,g)；
8.1.4 MPK and MSK are generated.
8.2 This step is the encryption phase of the data, the seller generates a symmetric key ek, encrypts the data D to generate ED, and then generates CD and CT according to the access policy and the data key index.
As a preferred embodiment of the present invention, the generation steps are as follows:
8.2.1 MPK and access policy= { Dattr) 1 ,Dattr 2 ,…,Dattr n DS encrypts data D using a symmetric key ek, resulting in encrypted data ed=sk_enc (D, ek),creating an index according to the data keyword w;
8.2.2 DS sends the encrypted data to the storage operator and obtains the returned storage address ED addr Then DS randomly selects s, r 1 ,r 2 ∈Z p Calculate ciphertext c=e (g, g) α·s ·ek，C′＝g s Generating cd= (ED, C') and sending to the storage operator;
8.2.3 If Dattr in policy i E U, setting a value v corresponding to the attribute i ' v is i And T is i ′＝T i Otherwise, is-v i And T is i ′＝T i+n Calculation of
8.2.4 Calculation of (c)B＝e(g γ·s ,g β ) GeneratingUploading to the key management DAPP.
8.3 This step is the generation phase of the buyer attribute key, which occurs after the buyer has completed payment, for which the key management DAPP generates the attribute key prv according to the buyer attribute, and the storage operator's partial decryption key SK o 。
As a preferred embodiment of the invention, prv and SK o The generation steps of (a) are as follows:
8.3.1 MPK, MSK, and attribute set Batters of DB). The key management DAPP selects a random value alpha 1 ∈Z p Generating alpha 2 ＝α-α 1 Calculation ofSK is used for o Sending to a storage operator;
8.3.2 V=g) α·γ If the ith attribute Battr of DB i E U, setting the value corresponding to the attribute as v i And py i ＝y i ，Otherwise, the value corresponding to the attribute is-v i And py i ＝y i+n ，/>
8.3.3 Calculation of (c)Generating prv= (v, sig, py, SK) b ) Prv is sent to DB.
8.4 This step is a generation stage of the buyer trapdoor, which occurs at a time when the buyer is ready to acquire data after acquiring the attribute key. The buyer first randomly selects θ ε Z p Calculate Trap 1= (g) α ·g β·H(bw) ) θ ，Trap2＝g θ·γ Trapdoor trap= (Trap 1, trap2, v) is then generated θ ,sig θ ,py θ ) Uploading to the key management DAPP.
8.5 In the step of searching data, after the buyer sends trapdoor, the key management DAPP combines trapdoor and CT to judge whether the buyer attribute meets the requirement, firstly, calculatingIf the buyer's property Battrs satisfies the access policy, equation +.>e(W 0 ,Trap1)·res＝e(W 1 Trap 2) is established, and a search result sr= (B, ED) is generated addr ) Sending to a storage operator; if the equation is not true, the error identifier "∈is returned.
8.6 This step is the decryption phase of the data.
As a preferred embodiment of the invention, step 8.6) essentially proceeds as follows:
8.6.1 Storage operators according to ED addr Retrieving encrypted data and then utilizing SK o Partial decryption of keys, i.e.Finally, the PD and the CD are sent to the buyer;
8.6.2 DB use SK b And PD to calculate the key ek=c·pd/e (C', SK) b ) Data D is obtained by decryption of d=sk_dec (ED, ek).
9) The buyer feeds back the transaction according to the acquired data D;
10 Data market and data seller invoking intelligent contract to make payment settlement, and the settlement amount is x according to the optimal transaction data amount * And an optimal selling price p s * Determining;
11 The data market synthesizes the feedback of each data buyer, updates the credit score of the data seller by combining the checking result, and stores the updated record of the credit score in the blockchain;
12 The data market writes the relevant details of the completed one-to-many data transaction into the blockchain ledger, providing transaction traceability for the user.
The examples are preferred embodiments of the present invention, but the present invention is not limited to the above-described embodiments, and any obvious modifications, substitutions or variations that can be made by one skilled in the art without departing from the spirit of the present invention are within the scope of the present invention.
The present invention performed experimental analysis in a Hyperledger Fabric blockchain environment, using Go as the development language. The Fabric blockchain is adopted as an experimental environment, and a one-to-many data transaction method based on the safe fairness of the blockchain is designed and completed mainly aiming at data transaction, so that the method has the advantages of high efficiency, low cost, traceability of transaction, decentralization, privacy protection and the like. The foregoing is merely illustrative of the design and features of the present invention, and is intended to enable those skilled in the art to make and use the present invention, without limiting the scope of the invention thereto. Therefore, all equivalent changes or modifications according to the principles and design ideas of the present invention are within the scope of the present invention.
Claims (10)
1. A one-to-many data transaction method based on block chain safety fairness is characterized by comprising the following steps:
1) Initializing a system: constructing a data transaction platform, namely a data market, deploying blockchains at all nodes of a cloud server, activating a DAPP for transaction market and key management, and registering accounts for users to participate in transactions;
2) After the data seller collects the instant data generated by the Internet of things equipment, the data are aggregated according to the type of the equipment and the data, and a sales request and corresponding commodity information CI are uploaded; after receiving the selling request, the data market generates a unique CI for the CI id ；
3) The data buyer checks a data commodity list, wherein the commodity list comprises commodity information CI uploaded by the data seller and credit scores of the data seller; the data buyer selects the interested commodity and uploads a purchase request;
4) According to the initial price set by the data seller and the number of data buyers uploading the purchase request, calculating an optimal pricing strategy by using a Stackelberg game and a reverse induction method to price the data in the data market;
5) The data seller encrypts data according to an encryption data stage in the improved attribute-based searchable encryption method, and invokes an intelligent contract to upload the data;
6) The data market calls intelligent contracts, data goods are checked based on a goods checking mechanism with zero knowledge proof, if the goods check is successful, the step 7) is carried out, and if the goods check is failed, the transaction is canceled, and the step 11) is carried out;
7) The data buyer calls intelligent contracts to pay to the data market, and the number is based on the optimal transaction data quantity x * And an optimal purchase price p b * Determining;
8) The data buyer downloads decrypted data according to a data buyer door-trapping stage and a data decrypting stage in the improved attribute-based searchable encryption method;
9) The buyer feeds back the transaction according to the acquired data D;
10 Data market and data seller invoking intelligent contract to make payment settlement, and the settlement amount is x according to the optimal transaction data amount * And an optimal selling price p s * Determining;
11 The data market synthesizes the feedback of each data buyer, updates the credit score of the data seller by combining the checking result, and stores the updated record of the credit score in the blockchain;
12 The data market writes the relevant details of the completed one-to-many data transaction into the blockchain ledger, providing transaction traceability for the user.
2. The blockchain-based safe fair one-to-many data transaction method of claim 1 wherein the data market includes a transaction market DAPP, a blockchain, a key management DAPP, a storage operator, and a plurality of edge computing devices, the blockchain being deployed on the edge computing devices, the ledger being managed to record all data transaction histories, the data transaction histories being recorded in blocks in groups and linked in time sequence; the transaction market DAPP is responsible for simulating a data transaction market and completing transaction flow, data pricing, data checking and user reputation management; the key management DAPP is used as a security component for monitoring communication among all entities, protecting privacy of data in the circulation process and mainly responsible for user identity verification and data encryption; the storage operator cluster is a distributed cluster formed by a plurality of peer nodes and is responsible for storing data encrypted by a data seller and delivering the data to a data buyer passing trapdoor inspection.
3. The blockchain-based safe and fair one-to-many data transaction method according to claim 1, wherein the data seller mainly comprises manufacturers or owners of various internet of things devices, such as intelligent home devices for collecting home use data of clients, intelligent medical devices for collecting physical characteristics and health data of patients, and internet of vehicles for collecting driving data and road information of vehicles; the commodity information CI needs to include, but is not limited to, a theme, a preset price, a commodity description, and a data size.
4. The blockchain-based safe fair one-to-many data transaction method of claim 1, wherein the data pricing process of step 4) comprises the steps of:
4.1 Deriving expected revenue functions SU, MU, BU for the data seller, data market, buyer, respectively, as shown in the following formulas:
SU(x,p s )＝n·p s ·x-C s ·x，
MU(x,p b ,p s )＝n·p b ·x-n·p s ·x-C m ·x，
wherein C is s For the unit cost of the cost, p s Initial selling price of unit data set for data seller, n is number of data buyers buying the batch of data, x represents number of units of transaction data, p b Initial purchase price of unit data paid to data market for data buyer, C m Storage cost and transaction cost of unit data, C b Cost for obtaining unit original data; the interests obtained by different data buyers from the same data are different, and the interests obtained by the data buyers i (i=1, 2, … n) are set as V i ＝b i Ln (1+x), where b i A benefit parameter representing buyer i;
4.2 Analyzing three-layer Stackelberg games by using a reverse induction method, and obtaining an optimal strategy by solving sub games among different layers so as to achieve the balance of the Stackelberg;
4.3 Data market adjusts data price p according to the optimal purchasing strategy of the data buyer b Maximizing the income of the game machine and forming a sub game of the data market; the purpose of the sub-game is to set an optimal purchase price under the condition that the data buyer adopts an optimal strategy, so x is as follows * Substituted into MU (x, p) b ,p s ) And (3) developing to obtain MU (x * ,p b ,p s ) The following is shown:
deriving its pair p b And the second partial derivative is also constant negative, so MU (x * ,p b ,p s ) Is a strict convex function whenWhen MU takes the maximum value, the optimal purchase price p b * The following are provided:
due to BU (x, p b ) And MU (x) * ,p b ,p s ) Is constant negative, so x * And p is as follows b * Are all globally unique and optimal, i.e. x * And p is as follows b * Is the equilibrium point of the low-layer Stackelberg game;
4.4 To maximize revenue, the data seller dynamically adjusts the data selling price according to the data market's policies, forming a sub-game for the data seller; will p b * And x * Substitution into SU (x, p) s ) Expanding to obtain SU (x) * ,p s ) The following is shown:
calculate its pair p s Is a constant negative number, SU (x) * ,p s ) Is a strict convex function, p s * Is of the equationIs globally unique and optimal;
4.5 According to the three sub-games, the optimal strategy point for achieving the balance of the Stackelberg can be expressed as:
in the constructed Stackelberg game architecture, an optimal strategy x is calculated according to a low-level game through a reverse induction method * Second, according to x * Generating an optimal policy p for a data market b * The method comprises the steps of carrying out a first treatment on the surface of the When the low-level Stackelberg game reaches an equilibrium point, an optimal strategy p can be obtained according to the sub-games of the data seller s * Global packbelg equalization is achieved at this point, i.e. (x * ,p b * ,p s * ) Is a global equilibrium point;
4.6 Data market will optimize policy (x) * ,p b * ,p s * ) To the data buyer and the data seller.
5. The blockchain-based safe fair one-to-many data transaction method of claim 4 wherein the optimal strategy of step 4.2) specifically comprises the steps of:
4.2.1 From expected revenue functions of the data buyer and the data market, the buyer purchases data amount x and purchase price p b Influencing the returns of both parties, buyers and data marketsInter-gaming is by determining optimal x and p b To simultaneously meet the maximization of the benefits of both parties, and according to the calculation of the optimal data quantity x * Forming a sub-game for the buyer;
4.2.2 Calculating buyer revenue BU (x, p) b ) For the first and second partial derivatives of x, BU (x, p b ) Is a strict convex function whenAt the time of BU maximum value, at this time, the optimal data amount x * The following are provided:
6. the blockchain-based safe fair one-to-many data transaction method of claim 1 wherein the zero knowledge proof-based checkstand mechanism of step 6) comprises the steps of:
6.1 Data vendor calculates the value component x in terms of multi-dimension, activity, information entropy, and acquisition cost 1 、x 2 、x 3 、x 4 Obtaining the total value of the dataIs x i Square and square root are first found and then recorded as +.>Randomly generating three random numbers r i 、r * 、r′ i ∈Z p And generates promise->Cm(0,r * ) And Cm (x) i ,r i ) Transmitting the promise and y to a data market;
6.2 Data market issue)Sending random challenge beta epsilon Z p Giving the data seller;
6.3 Data vendor replies to two certificatesGiving the data market;
6.4 Data market check Whether or not it is.
7. The blockchain-based safe fair one-to-many data transaction method of claim 1 wherein the improved attribute set searchable encryption method of step 8) comprises the steps of:
8.1 Generating system parameters: the public parameter MPK and the master key MSK of the system are generated by the key management DAPP at the time of system initialization, respectively as shown in the following formulas
MPK＝{u,g,g α ,g β ,g γ ,H,(T i ,y i ) i∈{1,2,…,2n} }，
MSK＝{α,β,γ,(t i ,x i ) i∈{1,2,…,2n} }，
8.2 Encrypted data): the data seller generates a symmetric key ek, encrypts data D to generate ED, and then generates ciphertext CD and CT according to the access strategy and the data keyword index;
8.3 Generating a data buyer attribute key: after the data buyer has completed the payment, the key management DAPP generates an attribute key prv for the data buyer according to its attributes, and a partial decryption key SK of the storage operator o ；
8.4 Generating a data buyer trapdoor: when a data buyer obtains an attribute key and prepares to obtain data, the data buyer first randomly selects theta epsilon Z p Trapdoor component Trap 1= (g) is calculated α ·g β · H(bw) ) θ ，Trap2＝g θ·γ Trapdoor trap= (Trap 1, trap2, v) is then generated θ ,sig θ ,py θ ) Uploading to a key management DAPP;
8.5 Searching data: after the data buyer sends the trapdoor, the key management DAPP combines the trapdoor and the CT to judge whether the attribute of the data buyer meets the requirement or not, and calculates the search resultIf the property Battrs of the data buyer satisfies the access policy, equation +.>e(W 0 ,Trap1)·res＝e(W 1 Trap 2) is established, and a search result sr= (B, ED) is generated addr ) Sending to a storage operator; if the equation is not satisfied, returning an error identifier of 'T';
8.6 Decrypting the data: storage operators according to ED addr Retrieving encrypted data using SK o Partial decryption of keys, i.e. partial data ciphertextAnd sending the PD and the CD to the buyer; DB uses SK b And PD to calculate the key ek=c·pd/e (C', SK) b ) Data D is obtained by decryption of d=sk_dec (ED, ek).
8. The one-to-many data transaction method based on blockchain security fairness of claim 7, wherein the generating process of the parameters in the step 8.1) is as follows:
8.1.1 Generating two p-orders (p) from the input security parameter lambda>2 λ ) Multiplying loop group G, G T And a bilinear map e G G.fwdarw.G T G is a generator of G;
8.1.2 Defining a collision-resistant hash function H {0,1} * →Z p Randomly select alpha, beta, gamma E Z p ；
8.1.3 Defining an attribute set u= { attr 1 ,attr 2 ,…,attr n Random selection of { t } 1 ,t 2 ,…,t 2n }∈Z p ，{x 1 ,x 2 ,…,x 2n E G, sety i ＝e(x i ,g)；
8.1.4 MPK and MSK are generated.
9. The blockchain-based safe fair one-to-many data transaction method of claim 7 wherein said step 8.2) comprises the steps of:
8.2.1 MPK and access policy= { Dattr) 1 ,Dattr 2 ,…,Dattr n The DS encrypts the data D by using the symmetric key ek to obtain encrypted data ED=SK-Enc (D, ek), and creates an index according to the data keyword w;
8.2.2 DS sends the encrypted data to the storage operator and obtains the returned storage address ED addr Then DS randomly selects s, r 1 ,r 2 ∈Z p Calculate ciphertext c=e (g, g) α·s ·ek，C′＝g s Generating cd= (ED, C') and sending to the storage operator;
8.2.3 If Dattr in policy i E U, setting a value v corresponding to the attribute i ' v is i And T is i ′＝T i Otherwise, is-v i And T is i ′＝T i+n Calculation of
8.2.4 Calculation of (c)B＝e(g γ·s ,g β ) GeneratingUploading to the key management DAPP.
10. The one-to-many data transaction method based on blockchain security fairness as recited in claim 7, wherein the attribute key prv and the decryption key SK in step 8.3) o The generation of (a) comprises the following steps:
8.3.1 Input MPK, MSK, and attribute set Batters of DB; the key management DAPP selects a random value alpha 1 ∈Z p Generating 2 ＝-α 1 Calculation ofSK is used for o Sending to a storage operator;
8.3.2 V=g) α·γ If the ith attribute Battr of DB i E U, setting the value corresponding to the attribute as v i And py i ＝y i ，Otherwise, the corresponding value of the attribute is- i And py i ＝ i+n ，/>
8.3.3 Calculation of (c)Generating prv= (v, sig, py, SK) b ) Prv is sent to DB.
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