Hierarchical collaborative catering data sharing system based on block chain and privacy calculation

Abstract

The invention provides a hierarchical collaborative catering data sharing system based on a block chain and privacy calculation, and aims to solve the problems that a dynamic key is difficult to update, an access control strategy lags behind and privacy protection intensity is inconsistent. According to the data security sharing and updating method based on the core layer-service layer-privacy layer three-layer collaborative architecture, a graph neural network optimization model and a dynamic key management mechanism are fused, and collaborative optimization of data encryption, access control and privacy calculation is achieved. By introducing a federal CP-ABE strategy matching mechanism and a hierarchical zero-knowledge proof protocol, a data sharing platform capable of adapting to service changes is constructed, the compatibility and safety of the system in a heterogeneous environment are improved, and the data sharing efficiency and compliance of catering enterprises in a multi-party participation and multi-role dynamic access scene are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of data sharing and data security, and particularly relates to a hierarchical collaborative catering data sharing system based on blockchain and privacy computing. Background Art

[0002] With the continuous advancement of the digital transformation of the catering industry, a large amount of data has emerged in the catering industry, such as order data, supply chain data, user data, etc. Catering data has become a key asset driving service optimization and precise decision-making. However, the data generated by multi-source heterogeneous systems is usually scattered on different platforms, lacking a unified standard, and there are data islands among various catering enterprises, resulting in problems such as high data sharing costs, high security risks, and high compliance difficulties.

[0003] Currently, traditional data sharing mostly adopts static key encryption and fixed policy control mechanisms, which have potential risks such as lagging updates, key leakage, and policy configuration errors, and cannot effectively meet the real-time sharing and dynamic access requirements of catering data. At the same time, in the face of complex sharing scenarios involving multiple roles, the catering data sharing platform cannot accurately define access permissions and boundary behaviors, and there are problems such as out-of-range access and data redundancy, which limit the in-depth exploration of data value.

[0004] Blockchain technology, with its immutability and chain structure, provides guarantees for trusted data storage and interaction in a distributed environment; privacy computing technology can support the encryption processing and joint calculation of sensitive data locally based on mechanisms such as symmetric encryption and zero-knowledge proof on the premise that the original data is not exposed, so as to achieve cross-organization data collaborative analysis.

[0005] Therefore, there is an urgent need for a catering data sharing system that integrates blockchain and privacy computing to solve the problems of difficult dynamic key update, lagging access control policy, and inconsistent privacy protection intensity. Especially in the catering industry, different data has different sensitive levels, and different participants have different access requirements. It is necessary to adjust encryption and policy configuration according to the business status to ensure the effective flow and efficient sharing of catering data. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a hierarchical collaborative catering data sharing system based on blockchain and privacy computing, aiming to solve the problems of difficult dynamic key update, lagging access control policies, and inconsistent privacy protection intensity. The present invention is based on a data security sharing and updating method for a three-layer collaborative architecture of a core layer - business layer - privacy layer, which integrates a graph neural network optimization model and a dynamic key management mechanism to achieve the collaborative optimization of data encryption, access control, and privacy computing. By introducing a federated CP-ABE policy matching mechanism and a hierarchical zero-knowledge proof protocol, a data sharing platform adaptable to business changes is constructed, improving the compatibility and security of the system in heterogeneous environments, and ensuring the data sharing efficiency and compliance of catering enterprises in scenarios of multi-party participation and multi-role dynamic access.

[0007] A hierarchical collaborative catering data sharing system based on blockchain and privacy computing, comprising:

[0008] Step 1: Collect and preprocess catering data;

[0009] In the catering data sharing scenario, there are a large amount of catering data. First, collect catering data through Internet of Things devices and catering management systems. Secondly, preprocess the catering data. Finally, standardize the data to ensure the unity of data format.

[0010] The specific operation process is designed as follows:

[0011] In the catering data sharing scenario, collect real-time data through Internet of Things devices and catering management systems, including order data D order,i ={O1, O2,..., O n}, inventory data D stock,i ={S1, S2,..., S m}, supply chain data D supply,i ={P1, P2,..., P l}, and customer feedback data D review,i ={R1, R2,..., R k}, etc. After collecting the catering data, preprocess the data. First, perform data cleaning, remove noise data, and handle missing values. Secondly, standardize the data to ensure the unity of data format. Finally, extract features from the catering data. The data X i is converted into a standard format, X i =f(D i )={x1, x2,..., x p}, where X i represents the standard data set obtained after preprocessing by catering enterprise R i , including customer preferences, dish sales volume, inventory turnover rate, etc.

[0012] Step 2: Design of Hierarchical Collaborative Catering Data Sharing Architecture

[0013] In this step, a hierarchical collaborative system architecture is constructed, which is divided into three major modules: the core layer, the business layer, and the privacy layer. Among them, the core layer is responsible for the encryption protection of raw data and key generation, and constructs an encrypted mapping and dynamic key update model for catering data; the business layer is oriented to sharing control and policy matching, executes access verification and decryption control, and coordinates the strategic game and interest balance between data providers and data consumers; the privacy layer introduces zero-knowledge proof technology to achieve privacy protection of different data according to the sensitivity of catering data.

[0014] The specific operation process design is as follows:

[0015] In the catering data sharing scenario, for the core layer, the invention constructs a dynamic key based on HMAC-SM3, and derives the key in combination with the participant attributes and time window. The key derivation function is:

[0016]

[0017] Among them, K master is the 128-bit master key, A = {a1, a2,..., a n} is the set of participant attributes (such as catering enterprise ID, takeaway platform permissions, etc.), Concat(A) represents concatenating the attributes into a binary string in lexicographical order, Hash(t) is the hash value of the time window t, and in this invention, SHA-256 is used and truncated to 128 bits. K session is the finally generated session key.

[0018] At the same time, to ensure the security of catering data, the key is rotated regularly. The invention defines the key life cycle as the time window t. When t expires, the key is re-derived:

[0019] K (t+1) session = HMAC-SM3(K (t) session , Nonce)

[0020] After the key K session is generated, to prevent the key from being cracked, a hybrid encryption method of post-quantum secure key exchange (Kyber) + symmetric encryption (SM4) is adopted. First, use Kyber to perform public key encryption on the key:

[0021] C KEM = Kyber.Enc(PK recv , K session )

[0022] Use SM4 to encrypt the original catering data P:

[0023] C data = SM4.Enc(K session , P)

[0024] The final ciphertext is:

[0025] C = Concat(C KEM , C data )

[0026] For the business layer, different entities have different access rights to data. Multiple parties such as catering enterprises, supply chains, and logistics companies need to share data without exposing all information. Therefore, the federated attribute encryption technology (Federated CP-ABE) is adopted to ensure that only users who meet specific access policies can decrypt the key. First, the catering data provider i uses CP-ABE to encrypt the session key C KEM as follows:

[0027] C Ki = CP-ABE.Enc(C KEM , A i , PK)

[0028] where C KEM is the key encrypted in the core layer, A i is the access policy defined by the catering data provider i, specifying which set of user attributes can decrypt the key, and PK is the public parameter of the CP-ABE system, generated by the key management center (KGC). The core layer has already encrypted the original data P using SM4 to obtain the encrypted data C data . To ensure access control for catering data sharing, the present invention further uses CP-ABE to encrypt the encrypted data:

[0029] C i = CP-ABE.Enc(C data , A i , PK)

[0030] The catering data provider i finally publishes two ciphertexts, namely the encrypted session key and the encrypted business data C i . Only the visitor who decrypts both and C i can finally obtain the original data P.

[0031] Traditional CP-ABE requires manual design of the access policy A iTherefore, the present invention uses a graph neural network (GNN) to automatically generate an optimal access policy. First, data providers such as catering enterprises, suppliers, and logistics companies are modeled into an attribute graph, where the nodes are the catering data sharing entities and the edges are the data sharing relationships. The GNN is used to predict which catering entities should be granted access rights and output an optimized access policy.

[0032] As an example, the catering data sharing network is a graph G=(V, E), where V is the set of data provider nodes and E is the edge of the data sharing relationship. The GNN calculates the feature vector h of data provider node i i The formula is as follows:

[0033]

[0034] where h i is the feature vector of data provider i, N(i) is the set of neighbor nodes of data provider i, W is the parameter obtained by training the GNN, σ is the activation function, and h j is the state or feature of the neighbor node directly connected to the current node i. According to the GNN calculation of the feature vector h i , an access policy A for data provider i is generated i , A i =f(h i ), where f(h i ) is the access control policy generation function, which is responsible for generating a specific access policy according to the feature vector h i .

[0035] For the privacy layer, different catering data has different privacy protection intensities. Before data decryption, a hierarchical zero-knowledge proof (ZKP) mechanism is introduced to provide verifiable and selectable privacy protection for data sharing. First, a data sensitivity function is defined

[0036]

[0037] The system selects different ZKP protocols according to the sensitivity f(p i )∈{1, 2, 3} of p i . For low-sensitivity data, a lightweight mechanism Bulletproofs is used to generate a range proof:

[0038] π i (1) =Bulletproofs.Prove

[0039] For medium-sensitivity data, the PLONK is used to construct an equality circuit to verify constraints such as the identity of the visitor and the data request path:

[0040] π i(2) = PLONK.Prove(C id (v i ), p i )

[0041] For highly sensitive data, use recursive ZK-SNARKs to construct a multi-layer nested verification circuit:

[0042] π3 (3) = ZK-SNARKs.RecursiveProve(P i , C1℃2℃3)

[0043] The platform verifier uses the public verification key VK to verify the proof:

[0044]

[0045] Only when the verification passes, is the final decryption of the data ciphertext C i allowed.

[0046] Step 3: Design a collaborative optimization objective function

[0047] In this step, a collaborative optimization objective function is designed to unify and coordinate three policy dimensions: encryption strength, access control, and privacy protection. Aiming at problems such as high-frequency orders, variable user access behaviors, and diverse data sensitivity levels in the catering industry, the present invention proposes a collaborative optimization function under multi-objective constraints. By introducing weight parameters α, β, and γ, the encryption overhead, access latency, and privacy leakage risk are added to the objective function, and techniques such as the Lagrange multiplier method and ADMM are used for solution. This optimization function effectively reduces the data access latency and computational burden, and realizes the dynamic balance of security, efficiency, and privacy in complex catering data sharing scenarios with multiple roles, dynamic access, and highly sensitive data. Through this mechanism, the system can quickly adjust each policy parameter according to changes, making the catering data sharing more efficient, convenient, and trustworthy.

[0048] The specific operation process is designed as follows:

[0049] In the catering data sharing system, the goals of the core layer, business layer, and privacy layer are combined, and three key factors are optimized, namely encryption cost, access latency, and privacy overhead. Therefore, the objective optimization function is expressed as:

[0050]

[0051] Among them, α, β, and γ are adjustable weight parameters used to find a balance among security, performance, and privacy. K represents the key management strategy, which affects the encryption cost. T represents the access control strategy, which affects the data access latency. π represents the privacy protection strategy, which affects the computational overhead. The constraints are as follows:

[0052] Security constraint: SecurityLevel(K) ≥ λ sec , ensuring that the encryption level meets industry standards.

[0053] Privacy constraint: ε ≤ ε max , ensuring that differential privacy meets the privacy budget requirements.

[0054] Policy compliance constraint: T |=P legal , ensuring that the access control policy complies with regulations.

[0055] To solve the optimization problem with constraints, the Lagrangian function is introduced:

[0056] L(K, T, π, μ1, μ2, μ3) = αEncryptionCost(K) + βAccessDelay(T) + γProofOverhead(π) +

[0057] μ1(λ sec - SecurityLevel(K)) + μ2(ε max - ε) + μ3(T |=P legal )

[0058] Take the partial derivatives with respect to K, T, and π and set their gradients equal to 0:

[0059]

[0060] Solve for the Lagrange multipliers to ensure that the constraints are satisfied:

[0061] μ1(λ sec - securityLevel(K)) = 0, μ2(ε max - ε) = 0, μ3(T |=P legal ) = 0

[0062] Since this problem involves multiple sub - problems, the Alternating Direction Method of Multipliers (ADMM) is used in the present invention. ADMM can split the problem into multiple sub - problems for separate optimization. First, optimize the key management K:

[0063]

[0064] Secondly, optimize the access control strategy T:

[0065]

[0066] Finally, optimize the privacy protection strategy π:

[0067] Brief Description of the Drawings

[0068] Figure 1 It is a data preprocessing flowchart of a hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to the present invention.

[0069] Figure 2 It is a hierarchical architecture diagram of a hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to the present invention.

[0070] Figure 3 It is an optimization flowchart of a collaborative function of a hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to the present invention. Detailed Embodiments

[0071] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following embodiments will further illustrate the present invention with reference to the accompanying drawings; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not limited to the present invention.

[0072] The present invention proposes a hierarchical collaborative catering data sharing system based on blockchain and federated learning.

[0073] Combined with Figure 1 , step 1 specifically includes the following:

[0074] In the catering industry, data is usually created by Internet of Things devices and catering management systems, which contains a large amount of real-time data, including order data D order,i ={O1, O2,..., O n}, inventory data D stock,i ={S1, S2,..., S m}, supply chain data D supply,i ={P1, P2,..., P l} and customer feedback data D review,i ={R1, R2,..., R k}, etc. After each enterprise collects data using devices and systems, it preprocesses the catering data. First, it is data cleaning, removing noise data and dealing with missing values. Secondly, it standardizes the data to ensure the unified data format. Finally, it extracts features from the catering data. After preprocessing, the data X i is converted into a standard format, X i =f(D i )={x1, x2,..., x p}, where Xi Representing the catering enterprise R i The standard data set obtained after preprocessing, including customer preferences, dish sales volume, inventory turnover rate, etc.

[0075] Combined with Figure 2 , Step 2 specifically includes the following:

[0076] In the catering industry, data sharing usually involves multiple parties such as catering enterprises, food delivery platforms, and supply chains. Therefore, the present invention adopts an attribute-based key mechanism for data at the core layer to ensure that different participating parties can obtain different keys when accessing data. The present invention constructs a dynamic key based on HMAC-SM3 and performs key derivation in combination with the participating party attributes and time window. The key derivation function is:

[0077]

[0078] Where K master Is a 128-bit master key, A = {a1, a2,..., a n} is the set of participating party attributes (such as catering enterprise ID, food delivery platform permissions, etc.), Concat(A) represents concatenating the attributes into a binary string in lexicographical order, Hash(t) is the hash value of the time window t, and in the present invention, SHA-256 is used and truncated to 128 bits. K session Is the finally generated session key.

[0079] At the same time, to ensure the security of catering data, key rotation is performed regularly. The present invention defines the key life cycle as the time window t. When t expires, a new key is derived:

[0080] K (t+1) session = HMAC-SM3(K (t) session , Nonce)

[0081] Nonce is generated through negotiation among all parties to ensure the randomness and security of the update process.

[0082] After the key K session Is generated, it needs to be transmitted among multiple catering data sharing parties. Traditional public key encryption (such as RSA, ECC) is easily cracked. Therefore, the present invention adopts a hybrid encryption method of post-quantum secure key exchange (Kyber) + symmetric encryption (SM4). First, use Kyber to perform public key encryption on the key K session :

[0083] C KEM = Kyber.Enc(PK recv , K session )

[0084] Use KM4 to encrypt the original catering data P:

[0085] C data = SM4.Enc(K session , P)

[0086] The final ciphertext is:

[0087] C = Concat(C KEM , C data )

[0088] In the data sharing scenario of the catering industry, different entities have different access rights to data. Multiple parties such as catering enterprises, supply chains, and logistics companies need to share data without exposing all information. Therefore, the federated attribute encryption technology (Federated CP-ABE) is adopted at the business layer to ensure that only users who meet specific access policies can decrypt the key. First, the catering data provider i uses CP-ABE to encrypt the session key C KEM :

[0089]

[0090] Among them, C KEM is the key encrypted at the core layer, A i is the access policy defined by the catering data provider i, which stipulates which user's attribute set can decrypt the key, and PK is the public parameter of the CP-ABE system, generated by the key management center (KGC). The core layer has used SM4 to encrypt the original data P and obtained the encrypted data C data . To ensure the access control of catering data sharing, the present invention further uses CP-ABE to encrypt the encrypted data:

[0091] C i = CP-ABE.Enc(C data , A i , PK)

[0092] The catering data provider i finally publishes two ciphertexts, namely the encrypted session key and the encrypted business data C i . Only the visitor who decrypts and C i simultaneously can finally obtain the original data P.

[0093] In the catering industry data sharing scenario, traditional CP-ABE requires manual design of the access policy A i, therefore, the present invention uses a graph neural network (GNN) to automatically generate an optimal access policy. First, data providers such as catering enterprises, suppliers, and logistics companies are modeled into an attribute graph, where the nodes are the catering data sharing entities and the edges are the data sharing relationships. GNN is used to predict which catering entities should be granted access rights, and an optimized access policy is output.

[0094] As an example, assume that the catering data sharing network is a graph G=(V, E), where V is the set of data provider nodes and E is the data sharing relationship edge. GNN calculates the feature vector h of data provider node i i The formula is as follows:

[0095]

[0096] where h i is the feature vector of data provider i, N(i) is the set of neighbor nodes of data provider i, W is the parameter obtained by GNN training, σ is the activation function, and h j is the state or feature of the neighbor nodes directly connected to the current node i. According to the feature vector h calculated by GNN i , an access policy A for data provider i is generated i , A i =f(h i ), where f(h i ) is the access control policy generation function, which is responsible for generating a specific access policy based on the feature vector h i .

[0097] In the catering industry, different data should have different privacy protection intensities. Therefore, the present invention proposes a privacy layer, which introduces a hierarchical zero-knowledge proof (ZKP) mechanism before data decryption to provide verifiable and selectable privacy protection for data sharing. First, a data sensitivity function is defined

[0098]

[0099] The system selects different ZKP protocols according to the sensitivity f(p i ) ∈ {1, 2, 3} of p i ). For low-sensitivity data, a lightweight mechanism Bulletproofs is used to generate a range proof:

[0100] π i (1) =Bulletproofs.Prove

[0101] For medium-sensitivity data, a PLONK construction equation circuit is used to verify constraints such as the identity of the visitor and the data request path:

[0102] πi (2) = PLONK.Prove(C id (v i ), p i )

[0103] For highly sensitive data, use recursive ZK - SNARKs to construct a multi - layer nested verification circuit:

[0104] π3 (3) = ZK - SNARKs.RecursiveProve(P i , C1℃2℃3)

[0105] The platform verifier uses the public verification key VK to verify the proof:

[0106]

[0107] Only when the verification passes is the final decryption of the data ciphertext C i allowed.

[0108] Combined with Figure 3 , step 3 specifically includes the following:

[0109] In the catering data sharing system, combine the goals of the core layer, business layer, and privacy layer to design a collaborative optimization objective function. This invention optimizes three key factors, namely encryption cost, access latency, and privacy overhead. Because, the objective optimization can be expressed as:

[0110]

[0111] Among them, α, β, γ are adjustable weight parameters used to find a balance among security, performance, and privacy. K represents the key management strategy, which affects the encryption cost. T represents the access control strategy, which affects the data access latency. π represents the privacy protection strategy, which affects the computational overhead. The constraint conditions are:

[0112] Security constraint: SecurityLevel(K) ≥ λ sec , ensuring that the encryption level meets industry standards.

[0113] Privacy constraint: ε ≤ ε max , ensuring that differential privacy meets the privacy budget requirements.

[0114] Policy compliance constraint: T |= P legal , ensuring that the access control policy complies with regulations.

[0115] To solve the optimization problem with constraints, introduce the Lagrangian function:

[0116] L(K, T, π, μ1, μ2, μ3) = αEncryptionCost(K) + βAccessDelay(T) + γProofOverhead(π) + μ1(λ sec -SecurityLevel(K)) + μ2(ε max -ε) + μ3(T |=P legal )

[0117] Take the partial derivatives with respect to K, T, and π and set their gradients equal to 0:

[0118]

[0119] Solve for the Lagrange multipliers to ensure that the constraints are satisfied:

[0120] μ1(λ sec -securityLevel(K)) = 0, μ2(ε max -ε) = 0, μ3(T |=P legal ) = 0

[0121] Since this problem involves multiple sub - problems, the present invention uses the Alternating Direction Method of Multipliers (ADMM). ADMM can split the problem into multiple sub - problems and optimize them separately. First, optimize the key management K:

[0122]

[0123] Secondly, optimize the access control policy T:

[0124]

[0125] Finally, optimize the privacy protection policy π:

[0126]

[0127] To better illustrate the hierarchical architecture of the present invention, it is sorted out again through a brief introduction as follows:

[0128] The present invention proposes a hierarchical collaborative catering data sharing system based on blockchain and privacy computing. Through the deep integration of dynamic encryption in the core layer, intelligent access policies in the business layer, and verifiable mechanisms in the privacy layer, it realizes secure, efficient, and compliant data transfer and sharing.

[0129] In the core layer, dynamic encryption and key management are adopted to ensure the confidentiality of data during transmission and storage, and resist external attacks and the risk of long - term key leakage. Based on the attributes of catering data sharing participants and time windows, session keys are dynamically generated, and key automatic rotation is supported. The combination of Kyber and SM4 is used to encrypt the original data P, taking into account both performance and future security requirements.

[0130] Intelligent access control and policy generation are adopted at the business layer. The main body of catering data sharing is modeled as an attribute graph, and the optimal permission allocation is predicted through node features and relationships. According to the implementation of business changes, the parameters of the GNN model are updated online to optimize the access policy.

[0131] Hierarchical verification is adopted at the privacy layer. Through hierarchical zero-knowledge proofs, different protocols are selected according to the sensitivity level of catering data. For low-sensitivity data, the lightweight mechanism Bulletproofs is used; for medium-sensitivity data, PLONK is used; for high-sensitivity data, recursive ZK-SNARKs is used. The hierarchical architecture of the present invention solves the pain points such as the imbalance between security and efficiency, the lag of manual policies, and the high compliance cost in catering data sharing through the organic integration of dynamic keys, intelligent access policies, and verifiable privacy, and provides a flexible and reliable solution for the catering industry.

[0132] The above are only the preferred embodiments of the present invention. It should be understood that the description of the above embodiments is only used to help understand the method and its core idea of the present invention, and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A hierarchical collaborative catering data sharing system based on blockchain and privacy computing, characterized in that It includes the following specific steps: Step 1: Collect and preprocess the catering data. Step 2: In the catering data sharing scenario, design a hierarchical collaborative catering data sharing architecture, which is divided into three major modules: the core layer, the business layer, and the privacy layer. Step 3: Design a collaborative optimization objective function to combine the objectives of the core layer, the business layer, and the privacy layer, and optimize three key factors: encryption cost, access latency, and privacy overhead.

2. The hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to claim 1, characterized in that, In the catering data sharing scenario, there is a large amount of catering data. First, collect the catering data through Internet of Things devices and catering management systems. Secondly, preprocess the catering data. Finally, standardize the data to ensure the uniformity of the data format.

3. The hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to claim 1, characterized in that A hierarchical collaborative system architecture is constructed, which is divided into three major modules: the core layer, the business layer, and the privacy layer. The core layer is responsible for the encryption protection of the original data and key generation, and constructs an encrypted mapping and dynamic key update model for catering data; the business layer is oriented to sharing control and policy matching, executes access verification and decryption control, and coordinates the policy game and interest balance between data providers and data consumers; the privacy layer introduces zero-knowledge proof technology to achieve privacy protection for different data according to the sensitivity of catering data.

4. A hierarchical collaborative catering data sharing system based on blockchain and privacy computing according to claim 1, characterized in that A collaborative optimization objective function is designed to uniformly coordinate three types of policy dimensions: encryption intensity, access control, and privacy protection. Aiming at problems such as high order frequency, variable user access behavior, and diverse data sensitivity levels in the catering industry, the present invention proposes a collaborative optimization function under multi-objective constraints. By introducing weight parameters α, β, γ, the encryption overhead, access latency, and privacy leakage risk are added to the objective function, and techniques such as the Lagrange multiplier method and ADMM are used for solution.

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