Privacy-enhanced trusted data transaction method and system based on alliance chain

Abstract

The application relates to data transaction technology and discloses a privacy-enhanced trusted data transaction method based on a consortium chain, which comprises the following steps: a key generation center generates a system parameter list; a data owner calculates a shared key according to the system parameter list, calculates encrypted ciphertext, and uploads the encrypted ciphertext to a decentralized storage network; the decentralized storage network receives the encrypted ciphertext, generates a unique content identifier, and divides the encrypted ciphertext into multiple ciphertext fragments; a root node hash value is calculated, and the root node hash value and the unique content identifier are recorded in the consortium chain; a data buyer encrypts data demand, publishes the data demand through the consortium chain, and the consortium chain matches transaction data according to corresponding demand attributes in the data demand and performs decryption operation to obtain required transaction data. The application further provides a system of the privacy-enhanced trusted data transaction method based on the consortium chain, which can realize safe transaction of data and improve controllability of data in a transaction process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data transaction, in particular to a privacy-enhanced trusted data transaction method and system based on a consortium chain. BACKGROUND

[0002] Under the wave of digital economy and data factorization transformation, data transaction has become the core driving force for promoting business intelligence, precision marketing and cross-field cooperation. As a new type of production factor, the value release of data depends on a safe, efficient and controllable circulation mechanism. However, there are many challenges in the process of data transaction, such as illegal invasion of malicious nodes, risk of leakage of sensitive data, contradiction between transaction efficiency and privacy protection, and data reselling, which seriously restricts the safe circulation and value release of data factors.

[0003] Traditional centralized data transaction platforms rely on trusted third parties to host data, which has single point of failure and privacy exposure risks; while existing decentralized solutions still have deficiencies in privacy protection granularity, transaction efficiency and permission control. In recent years, blockchain technology, with its characteristics of decentralization, non-tamperability and traceability, has provided a new technical path for data transaction. However, the openness of public chains cannot meet the high requirements of enterprises on data privacy, and the closed nature of private chains limits the cross-domain circulation of data. As a compromise solution, consortium chains strike a balance between efficiency and security through admission mechanisms and consensus algorithms, but simply using consortium chains to achieve privacy protection for data transaction will result in insufficient privacy protection strength and low controllability of data, making it difficult to effectively control data in the transaction process. SUMMARY

[0004] The present application provides a privacy-enhanced trusted data transaction method and system based on a consortium chain, which can realize safe transaction of data and improve the controllability of data in the transaction process.

[0005] To achieve the above purpose, the present application provides a privacy-enhanced trusted data transaction method based on a consortium chain, comprising:

[0006] The key generation center initializes system parameters and generates a system parameter list;

[0007] The data owner calculates a shared key according to the system parameter list, and calculates an encrypted ciphertext using the original data and the shared key, and uploads the encrypted ciphertext to a decentralized storage network;

[0008] The decentralized storage network receives the encrypted ciphertext and generates a unique content identifier, and divides the encrypted ciphertext into multiple ciphertext fragments; calculates the hash value of each ciphertext fragment, constructs a hash tree using the hash value, and extracts the root node hash value corresponding to the root node of the hash tree; records the root node hash value and the unique content identifier to the consortium chain;

[0009] The data buyer encrypts the data requirement and publishes the data requirement through the alliance chain, the alliance chain matches the transaction data according to the corresponding requirement attribute in the data requirement, and after the matching is successful, a third-party agency is used for decryption operation to obtain the required transaction data.

[0010] Optionally, the constructing the hash tree by using the hash values comprises:

[0011] Step 1, taking the hash value of each ciphertext slice as a first hash tree node;

[0012] Step 2, taking the hash value obtained by splicing the hash values of two adjacent first hash tree nodes and then performing hash operation again as a second hash tree node;

[0013] Repeating steps 1-2 until the final hash value is generated, and taking the final hash value as a hash tree root node;

[0014] Integrating the first hash tree node, the second hash tree node and the hash tree root node to obtain the hash tree.

[0015] Optionally, before the alliance chain matches the transaction data according to the corresponding requirement attribute in the data requirement, it further comprises: performing differential privacy protection on the data of the data owner.

[0016] Optionally, the performing differential privacy protection on the data of the data owner comprises:

[0017] Obtaining a data attribute set of the data owner, and judging the category of the data attribute in the data attribute set;

[0018] Converting the data attribute set into a high-dimensional vector according to the category of the data attribute, wherein when the category of the data attribute in the data attribute set is a classification attribute, the data attribute is converted into a high-dimensional vector by using one-hot encoding; and when the category of the data attribute in the data attribute set is a numerical attribute, the data attribute is converted into a high-dimensional vector by using discretization processing;

[0019] Summarizing all the high-dimensional vectors to obtain a data attribute set vector of the data owner;

[0020] Selecting a preset size of a privacy budget, and adding noise to the data attribute set vector by using the privacy budget to obtain a scrambled data attribute set vector.

[0021] Optionally, the adding noise to the data attribute set by using the privacy budget to obtain the scrambled data attribute set vector comprises: calculating a noise scale parameter according to the privacy budget, and scrambling the data attribute set by using the noise obtained by sampling according to the noise scale parameter to obtain the scrambled data attribute set vector.

[0022] Optionally, after obtaining the scrambled data attribute set vector, further comprising:

[0023] mapping the scrambled data attribute set vector to a low-dimensional space using a random projection algorithm, and dividing the matching container according to the projection result;

[0024] Constructing a plurality of independent hash functions, and sequentially performing hash calculation on the scrambled data attribute set vector according to the random parameters in each independent hash function to obtain the matching hash value of the scrambled data attribute set vector;

[0025] The matching hash value is used as a key value, and the key value is associated with the scrambled data attribute set vector and stored in the matching container.

[0026] Optionally, the alliance chain matches the transaction data according to the corresponding demand attribute in the data demand, comprising:

[0027] The data buyer quantifies the data attribute of the data demand into a high-dimensional vector to obtain a demand data attribute vector;

[0028] The demand data attribute vector is hashed using the same plurality of independent hash functions as in the hash calculation of the scrambled data attribute set vector to obtain a demand data attribute vector hash value;

[0029] The alliance chain performs a matching operation in the matching container according to the demand data attribute vector hash value, and obtains the transaction data after the matching is completed.

[0030] In order to solve the above problems, the application also provides a system for a privacy-enhanced trusted data transaction method based on an alliance chain, comprising a key generation center, a data owner and a data buyer in communication with the key generation center respectively, a certificate authority, a third-party agency and an alliance chain in communication with the data owner and the data buyer, and a decentralized storage network in communication with the data owner.

[0031] Optionally, the key generation center distributes public keys and private keys to the data owner and the data buyer respectively; the key generation center communicates with the third-party agency and distributes public keys, private keys and test keys to the third-party agency.

[0032] Optionally, the data owner and the data buyer respectively submit their own identity information to the certificate authority communication, and the certificate authority communication verifies the identity information and issues digital certificates to the data owner and the data buyer respectively;

[0033] The data owner adds differential privacy noise to the quantized data attribute set to obtain a scrambled data attribute set vector, applies a hash function to generate a hash value of the scrambled data attribute set vector and uploads it to the alliance chain for recording, and the data buyer adds differential privacy noise to the quantized data attribute set to obtain a demand data attribute vector, applies a hash function to generate a hash value of the demand data attribute vector and uploads it to the alliance chain for recording.

[0034] The application can realize privacy protection of data in the data owner by calculating a shared key according to a system parameter list and calculating an encrypted ciphertext using the original data and the shared key, can realize distributed storage of the ciphertext by receiving the encrypted ciphertext using a decentralized storage network and generating a unique content identifier, can ensure the uniqueness of the data using the unique content identifier, can realize distributed storage of the ciphertext by dividing the encrypted ciphertext into multiple ciphertext fragments and recording them in the alliance chain, can improve the data storage reliability and traceability by recording using the alliance chain, and can protect the demand privacy and realize safe transaction of the data by matching the transaction data according to the corresponding demand attribute in the encrypted data demand and encrypting the data demand in the data buyer, and can improve the controllability of the data in the transaction process. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A flowchart of a privacy-enhanced trusted data transaction method based on an alliance chain provided by an embodiment of the application is shown.

[0036] Figure 2 A system diagram of a privacy-enhanced trusted data transaction method based on an alliance chain provided by an embodiment of the application is shown.

[0037] Figure 3 A flowchart of an optional embodiment of a privacy-enhanced trusted data transaction method based on an alliance chain provided by an embodiment of the application is shown.

[0038] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0039] It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0040] The embodiment of the application provides a privacy-enhanced trusted data transaction method based on a consortium chain. The execution subject of the privacy-enhanced trusted data transaction method based on the consortium chain includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the application. In other words, the privacy-enhanced trusted data transaction method based on the consortium chain can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be a stand-alone server or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0041] Referring to Figure 1 FIG. 1 is a flowchart of a privacy-enhanced trusted data transaction method based on a consortium chain provided by an embodiment of the application. In this embodiment, the privacy-enhanced trusted data transaction method based on the consortium chain includes the following steps.

[0042] S1, a key generation center initializes system parameters and generates a system parameter list.

[0043] In the embodiment of the application, the key generation center refers to a core entity responsible for generating, managing, and distributing encryption keys in a cryptography generation system, and is a trusted third-party institution or service.

[0044] Exemplarily, the key generation center initializes system parameters and generates a system parameter list by adopting the following implementation steps.

[0045] The key generation center (KGC) initializes system parameters, wherein the system parameters include a security parameter λ, the order p of a group, a group generator g, and a bilinear mapping e: G1x G1→ G T . The KGC selects a random number as a master key, wherein represents an integer set modulo p; a public parameter list PP=(p, G1, G T , e, g, H) is generated based on the master key, wherein G1 is a prime-order cyclic additive group, G T is a prime-order cyclic multiplicative group, e is a bilinear mapping, g is a generator, and H is a hash function.

[0046] S2, the data owner calculates a shared key according to a system parameter list, and calculates encrypted ciphertext by using the shared key and original data possessed by the data owner, and uploads the encrypted ciphertext to a decentralized storage network.

[0047] In the embodiment of the application, in actual implementation, the data owner represents a data owner device, and the data owner refers to an enterprise or an institution that possesses transaction data, for example, a medical institution, a school, and a transaction platform.

[0048] In the embodiment of the application, the original data refers to data possessed by the data owner, for example, medical data possessed by a medical institution as the data owner.

[0049] Exemplarily, the data owner can adopt the following implementation steps to calculate the shared key according to the system parameter list:

[0050] The data owner selects a random number in the encryption process wherein, represents an integer set modulo p, and a shared key K = e(g, g) is calculated s The original data in the data owner is D, C0 = SymEnc(K, D) is calculated, C1 = g s C2 = H(policy) s The encrypted ciphertext C = (C0, C1, C2, policy) is obtained, wherein C0 is symmetric encryption ciphertext, C1 is a public parameter of bilinear pair encryption, C2 is privacy policy ciphertext, and policy is an access policy.

[0051] In the embodiment of the application, the decentralized storage network refers to a storage mode in which data is stored in multiple independent nodes by distributed technology, including but not limited to IPFS (InterPlanetary File System, InterPlanetary File System).

[0052] S3, the decentralized storage network receives the encrypted ciphertext and generates a unique content identifier, and divides the encrypted ciphertext into multiple ciphertext fragments; a hash value of each ciphertext fragment is calculated, a hash tree is constructed by using the hash value, and a root node hash value corresponding to a root node of the hash tree is extracted; and the root node hash value and the unique content identifier are recorded in a consortium chain.

[0053] In the embodiment of the application, the unique content identifier refers to a mark used for uniquely identifying data, and the unique content identifier can be used to uniquely represent data and prevent data from being tampered with.

[0054] In the embodiment of the present application, the hash tree refers to a tree structure constructed by using the hash values of the ciphertext fragments, and a Merkle tree structure can be used, wherein the hash tree node includes the hash value of each ciphertext fragment and the hash value obtained by splicing the hash values of two adjacent hash tree nodes and then performing hash operation again.

[0055] As an embodiment of the present application, the hash tree is constructed by using the hash values, including:

[0056] Step 1: taking the hash value of each ciphertext fragment as a first hash tree node;

[0057] Step 2: taking the hash value obtained by splicing the hash values of two adjacent first hash tree nodes and then performing hash operation again as a second hash tree node;

[0058] Repeating steps 1-2 until the final hash value is generated, and taking the final hash value as a hash tree root node;

[0059] Integrating the first hash tree node, the second hash tree node and the hash tree root node to obtain the hash tree.

[0060] In the embodiment of the present application, the hash tree can be expressed as follows:

[0061] Step one: the ciphertext fragments are S1, S2,..., S n are hashed as H(S1), H(S2),..., H(S n ), as first hash tree nodes;

[0062] Step two: taking the hash value obtained by hash calculation after splicing H(S1) and H(S2) as a second hash tree node, wherein || is a splicing symbol;

[0063] Step three: repeating the splicing and merging process until only one hash value is left to obtain a hash tree root node.

[0064] In the embodiment of the present application, the alliance chain refers to a kind of blockchain jointly managed and maintained by a plurality of pre-selected trusted organizations, which balances transparency, security and efficiency through limited decentralization and permission control, and is suitable for scenarios requiring multi-party cooperation and having high requirements for privacy and compliance.

[0065] S4, the data buyer encrypts the data demand, and publishes the data demand through the alliance chain, the alliance chain matches the transaction data according to the corresponding demand attribute in the data demand, and after successful matching, uses a third-party agency to perform decryption operation to obtain the required transaction data.

[0066] In the embodiment of the present application, the data buyer refers to an entity such as an individual, an enterprise or an organization that obtains the right to use or ownership of data by payment in the data transaction ecosystem.

[0067] In the embodiment of the present application, the data requirement refers to data requirement information generated by a digital purchaser according to a purchase requirement.

[0068] As an embodiment of the present application, before the alliance chain matches transaction data according to the corresponding requirement attribute in the data requirement, the method further comprises performing differential privacy protection on data in the data owner.

[0069] Further, performing differential privacy protection on data in the data owner comprises:

[0070] Obtaining a data attribute set of the data owner, and judging the category of the data attribute in the data attribute set;

[0071] Converting the data attribute set into a high-dimensional vector according to the category of the data attribute, wherein when the category of the data attribute in the data attribute set is a classification attribute, the data attribute is converted into a high-dimensional vector by using one-hot encoding; and when the category of the data attribute in the data attribute set is a numerical attribute, the data attribute is converted into a high-dimensional vector by using discretization processing;

[0072] Summarizing all the high-dimensional vectors to obtain a data attribute set vector of the data owner;

[0073] Selecting a preset size of a privacy budget, and adding noise to the data attribute set vector by using the privacy budget to obtain a scrambled data attribute set vector.

[0074] Exemplarily, adding noise to the data attribute set vector by using the privacy budget can adopt the following implementation steps:

[0075] Defining a sensitivity △f, the sensitivity represents the maximum influence of the change of a single individual on the output, and selecting a privacy budget ε, the privacy budget controls the strength of privacy protection, a smaller ε provides stronger privacy protection, but reduces the matching accuracy, a larger ε can improve the matching accuracy, but reduces the privacy protection, calculating a noise scale parameter b from a Laplace distribution formula b=△f / ∈, sampling noise Noise from a Laplace distribution Laplace(0,b) i , adding noise V′ attr [i] to each dimension i of the attribute value V attr [i] = V attr [i] + Noise i , and finally obtaining the scrambled data attribute set vector V′ attr .

[0076] In this embodiment of the invention, one-hot encoding refers to a preprocessing technique that converts categorical variables into numerical forms. Each category is represented by a binary vector, ensuring that the algorithm can correctly process non-numerical data. Its core is to map the N possible values ​​of each categorical variable into an N-dimensional binary vector, where only one position is 1 (representing the current category), and the rest are 0.

[0077] Furthermore, noise is added to the data attribute set using a privacy budget to obtain a scrambled data attribute set vector, including: calculating a noise scale parameter based on the privacy budget, and using the noise obtained by sampling the noise scale parameter to scramble the data attribute set to obtain a scrambled data attribute set vector.

[0078] Furthermore, after obtaining the scrambled data attribute set vector, it also includes:

[0079] The scrambled data attribute set vector is mapped to a low-dimensional space using a random projection algorithm, and matching containers are divided according to the projection results;

[0080] Construct multiple independent hash functions, and perform hash calculations on the scrambled data attribute set vector in sequence according to the random parameters in each independent hash function to obtain the matching hash value of the scrambled data attribute set vector;

[0081] The matching hash value is used as the key, and the key is associated with the scrambled data attribute set vector and then stored in the matching container.

[0082] In this embodiment of the invention, a matching container refers to a data container in a hash table that stores data with the same hash key value.

[0083] For example, hashing the scrambled data attribute set vector sequentially based on the random parameters in each independent hash function can be implemented using the following steps:

[0084] Construct k independent hash functions, each corresponding to a different set of random parameters (r, μ), where r is the first random parameter and μ is the second random parameter. The k hash functions ultimately form a hash table. For the perturbed attribute vector V′ of the data owner... attr Apply all the hash functions in sequence to generate a set of hash values ​​H(V′). attr ),Right now H(V′ attr )=(H1(V′ attr ),H2(V′ attr ),…,H k (V′ attr )). H(V′) attr V′ is used as the key. attr Assign it to the corresponding matching container.

[0085] Further, the alliance chain matches the transaction data according to the corresponding demand attribute in the data demand, including:

[0086] The data buyer quantifies the data attribute of the data demand into a high-dimensional vector to obtain a demand data attribute vector;

[0087] The demand data attribute vector is hashed by using the same multiple independent hash functions in the hash calculation of the scrambled data attribute set vector to obtain a demand data attribute vector hash value;

[0088] The alliance chain performs a matching operation in the matching container according to the demand data attribute vector hash value, and obtains the transaction data after the matching is completed.

[0089] Exemplarily, the demand data attribute vector is hashed by using the same multiple independent hash functions in the hash calculation of the scrambled data attribute set vector, and the following implementation steps can be adopted:

[0090] The scrambled data attribute set vector V′ Q The same hash function as the data owner is applied to generate a group of hash values H(V′ Q ), H(V′ Q )=(H1(V′ Q ), H2(V′ Q ), …, H k (V′ Q )). The third-party proxy agency searches for the corresponding matching container in the hash table according to H(V′ Q ), and all vectors in the matching container are considered as potential matching objects. For each candidate vector V′ attr in the matching container, the Euclidean distance between V′ Q and V′ Q is calculated, A similarity threshold T is defined, and if Distance(V′ Q , V′ attr )≤T, it is considered that the matching is successful.

[0091] In the embodiment of the application, the third-party proxy agency refers to a neutral and trusted intermediary service entity.

[0092] Before the required transaction data is obtained by using the third-party proxy agency to perform the decryption operation in the embodiment of the application, the entity participating in the transaction further includes: submitting identity information to the certificate authority, verifying the real identity of the entity by the certificate authority, and issuing a digital certificate for the entity using the private key of the certificate authority.

[0093] As an embodiment of the application, after the matching is successful, the required transaction data is obtained by using the third-party proxy agency to perform the decryption operation, including:

[0094] The third-party agency verifies whether the data attributes of the data requirement of the data purchaser satisfy the access policy in the encrypted ciphertext according to the digital certificate issued by the certificate authority to the data purchaser;

[0095] After satisfying the access policy in the encrypted ciphertext, the third-party agency converts the encrypted ciphertext into a re-encrypted ciphertext and distributes the re-encrypted ciphertext to the data purchaser;

[0096] The data purchaser decrypts the transaction data by using the re-encrypted ciphertext to obtain the required transaction data.

[0097] In the embodiment of the present application, the access policy policy is expressed by a linear secret sharing scheme matrix (LSSS), and M is a l*n matrix, wherein l is the number of rows, n is the number of columns, and p(i) represents the attribute corresponding to the i-th row of the matrix. The attribute set W of the data purchaser is mapped to the row index of the LSSS matrix, and if there is a recovery coefficient ω such that ∑ i∈I ω i ·M i =(1,0,…,0), it is proved that the attribute set of the data purchaser satisfies the access policy, wherein I is the row index set satisfying the attribute.

[0098] In order to prevent the third-party agency from abusing the re-encryption key, the present application introduces a test key and a test function to ensure that the third party can only convert specific ciphertexts, as shown in the following steps:

[0099] Step 1, the key generation center constructs a t-1 degree polynomial f(x)=a0+a1x+a2x 2 +…+a t-1 x t-1 , wherein a0=α is the master key. For each encrypted ciphertext C, the key generation center randomly selects a unique point x C , and calculates the polynomial value y C =f(x C ). The key generation center selects a random number and calculates the test key , wherein H(C) is the hash tree root node H root of the ciphertext. Finally, the key generation center distributes TK C and x C to the third-party agency.

[0100] Step 2, since the polynomial value y C is needed when the test function is executed to verify the ciphertext, in order to verify the y CWhether the value is valid needs to be verified by Lagrange interpolation first. A set of auxiliary point values ​​(x1, y1), (x2, y2), ..., (x...) are provided by the key generation center. t ,y t ), where y i =f(x) i ). Calculated by a third-party agency Submit it to the system for verification.

[0101] Step 3, after confirming y C After verification, a test function is used for validation. (Comparison) and Are they equal, where TK C It is the test key, g is the group generator, and r is the test key. C PK is a random number generated by KGC for the encrypted ciphertext C, PK is the system public key, and H(C) in the right-hand equation is the hash value of the root node of the Merkle hash tree of the ciphertext stored in the consortium blockchain, which is bound to the encrypted ciphertext C. If they are equal, a third-party agent is allowed to re-encrypt the encrypted ciphertext C.

[0102] Step 4: After verification via the test function, the third-party agency can use the re-encryption key to convert the original ciphertext into re-encrypted ciphertext. The re-encryption key is... Among them SK owner It is the data owner's private key, PK buyer H is the public key of the data buyer, H is the hash function, and g is the group generator.

[0103] Step 5: The third-party agent first calculates the shared key. Using shared key K * Re-encrypt the ciphertext component C0 to generate a new ciphertext component C0. * =SymEnc(K * (C0), to obtain the final re-encrypted ciphertext C. RE =(C0) * (C1,C2,policy).

[0104] To ensure data privacy and prevent data buyers from reselling the data, this invention employs a Trusted Execution Environment (TEE) to achieve data availability without visibility. Data buyers can only access the data analysis results, not the original data. Therefore, after re-encryption, a third-party agency transmits the re-encrypted ciphertext to a trusted node designated by the data buyer.

[0105] Data buyers transmit their private keys to a trusted node via a secure channel, and the trusted node calculates the shared key. Then use the shared key K** Decryption of ciphertext component C0 * Obtain the symmetric encryption key K * = SymDec(K ** , C0 * ), the trusted node uses the symmetric encryption key K * decrypt the original data D = SymDec(K * , C0 ), after obtaining the original data, the data buyer uses the analysis program to analyze the data, and the analysis result is transmitted to the data buyer through the secure channel. The original data is always kept inside the trusted node during the process, ensuring the security of the data.

[0106] Further, after the data buyer obtains the required transaction data, the smart contract generates a payment signal: the node transmits the analysis result to the data buyer at the same time, and sends a payment trigger signal to the smart contract, signal = {Addr buyer , Addr owner , Addr proxy , Amount, IDtransaction, TimeStamp, Signature}, wherein Addr buyer represents the public key address of the data buyer, Addr owner represents the public key address of the data owner, Addr proxy represents the public key address of the third-party agency, Amount represents the total payment amount, ID transaction represents the unique identifier of the transaction, TimeStamp represents the transaction trigger time, and Signature is the signature of the data buyer on the payment trigger signal; after receiving the payment trigger signal, the smart contract first verifies the signal, verifies the Signature using the public key of the data buyer, ensures that the signal has not been tampered with; checks whether ID transaction already exists in the on-chain record to prevent replay attacks; verifies whether TimeStamp is within the valid time window to prevent expired signals; checks whether Addr buyer , Addr owner and Addr proxy are legal addresses; after the check is completed, the smart contract will calculate the amount of money each party should get according to the preset proportion of the split and call the relevant function to transfer the funds into the account addresses of the data owner and the third-party agency respectively, and write the payment-related transaction record details to the consortium chain.

[0107] The application can realize privacy protection of data in the data owner by calculating a shared key according to a system parameter list and calculating encrypted ciphertext by using the original data and the shared key, can realize distributed storage of the encrypted ciphertext by receiving the encrypted ciphertext and generating a unique content identifier by using a decentralized storage network, can ensure the uniqueness of the data by using the unique content identifier, can realize distributed storage of the encrypted ciphertext by dividing the encrypted ciphertext into multiple ciphertext fragments and recording the ciphertext fragments into a consortium chain, can improve the data storage reliability and traceability by recording the ciphertext fragments into the consortium chain, in addition, the consortium chain can match transaction data according to corresponding demand attributes in the encrypted data demand, can protect the demand privacy by encrypting the data demand in the data buyer, can realize safe transaction of the data, and can improve the controllability of the data in the transaction process.

[0108] Referring to Figure 2 As shown in the figure, it is a system schematic diagram of a privacy-enhanced trusted data transaction method based on a consortium chain provided by an embodiment of the application.

[0109] As an embodiment of the application, the system comprises a key generation center, a data owner and a data buyer in communication with the key generation center respectively, further comprises a certificate authority, a third-party agency and a consortium chain in communication with the data owner and the data buyer respectively, and further comprises a decentralized storage network in communication with the data owner.

[0110] As an embodiment of the application, the key generation center distributes public keys and private keys to the data owner and the data buyer respectively; the key generation center communicates with the third-party agency and distributes public keys, private keys and test keys to the third-party agency.

[0111] As an embodiment of the application, the data owner and the data buyer respectively submit their own identity information to the certificate authority, and the certificate authority verifies the identity information and respectively issues digital certificates to the data owner and the data buyer.

[0112] The data owner adds differential privacy noise to the quantized data attribute set to obtain a scrambled data attribute set vector, applies a hash function to generate a hash value of the scrambled data attribute set vector and uploads the hash value to the consortium chain for recording, and the data buyer adds differential privacy noise to the quantized data attribute set to obtain a demand data attribute vector, applies a hash function to generate a hash value of the demand data attribute vector and uploads the hash value to the consortium chain for recording.

[0113] Referring to Figure 3 As shown in the figure, it is a flowchart of an optional embodiment of a privacy-enhanced trusted data transaction method based on a consortium chain provided by an embodiment of the application.

[0114] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments but can be implemented in other embodiments without departing from the scope of the application.

[0115] The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the description given above, so that all changes coming within the meaning and equivalency range of the claims are intended to be embraced therein. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0116] Embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, obtain knowledge and use knowledge to obtain the best results.

[0117] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The plurality of units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names and not to indicate any particular order.

[0118] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A privacy-enhanced trusted data transaction method based on consortium blockchain, characterized in that: The method includes: The key generation center initializes system parameters and generates a list of system parameters; The data owner calculates a shared key based on the system parameter list, uses the original data and the shared key to calculate encrypted ciphertext, and uploads the encrypted ciphertext to a decentralized storage network. During the encryption process, the data owner selects a random number. ,in, Indicates modulus Given a set of integers, calculate the shared key. The original data owner is D, and the calculation is... , , ciphertext ,in, Ciphertext for symmetric encryption These are the common parameters for bilinear pair encryption. For privacy policy ciphertext, Access policy; The decentralized storage network receives encrypted ciphertext and generates a unique content identifier, then divides the encrypted ciphertext into multiple ciphertext fragments; calculates the hash value of each ciphertext fragment, constructs a hash tree using the hash values, and extracts the root node hash value corresponding to the root node of the hash tree; and records the root node hash value and the unique content identifier in the consortium blockchain. Data buyers encrypt their data requirements and publish these requirements through a consortium blockchain. The consortium blockchain matches transaction data based on the corresponding requirements attributes in the data requirements. Once a match is successful, a third-party agent is used to decrypt the data to obtain the required transaction data. Before using the third-party agent to decrypt the data, the process includes: the entity participating in the transaction submitting its identity information to a certificate authority, which verifies the entity's true identity and issues a digital certificate for the entity using its private key. The process of obtaining the required transaction data by decrypting the data after a successful match using a third-party agency includes: Third-party agencies verify whether the data attributes of the data purchaser's data requirements meet the access policy in the encrypted ciphertext based on the digital certificate issued by the certificate authority to the data purchaser. After satisfying the access policy in the encrypted ciphertext, the third-party agency converts the encrypted ciphertext into re-encrypted ciphertext and distributes the re-encrypted ciphertext to the data purchaser. Data buyers use re-encrypted ciphertext to decrypt transaction data and obtain the desired transaction data.

2. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 1, characterized in that, The construction of a hash tree using hash values ​​includes: Step 1: Use the hash value of each ciphertext fragment as the first hash tree node; Step 2: Concatenate the hash values ​​of two adjacent first hash tree nodes and perform another hash operation to obtain the hash value as the second hash tree node; Repeat steps 1-2 until the final hash value is generated, and use the final hash value as the root node of the hash tree; The hash tree is obtained by integrating the first hash tree node, the second hash tree node, and the hash tree root node.

3. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 1, characterized in that, Before the consortium blockchain matches transaction data according to the corresponding demand attributes in the data requirements, it also includes: performing differential privacy protection on the data among the data owners.

4. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 3, characterized in that, The implementation of differential privacy protection for data among data owners includes: Obtain the set of data attributes of the data owner and determine the category of the data attributes in the set of data attributes; The data attribute set is transformed into a high-dimensional vector according to the category of the data attribute. When the category of the data attribute in the data attribute set is a categorical attribute, one-hot encoding is used to transform the data attribute into a high-dimensional vector; when the category of the data attribute in the data attribute set is a numerical attribute, discretization is used to transform the data attribute into a high-dimensional vector. Summarize all high-dimensional vectors to obtain the data attribute set vector of the data owner; Select a preset privacy budget and use the privacy budget to add noise to the data attribute set vector to obtain a scrambled data attribute set vector.

5. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 4, characterized in that, The step of adding noise to the data attribute set using a privacy budget to obtain a scrambled data attribute set vector includes: calculating a noise scale parameter based on the privacy budget, and using the noise obtained by sampling the noise scale parameter to scramble the data attribute set to obtain a scrambled data attribute set vector.

6. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 4, characterized in that, After obtaining the scrambled data attribute set vector, the following is also included: The scrambled data attribute set vector is mapped to a low-dimensional space using a random projection algorithm, and matching containers are divided according to the projection results; Construct multiple independent hash functions, and perform hash calculations on the scrambled data attribute set vector in sequence according to the random parameters in each independent hash function to obtain the matching hash value of the scrambled data attribute set vector; The matching hash value is used as the key, and the key is associated with the scrambled data attribute set vector and then stored in the matching container.

7. The privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 1 or 6, characterized in that, The consortium blockchain matches transaction data based on the corresponding requirement attributes in the data requirements, including: Data buyers quantify the data attributes of their data requirements into high-dimensional vectors, resulting in a demand data attribute vector. The hash value of the required data attribute vector is obtained by using multiple independent hash functions that are the same as those used in hashing the scrambled data attribute set vector; The consortium blockchain performs a matching operation in the matching container based on the hash value of the required data attribute vector, and obtains the transaction data after the matching is completed.

8. A system based on the privacy-enhanced trusted data transaction method based on a consortium blockchain as described in any one of claims 1-7, characterized in that, It includes a key generation center, data owners and data buyers who communicate with the key generation center, certificate authorities, third-party agents and consortium blockchains that communicate with both the data owners and the data buyers, and a decentralized storage network that communicates with the data owners.

9. The system of the privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 8, characterized in that, The key generation center distributes public and private keys to data owners and data purchasers respectively; the key generation center communicates with third-party agents and distributes public, private, and test keys to third-party agents.

10. The system of the privacy-enhanced trusted data transaction method based on consortium blockchain as described in claim 8, characterized in that, The data owner and the data purchaser each submit their own identity information to the Certificate Authority Communication, which verifies the identity information and issues digital certificates to the data owner and the data purchaser respectively. The data owner quantizes the data attribute set, adds differential privacy noise to obtain a scrambled data attribute set vector, applies a hash function to generate the hash value of the scrambled data attribute set vector, and uploads it to the consortium blockchain for recording. The data buyer quantizes the data attribute set, adds differential privacy noise to obtain a demand data attribute vector, applies a hash function to generate the hash value of the demand data attribute vector, and uploads it to the consortium blockchain for recording.

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