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

Abstract

The invention relates to a data transaction technology, and discloses a privacy enhanced trusted data transaction method based on an alliance chain, which comprises the following steps: a key generation center generates a system parameter list; the data owner calculates a shared key according to the system parameter list, calculates an 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 a plurality of ciphertext fragments; calculating a root node hash value, and recording the root node hash value and the unique content identifier into an alliance chain; and the data purchaser encrypts the data demand and issues the data demand through the alliance chain, and the alliance chain matches the transaction data according to the corresponding demand attribute in the data demand and performs decryption operation to obtain the required transaction data. The invention further provides a system of the privacy enhanced trusted data transaction method based on the alliance chain, the secure transaction of the data can be realized, and the controllability of the data in the transaction process is improved.

Description

Technical Field

[0001] The present invention relates to the field of data transaction technology, and in particular to a privacy-enhanced trusted data transaction method and system based on an alliance chain. Background Art

[0002] Under the wave of digital economy and data factor transformation, data trading has become the core driving force for promoting business intelligence, precision marketing and cross-domain collaboration. As a new production factor, the release of data value depends on a safe, efficient and controllable circulation mechanism. However, the data trading process faces multiple challenges, such as illegal intrusion of malicious nodes, the risk of leakage of sensitive data, the contradiction between transaction efficiency and privacy protection, and the secondary resale of data, which seriously restrict the safe circulation and value release of data elements.

[0003] Traditional centralized data trading platforms rely on trusted third parties to host data, which has single points of failure and privacy exposure risks; and existing decentralized solutions are still insufficient in terms of privacy protection granularity, transaction efficiency and permission control. In recent years, blockchain technology has provided a new technical path for data trading with its decentralized, tamper-proof and traceable characteristics. However, the openness of the public chain is difficult to meet the high requirements of enterprises for data privacy, while the closed nature of the private chain limits the cross-domain circulation of data. As a compromise solution, the alliance chain strikes a balance between efficiency and security through access mechanisms and consensus algorithms. However, only using the alliance chain to achieve privacy protection in data transactions will result in insufficient privacy protection and make the data less controllable, making it difficult to effectively control the data during the transaction process. Summary of the invention

[0004] The present invention provides a privacy-enhanced trusted data transaction method and system based on an alliance chain, which can realize secure data transactions and improve the controllability of data during the transaction process.

[0005] To achieve the above purpose, the present invention 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 the shared key based on the system parameter list, and uses the original data and shared key to calculate the encrypted ciphertext, and uploads the encrypted ciphertext to the 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 shards; calculates the hash value of each ciphertext shard, uses the hash value to build a hash tree, 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 in the alliance chain;

[0009] The data purchaser encrypts the data requirements and publishes the data requirements through the consortium blockchain. The consortium blockchain matches the transaction data according to the corresponding requirement attributes in the data requirements. After successful matching, a third-party agency is used to perform decryption operations to obtain the required transaction data.

[0010] Optionally, constructing the hash tree using the hash value includes:

[0011] Step 1: Use the hash value of each ciphertext shard as the first hash tree node;

[0012] Step 2: Concatenate the hash values of two adjacent first hash tree nodes and then perform a hash operation again to obtain the hash value as the second hash tree node;

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

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

[0015] Optionally, before the consortium blockchain matches the transaction data according to the corresponding requirement attributes in the data requirements, it also includes: performing differential privacy protection on the data in the data owner.

[0016] Optionally, performing differential privacy protection on the data in the data owner includes:

[0017] Obtain the data attribute set of the data owner and determine the category of the data attributes in the data attribute set;

[0018] Convert the data attribute set into a high-dimensional vector according to the category of the data attributes. Among them, when the category of the data attributes in the data attribute set is a categorical attribute, one-hot encoding is used to convert the data attributes into a high-dimensional vector; when the category of the data attributes in the data attribute set is a numerical attribute, discretization processing is used to convert the data attributes into a high-dimensional vector;

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

[0020] Select a privacy budget of a preset size and add noise to the data attribute set vector using the privacy budget to obtain a scrambled data attribute set vector.

[0021] Optionally, adding noise to the data attribute set using the privacy budget to obtain a scrambled data attribute set vector includes: calculating a noise scale parameter according to the privacy budget and scrambling the data attribute set with the noise sampled using the noise scale parameter to obtain a scrambled data attribute set vector.

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

[0023] Mapping the scrambled data attribute set vector to a low-dimensional space by using a random projection algorithm, and dividing matching containers according to the projection results;

[0024] Constructing multiple independent hash functions, and successively performing hash calculations on the scrambled data attribute set vector according to the random parameters in each independent hash function to obtain the matching hash values of the scrambled data attribute set vector;

[0025] Taking the matching hash values as key values, associating the key values with the scrambled data attribute set vector, and storing them in the matching containers.

[0026] Optionally, the consortium blockchain matches transaction data according to the corresponding demand attributes in the data requirements, including:

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

[0028] Using the same multiple independent hash functions for hash calculation of the scrambled data attribute set vector to perform hash calculation on the demand data attribute vector to obtain the hash value of the demand data attribute vector;

[0029] The consortium blockchain performs matching operations in the matching containers according to the hash value of the demand data attribute vector, and obtains transaction data after the matching is completed.

[0030] To solve the above problems, the present invention also provides a system for a privacy-enhanced trusted data transaction method based on a consortium blockchain, including a key generation center, a data owner and a data purchaser respectively communicating with the key generation center, and further including a certificate issuing agency, a third-party agency and a consortium blockchain respectively communicating with the data owner and the data purchaser, and further including a decentralized storage network communicating with the data owner.

[0031] Optionally, the key generation center distributes public keys and private keys to the data owner and the data purchaser 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 purchaser respectively submit their own identity information to communicate with the certificate issuing agency, and the certificate issuing agency communicates to verify the identity information and issues digital certificates to the data owner and the data purchaser respectively;

[0033] The data owner quantifies the data attribute set and adds differential privacy noise 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 consortium blockchain for recording. The data purchaser quantifies the data attribute set and adds differential privacy noise to obtain a required data attribute vector, applies a hash function to generate a hash value of the required data attribute vector, and uploads it to the consortium blockchain for recording.

[0034] In the present invention, the data owner calculates a shared key according to the system parameter list, and uses the original data and the shared key to calculate the encrypted ciphertext, so as to realize the privacy protection of the data in the data owner. The decentralized storage network is used to receive the encrypted ciphertext and generate a unique content identifier. The unique content identifier can ensure the uniqueness of the data. The encrypted ciphertext is split into multiple ciphertext shards and recorded in the consortium blockchain, which can realize the distributed storage of the ciphertext, save storage resources, and using the consortium blockchain for recording can improve the reliability and traceability of data storage. In addition, the consortium blockchain matches the transaction data according to the corresponding required attributes in the encrypted data requirements, encrypts the data requirements of the data purchaser, can protect the privacy of the requirements, and realizes the secure transaction of the data, improving the controllability of the data in the transaction process. Brief Description of the Drawings

[0035] Figure 1 It is a schematic flowchart of a privacy-enhanced trusted data transaction method based on a consortium blockchain provided by an embodiment of the present invention;

[0036] Figure 2 It is a schematic system diagram of a privacy-enhanced trusted data transaction method based on a consortium blockchain provided by an embodiment of the present invention;

[0037] Figure 3 It is a schematic flowchart of an alternative embodiment of a privacy-enhanced trusted data transaction method based on a consortium blockchain provided by an embodiment of the present invention

[0038] The implementation, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed Embodiments

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

[0040] An embodiment of the present application provides a privacy-enhanced trusted data trading method based on a consortium blockchain. The execution entities of the privacy-enhanced trusted data trading method based on a consortium blockchain include, but are not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the privacy-enhanced trusted data trading method based on a consortium blockchain can be executed by software or hardware installed on 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, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0041] Referring to Figure 1 As shown, it is a schematic flowchart of a privacy-enhanced trusted data trading method based on a consortium blockchain provided by an embodiment of the present invention. In this embodiment, the privacy-enhanced trusted data trading method based on a consortium blockchain includes:

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

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

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

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

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

[0047] In the embodiments of the present invention, in actual implementation, the data owner represents the data owner device. The data owner refers to an enterprise or institution that owns transaction data, such as medical institutions, schools, and trading platforms, etc.

[0048] In the embodiments of the present invention, the original data refers to the data held by the data owner. For example, the medical data held by a medical institution as the data owner.

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

[0050] The data owner selects a random number during the encryption process Among them, represents the set of integers with modulus p, and calculates the shared key K = e(g, g) s , the original data in the data owner is D, calculates C0 = SymEnc(K, D), C1 = g s , C2 = H(policy) s , the encrypted ciphertext C = (C0, C1, C2, policy), where C0 is the ciphertext of symmetric encryption, C1 is the public parameter of bilinear pair encryption, C2 is the ciphertext of the privacy policy, and policy is the access policy.

[0051] In the embodiments of the present invention, the decentralized storage network refers to a storage mode that disperses data storage on multiple independent nodes through distributed technology, including but not limited to IPFS (InterPlanetary File System).

[0052] S3. The decentralized storage network receives the encrypted ciphertext and generates a unique content identifier, and splits the encrypted ciphertext into multiple ciphertext shards; calculates the hash value of each ciphertext shard, constructs a hash tree using the hash values, 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 into the consortium blockchain.

[0053] In the embodiments of the present invention, the unique content identifier refers to a tag used to uniquely identify data, and the unique content identifier can be used to uniquely represent data and prevent data from being tampered with.

[0054] In an embodiment of the present invention, a hash tree refers to a tree structure constructed using the hash values of ciphertext shards, and a Merkle tree structure can be adopted. Among them, a hash tree node includes the hash value of each ciphertext shard and the hash value obtained by concatenating the hash values of two adjacent hash tree nodes and then performing a hash operation again.

[0055] As an embodiment of the present invention, constructing a hash tree using hash values includes:

[0056] Step 1: Take the hash value of each ciphertext shard as the first hash tree node;

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

[0058] Repeat steps 1-2 until the final hash value is generated, and take the final hash value as the root node of the hash tree;

[0059] Integrate the first hash tree nodes, the second hash tree nodes, and the root node of the hash tree to obtain the hash tree.

[0060] In an embodiment of the present invention, the hash tree can be represented by the following expression:

[0061] Step 1: The ciphertext shards are S1, S2,..., S n Are hashed to H(S1), H(S2),..., H(S n ), as the first hash tree nodes;

[0062] Step 2: H(H(S1)||H(S2)) is the hash value obtained by concatenating H(S1) and H(S2) and then performing a hash calculation as the second hash tree node, where || is the concatenation symbol;

[0063] Step 3: Repeat the concatenation and merging process until only one hash value remains to obtain the root node of the hash tree.

[0064] In an embodiment of the present invention, a consortium blockchain refers to a blockchain jointly managed and maintained by multiple pre-selected trusted organizations. Through limited decentralization and permission control, it balances transparency, security, and efficiency, and is applicable to scenarios that require multi-party collaboration and have high requirements for privacy and compliance.

[0065] S4. The data purchaser encrypts the data requirements and publishes the data requirements through the consortium blockchain. The consortium blockchain matches the transaction data according to the corresponding requirement attributes in the data requirements. After successful matching, a third-party agency is used to perform a decryption operation to obtain the required transaction data.

[0066] In an embodiment of the present invention, a data purchaser refers to an entity that obtains the right to use or ownership of data through payment in the data trading ecosystem, such as an individual, enterprise, or organization.

[0067] In the embodiments of the present invention, the data requirement refers to the data requirement information generated by digital purchasers according to their purchase requirements.

[0068] As an embodiment of the present invention, before the consortium blockchain matches transaction data according to the corresponding requirement attributes in the data requirement, it further includes: performing differential privacy protection on the data of the data owners.

[0069] Further, performing differential privacy protection on the data of the data owners includes:

[0070] Obtaining the data attribute set of the data owner and determining the category of the data attributes in the data attribute set;

[0071] Converting the data attribute set into a high-dimensional vector according to the category of the data attributes. Among them, when the category of the data attributes in the data attribute set is a categorical attribute, one-hot encoding is used to convert the data attributes into a high-dimensional vector; when the category of the data attributes in the data attribute set is a numerical attribute, discretization processing is used to convert the data attributes into a high-dimensional vector;

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

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

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

[0075] Define the sensitivity △f, where the sensitivity represents the maximum impact of the change of a single individual on the output, and select the privacy budget ε. The privacy budget controls the strength of privacy protection. A smaller ε provides stronger privacy protection but reduces the matching accuracy, while a larger ε can improve the matching accuracy but reduces the privacy protection. Calculate the noise scale parameter b according to the Laplace distribution formula b = Δf / ∈, and sample the noise Noise from the Laplace distribution Laplace(0, b) i , for the attribute value V attr [i] add noise V′ attr [i] = V attr [i] + Noise i , and finally obtain the scrambled data attribute set vector V′ attr .

[0076] In the embodiments of the present invention, one-hot encoding refers to a preprocessing technique for converting categorical variables into numerical forms. Each category is represented by a binary vector to ensure that the algorithm can correctly process non-numerical data. The core lies in mapping the N possible values of each categorical variable to an N-dimensional binary vector, where only one position is 1 (representing the current category) and the rest are 0.

[0077] Further, noise is added to the data attribute set using the privacy budget to obtain a scrambled data attribute set vector, including: calculating the noise scale parameter according to the privacy budget, and scrambling the data attribute set using the noise sampled with the noise scale parameter to obtain the scrambled data attribute set vector.

[0078] Further, after obtaining the scrambled data attribute set vector, it further includes:

[0079] Mapping the scrambled data attribute set vector to a low-dimensional space using the random projection algorithm, and dividing the matching containers according to the projection results;

[0080] Constructing multiple independent hash functions, and sequentially performing hash calculations on the scrambled data attribute set vector according to the random parameters in each independent hash function to obtain the matching hash values of the scrambled data attribute set vector;

[0081] Using the matching hash values as key values, associating the key values with the scrambled data attribute set vector and storing them in the matching containers.

[0082] In the embodiments of the present invention, a matching container refers to a data container in a hash table that stores data with the same hash key values.

[0083] Exemplarily, sequentially performing hash calculations on the scrambled data attribute set vector according to the random parameters in each independent hash function can adopt the following implementation steps:

[0084] Construct k independent hash functions, each hash function corresponding to a different set of random parameters (r, μ), r being the first random parameter and μ being the second random parameter. The k hash functions finally form a hash table. For the attribute vector V′ of the perturbed data owner attr Apply all the hash functions in sequence to generate a set of hash values H(V′ attr ), that is H(V′ attr ) = (H1(V′ attr ), H2(V′ attr ), …, H k (V′ attr ))). Take H(V′ attr ) as the key value and assign V′ attr to the corresponding matching container.

[0085] Furthermore, the consortium blockchain matches transaction data according to the corresponding requirement attributes in the data requirements, including:

[0086] The data purchaser quantifies the data attributes of the data requirements into a high-dimensional vector to obtain a required data attribute vector;

[0087] Use multiple independent hash functions that are the same in the hash calculation of the scrambled data attribute set vector to perform hash calculation on the required data attribute vector to obtain the hash value of the required data attribute vector;

[0088] The consortium blockchain performs a matching operation in the matching container according to the hash value of the required data attribute vector, and obtains transaction data after the matching is completed.

[0089] Exemplarily, using multiple independent hash functions that are the same in the hash calculation of the scrambled data attribute set vector to perform hash calculation on the required data attribute vector, the following implementation steps can be adopted:

[0090] For the scrambled data attribute set vector V′ Q Apply the same hash function as the data owner to generate a set of hash values H(V′ Q ), H(V′ Q ) = (H1(V′ Q ), H2(V′ Q ), …, H k (V′ Q ))). The third-party agency looks up the corresponding matching container in the hash table according to H(V′ Q ), and all vectors in the matching container are considered potential matching objects. For each candidate vector V′ attr in the matching container, calculate its Euclidean distance from V′ Q , Define a similarity threshold T. If Distance(V′ Q , V′ attr ) ≤ T, it is considered that the matching is successful.

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

[0092] Before using the third-party agency to perform decryption operations to obtain the required transaction data in the embodiment of the present invention, it further includes: the entities participating in the transaction submit their identity information to the certificate authority, and the certificate authority verifies the true identities of the entities and uses the private key of the certificate authority to issue digital certificates to the entities.

[0093] As an embodiment of the present invention, after the matching is successful, using the third-party agency to perform decryption operations to obtain the required transaction data includes:

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

[0095] After the access policy in the encrypted ciphertext is satisfied, 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 using the re - encrypted ciphertext to obtain the required transaction data.

[0097] In the embodiment of the present invention, the access policy policy is represented by a linear secret sharing scheme matrix (LSSS). Let M be an l×n matrix, where l is the number of rows and n is the number of columns, and ρ(i) represents the attribute corresponding to the i - th row of the matrix. Map the set of required attributes W of the data purchaser to the row indices of the LSSS matrix. If there exists a recovery coefficient ω such that ∑ i∈I ω i ·M i =(1,0,…,0), it is proved that the set of attributes of the data purchaser satisfies the access policy, where I is the set of row indices that satisfy the attributes.

[0098] In the embodiment of the present invention, in order to prevent the third - party agency from misusing the re - encryption key, this scheme 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 polynomial f(x)=a0 + a1x + a2x 2 +…+a t-1 x t-1 , where 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 where H(C) is the root node H of the hash tree of the ciphertext root . 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 required when executing the test function for ciphertext verification, in order to verify the y CWhether the value is legal needs to be verified by Lagrange interpolation first. The key generation center provides a set of auxiliary point values \((x1,y1),(x2,y2),…,(x t ,y t ), where y i =f(x i ). The third-party agency calculates and submits it to the system for verification.

[0101] Step 3: After confirming that y C is legal, perform a test function verification. Compare with to see if they are equal, where TK C is the test key, g is the group generator, r C is a random number generated by the KGC for the encrypted ciphertext C, PK is the system public key, and in the right equation, H(C) is the hash value of the Merkle hash tree root node of the ciphertext stored in the consortium chain, which is bound to the encrypted ciphertext C. If they are equal, the third-party agency is allowed to perform a re-encryption operation on the encrypted ciphertext C.

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

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

[0104] In the embodiment of the present invention, in order to ensure the privacy of data and prevent problems such as the data purchaser reselling the data secondarily, this solution combines a trusted execution environment (TEE) to achieve data availability without visibility. The data purchaser can only obtain the data analysis results and cannot access the original data. Therefore, after re-encryption is completed, the third-party agency transmits the re-encrypted ciphertext to the trusted node specified by the data purchaser.

[0105] The data purchaser transmits his private key to the trusted node through a secure channel. The trusted node calculates the shared key and then uses the shared key K** Decrypt the ciphertext component C0 * Obtain the symmetric encryption key K * = SymDec(K ** , C0 * ), and the trusted node uses the symmetric encryption key K * to decrypt the original data D = SymDec(K * , C0). After obtaining the original data, the data purchaser uses an analysis program to analyze the data, and the analysis result is transmitted to the data purchaser through a secure channel. During the process, the original data always remains inside the trusted node, ensuring the security of the data.

[0106] Furthermore, after the data purchaser obtains the required transaction data, a payment signal is generated using a smart contract: when the node transmits the analysis result to the data purchaser, a payment trigger signal is sent to the smart contract, signal = {Addr buyer , Addr owner , Addr proxy , Amount, IDtransaction, TimeStamp, Signature}, where Addr buyer represents the public key address of the data purchaser, 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 transaction identifier, TimeStamp represents the transaction trigger time, and Signature is the signature of the data purchaser for the payment trigger signal; after receiving the payment trigger signal, the smart contract first performs signal verification, uses the public key of the data purchaser to verify Signature to ensure that the signal has not been tampered with; checks whether ID transaction already exists in the 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, the smart contract will calculate the due amounts of each participating party according to the preset sharing ratio and call relevant functions to transfer the funds to the account addresses of the data owner and the third-party agency respectively, and write the details of the payment-related transaction record into the consortium blockchain.

[0107] In the present invention, the data owner calculates a shared key according to a system parameter list, and uses the original data and the shared key it owns to calculate an encrypted ciphertext, which can achieve privacy protection for the data in the data owner. The decentralized storage network receives the encrypted ciphertext and generates a unique content identifier. Using the unique content identifier can ensure the uniqueness of the data. The encrypted ciphertext is split into multiple ciphertext shards and recorded in the consortium blockchain, which can achieve distributed storage of the ciphertext, save storage resources, and using the consortium blockchain for recording can improve the reliability and traceability of data storage. In addition, the consortium blockchain matches transaction data according to the corresponding demand attributes in the encrypted data demand, encrypts the data demand of the data purchaser, can protect the demand privacy, and achieve secure data transactions, improving the controllability of data during the transaction process.

[0108] Refer to Figure 2 As shown, it is a system schematic diagram of a privacy-enhanced trusted data trading method based on a consortium blockchain provided by an embodiment of the present invention.

[0109] As an embodiment of the present invention, the system includes a key generation center, a data owner and a data purchaser that communicate with the key generation center respectively, and also includes a certificate issuing authority, a third-party agency and a consortium blockchain that communicate with both the data owner and the data purchaser, and also includes a decentralized storage network that communicates with the data owner.

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

[0111] As an embodiment of the present invention, the data owner and the data purchaser submit their own identity information to communicate with the certificate issuing authority, and the certificate issuing authority communicates to verify the identity information and issues digital certificates to the data owner and the data purchaser respectively;

[0112] The data owner quantifies the data attribute set and 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, and the data purchaser quantifies the data attribute set and 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.

[0113] Refer to Figure 3 As shown, it is a flowchart of an optional embodiment of a privacy-enhanced trusted data trading method based on a consortium blockchain provided by an embodiment of the present invention.

[0114] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0115] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced by the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0116] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

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

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A privacy-enhanced trusted data trading method based on a consortium blockchain, characterized in that The method includes: The key generation center initializes the system parameters and generates a system parameter list; The data owner calculates the shared key according to the system parameter list, calculates the encrypted ciphertext using the original data it owns and the shared key, and uploads the encrypted ciphertext to the decentralized storage network; The decentralized storage network receives the encrypted ciphertext and generates a unique content identifier, and splits the encrypted ciphertext into multiple ciphertext shards; calculates the hash value of each ciphertext shard, constructs a hash tree using the hash values, 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 in the consortium blockchain; The data purchaser encrypts the data requirement and publishes the data requirement through the consortium blockchain. The consortium blockchain matches the transaction data according to the corresponding requirement attributes in the data requirement. After successful matching, a third-party agency is used for decryption operation to obtain the required transaction data.

2. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 1, wherein The constructing the hash tree using the hash values includes: Step 1: Take the hash value of each ciphertext shard as the first hash tree node; Step 2: Take the hash value obtained by concatenating the hash values of two adjacent first hash tree nodes and then performing a hash operation again as the second hash tree node; Repeat steps 1-2 until the final hash value is generated, and take the final hash value as the root node of the hash tree; Integrate the first hash tree nodes, the second hash tree nodes, and the root node of the hash tree to obtain the hash tree.

3. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 1, wherein, Before the consortium blockchain matches the transaction data according to the corresponding requirement attributes in the data requirement, it also includes: performing differential privacy protection on the data in the data owner.

4. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 3, wherein, The performing differential privacy protection on the data in the data owner includes: Obtain the data attribute set of the data owner and judge the category of the data attributes in the data attribute set; Convert the data attribute set into a high-dimensional vector according to the category of the data attributes. Among them, when the category of the data attributes in the data attribute set is a categorical attribute, the one-hot encoding is used to convert the data attributes into a high-dimensional vector; when the category of the data attributes in the data attribute set is a numerical attribute, the data attributes are converted into a high-dimensional vector by using discretization processing; Summarize all high-dimensional vectors to obtain the data attribute set vector of the data owner; Select a privacy budget of a preset size and add noise to the data attribute set vector using the privacy budget to obtain a scrambled data attribute set vector.

5. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 4, wherein, The adding noise to the data attribute set using the privacy budget to obtain a scrambled data attribute set vector includes: calculating the noise scale parameter according to the privacy budget, and scrambling the data attribute set using the noise sampled by the noise scale parameter to obtain a scrambled data attribute set vector.

6. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 4, wherein, After obtaining the scrambled data attribute set vector, it also includes: Map the scrambled data attribute set vector to a low-dimensional space using the random projection algorithm and divide the matching containers according to the projection result; Construct multiple independent hash functions, and sequentially perform hash calculations on the scrambled data attribute set vector according to the random parameters in each independent hash function to obtain the matching hash values of the scrambled data attribute set vector; Take the matching hash values as key values, associate the key values with the scrambled data attribute set vector, and store them in the matching containers.

7. The privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 1 or 6, characterized in that, The consortium blockchain matches transaction data according to the corresponding requirement attributes in the data requirements, including: The data purchaser quantifies the data attributes of the data requirements into high-dimensional vectors to obtain a demand data attribute vector; Using multiple independent hash functions that are the same in the hash calculation of the scrambled data attribute set vector to perform hash calculation on the demand data attribute vector to obtain the hash value of the demand data attribute vector; The consortium blockchain performs a matching operation in the matching container according to the hash value of the demand data attribute vector, and obtains transaction data after the matching is completed.

8. A system for a privacy-enhanced trusted data trading method based on a federated blockchain according to any one of claims 1-7, characterized in that, It includes a key generation center, a data owner and a data purchaser that communicate with the key generation center respectively, and also includes a certificate issuing authority, a third-party agency and a consortium blockchain that communicate with both the data owner and the data purchaser, and also includes a decentralized storage network that communicates with the data owner.

9. The system of the privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 8, wherein, The key generation center distributes public keys and private keys to the data owner and the data purchaser 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.

10. The system of the privacy-enhanced trusted data trading method based on the consortium blockchain according to claim 8, characterized in that, The data owner and the data purchaser submit their own identity information to the certificate issuing authority for communication, and the certificate issuing authority verifies the identity information and issues digital certificates for the data owner and the data purchaser respectively; The data owner quantifies the data attribute set and 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, and the data purchaser quantifies the data attribute set and 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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