Privacy protection and verifiable product carbon footprint evaluation method based on block chain

Abstract

The invention discloses a privacy protection and verifiable product carbon footprint evaluation method based on a block chain. The method mainly comprises the steps that a chain owner enterprise defines a purpose and a range; a chain owner enterprise requests carbon emission list data from an upstream raw material supplier according to a product material list (BOM), the raw material supplier performs homomorphic encryption on the carbon emission list data and then sends the data to a cloud computing platform of the chain owner enterprise, and identity verification is performed on a data source in combination with an RSA digital signature algorithm; the cloud platform completes carbon footprint calculation in a ciphertext state, and finally, a ciphertext result is returned to an upstream supplier; and the chain owner enterprise integrates the raw material supplier data through the cloud platform, completes final product carbon footprint calculation, verifies the validity of the calculation process based on a zero-knowledge proof technology, realizes automatic verification in combination with an intelligent contract, and finally explains and explains the whole process. According to the method, privacy protection of the carbon emission list data is realized, the evaluation process is verifiable, and the evaluation result is more accurate.

Description

Technical Field

[0001] The present invention belongs to the field of privacy computing applications, and specifically relates to a privacy protection and verifiable product carbon footprint evaluation method based on blockchain. Background Art

[0002] From raw material acquisition to product manufacturing, transportation, use and final disposal, each stage will produce different levels of greenhouse gas emissions. Product carbon footprint assessment is the process of quantifying and evaluating greenhouse gas emissions during the product life cycle. It provides an important basis for enterprises to formulate emission reduction measures and optimize production processes.

[0003] Product carbon footprint evaluation usually adopts Life Cycle Assessment (LCA). It includes four steps: purpose and scope determination, inventory analysis, impact assessment and explanation. The traditional manual product carbon footprint evaluation and analysis has problems such as difficulty in obtaining inventory data, difficulty in ensuring the objectivity and accuracy of the evaluation, and high evaluation costs. The digital LCA evaluation system has become an important way to evaluate product carbon footprint. It mainly includes:

[0004] 1. Product carbon footprint assessment based on LCA software: LCA software (such as GaBi, SimaPro, etc.) usually provides a complete life cycle assessment framework to evaluate the environmental impact of the entire life cycle of a product or service. However, its carbon inventory data comes from various databases rather than real field data. This method will lead to the lack of accuracy of the final carbon footprint assessment results, which will ultimately affect the relevant emission reduction policies.

[0005] 2. Product carbon footprint evaluation based on cloud computing: Based on the LCA method, the cloud platform is used to obtain product life cycle carbon emission inventory data provided by product supply chain companies and conduct carbon footprint evaluation and analysis. The carbon emission inventory data comes from the actual production site of the company. Compared with the use of environmental database data, the carbon footprint evaluation and analysis results are more accurate. However, the carbon emission inventory data of supply chain companies are usually related to the production management of the company and are private. Uploading to the cloud platform will cause the risk of privacy leakage. At the same time, cloud computing methods also have the possibility of data tampering and the lack of verification of the effectiveness of the evaluation process.

[0006] 3. Carbon footprint assessment of products based on blockchain: One type uses blockchain as a decentralized database. Enterprises in the product supply chain upload carbon inventory data for carbon footprint assessment to the blockchain, and then use the data on the blockchain for carbon footprint assessment. Blockchain effectively prevents data tampering, but there is a risk of privacy leakage. Another type focuses on using blockchain and smart contracts to verify the parameters and calculation methods of carbon footprint calculation. At present, there is no complete product carbon footprint LCA assessment solution based on blockchain.

[0007] In summary, the present invention proposes a privacy protection and verifiable carbon footprint evaluation method based on blockchain. Summary of the invention

[0008] This paper proposes a privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain. This method integrates blockchain and privacy computing technology to achieve verifiable product carbon footprint evaluation under the privacy protection of product supply chain inventory data.

[0009] The technical solution adopted by the present invention is as follows:

[0010] A privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain, such as Figure 1 As shown, the following steps are included: Step 1: The chain leader defines the purpose, system boundaries and functional units of the product life cycle assessment, and initializes the method-related parameters; Step 2: The chain leader sends a data request to the supply chain members based on the product BOM (bill of materials). Each supply chain member performs a list analysis. For the upstream raw material suppliers of the supply chain, the list data of raw material acquisition and parts manufacturing are homomorphically encrypted and a data commitment is generated and uploaded to the blockchain; Step 3: The upstream raw material supplier generates a digital signature based on the RSA algorithm and sends the data to the cloud platform of the chain leader enterprise. The cloud platform authenticates the data source enterprise and completes the privacy-protected carbon footprint calculation in a ciphertext state. Finally, the ciphertext result is returned to the upstream supplier to complete the enterprise-level carbon footprint calculation; Step 4: The chain leader integrates the data of supply chain members through the cloud platform, calculates the final product carbon footprint value based on the determined system boundaries, verifies the validity of the calculation process based on zero-knowledge proof technology and realizes automated verification in combination with smart contracts. Step 5: Summarize the data and results of the product carbon footprint calculation process, comprehensively explain the LCA process, and form meaningful conclusions and recommendations. The present invention focuses on steps 2 to 4, which will be described in detail below.

[0011] Furthermore, in step 2, the specific process is as follows: Step 2.1: Generate secret keys. The audit agency generates the corresponding public and private key pairs (PK, SK) using homomorphic encryption algorithms based on relevant parameters and distributes them; Step 2.2: Activity data encryption. After generating the public key, the upstream supplier homomorphically encrypts the activity data to generate activity data ciphertext. Activity data includes energy consumption indicators such as electricity consumption, water consumption, and natural gas consumption. The public key PK is used to convert the provided activity data into ciphertext; Step 2.3: Generate data commitment. Generate the commitment value of the ciphertext with the help of the RSA commitment mechanism. The specific operation is to first select a random number as the blinding factor r, which is used to hide the true value of the original data and prevent information from being revealed through the commitment. Then, the blinding factor and the data ciphertext are input into the hash function together to generate the commitment value and upload it to the blockchain;

[0012] Furthermore, in step 3, the specific process is as follows: Step 3.1: The upstream raw material supplier generates a digital signature using the RSA algorithm, and then sends the data and signature to the chain leader enterprise; Step 3.2: The chain leader enterprise uses the digital signature to verify the correctness of the data source. If the verification passes, the data is accepted; if the verification fails, the data is rejected. Step 3.3: Ciphertext homomorphic calculation. The upstream supplier uploads the ciphertext to the cloud server deployed by the chain leader enterprise to calculate the product carbon footprint, and uploads the ciphertext commitment to the blockchain. The cloud platform first verifies the authenticity of the data through the data commitment of the blockchain. After the verification, the cloud platform uses the homomorphic addition and homomorphic multiplication operations of the homomorphic encryption algorithm to complete the product carbon footprint calculation in the ciphertext state. After the calculation is completed, the ciphertext result is obtained, and finally the ciphertext result is returned to the corresponding upstream supplier; Step 3.4: Decryption of the ciphertext result. The upstream supplier first verifies the calculation result using the commitment mechanism, and then decrypts the carbon footprint calculation result to obtain the plaintext carbon footprint value;

[0013] Furthermore, in step 4, the specific process is as follows: Step 4.1: The chain leader collects its own activity data. For enterprises with a high degree of informatization, the Internet of Things detection equipment is deployed to automatically collect activity data such as electricity consumption, water consumption, and natural gas consumption; for enterprises with a low degree of informatization, manual collection can be used to collect activity data; Step 4.2: The chain leader uses the Pedersen commitment algorithm to generate commitments for the collected upstream carbon footprint values ​​and the collected activity data and upload them to the blockchain for evidence storage; Step 4.3: The chain leader uploads the collected upstream carbon footprint values ​​and collected activity data to the cloud platform for the final product carbon footprint calculation; Step 4.4: Generate a proof of correctness for the product carbon footprint calculation process using a non-interactive zero-knowledge proof algorithm. First, define a circuit that describes the product carbon footprint calculation process, define private data and public data descriptions, and then compile the circuit into constraint r1cs cal , initialize the algorithm's proof key pk cal and verification key vk cal , using carbon emission factors as public data to generate witness cal and publicWitness cal , and finally use r1cs cal ,pk cal ,witness cal Generate proof of computational process cal ; Step 4.5: vk cal and proof cal Upload to the blockchain and deploy the smart contract for verifying the correctness of the product carbon footprint calculation process. The smart contract will verify the correctness of the product carbon footprint calculation process.

[0014] Compared with the existing technical solutions, the beneficial effects of the present invention are:

[0015] 1. The present invention introduces homomorphic encryption technology. The upstream supplier uses the Paillier algorithm to encrypt the activity data. The cloud platform deployed by the chain leader enterprise completes the calculation of the carbon footprint in a ciphertext state, thereby realizing data privacy protection.

[0016] 2. The present invention introduces digital signature technology. When a data provider provides data to a chain owner enterprise, an RSA digital signature algorithm is used to generate a digital signature for the data. The chain owner enterprise uses the digital signature to authenticate the data source, thereby achieving the verifiable identity of the data source.

[0017] 3. The present invention introduces zero-knowledge proof and blockchain technology to generate a zero-knowledge proof for the calculation process, and stores it on the blockchain platform. It uses smart contracts to achieve public and automated verification, thereby achieving public verifiability of the product carbon footprint calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of the method of the present invention.

[0019] Figure 2 This is an overall architecture diagram of the privacy protection and verifiable product carbon footprint evaluation method of the present invention.

[0020] Figure 3 Schematic diagram of upstream carbon footprint calculation for privacy protection of the present invention.

[0021] Figure 4 It is a schematic diagram of the verifiable product carbon footprint calculation of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. The technical solutions of the present invention are further illustrated in conjunction with the accompanying drawings and embodiments.

[0023] Example

[0024] The following is a detailed description of the privacy protection and verifiable carbon footprint assessment method based on blockchain, which includes four types of entities, namely upstream suppliers, chain leader enterprises, cloud servers and blockchains. The method flow is as follows: Figure 1 As shown, the method architecture is as follows Figure 2 As shown, the homomorphic encryption algorithm flow chart is as follows Figure 3 The verifiable product carbon footprint calculation flow chart is shown in Figure 4 shown.

[0025] The specific implementation process of this embodiment is as follows:

[0026] Step 1: The chain leader defines the purpose, system boundaries and functional units of the product life cycle assessment, and initializes the method-related parameters;

[0027] Step 2: The chain leader sends a data request to the supply chain members based on the product BOM (bill of materials). Each supply chain member performs a list analysis. For the upstream raw material suppliers of the supply chain, the list data of raw material acquisition and parts manufacturing are homomorphically encrypted and a data commitment is generated and uploaded to the blockchain;

[0028] Furthermore, in step 2, the upstream provider uses a homomorphic encryption algorithm to encrypt active data, calculate commitments, generate digital signatures, and perform homomorphic calculations on the cloud server as follows: Step 1: Generate the corresponding public key and private key using the Paillier algorithm. The generation process is as follows: select two large prime numbers p and q; calculate the product of p and q n = p*q, and calculate: λ=lcm(p-1,q-1) selects a random integer g, Defining functions And compute the modular inverse element: μ=L(g λ mod n 2 ) -1 mod n Get the public key PK of the Paillier algorithm pai =(n,g), private key SK pai =(λ,μ). Step 2: Activity data encryption. After generating the public key, the upstream supplier encrypts the activity data, which includes energy consumption indicators such as electricity consumption and water consumption. i , water consumption is w i , the amount of natural gas used is g i , transport distance d, using public key PK pai Convert the provided activity data into ciphertext, respectively c ei , c wi , c gi , c d . Among them, m is the activity data, p and q are random large prime numbers, g is a random integer, and r is a random number. Step 3: Generate data commitment. Generate the commitment value of the ciphertext with the help of the RSA commitment mechanism. The specific operation is to first select a random number as the blinding factor r, which is used to hide the true value of the original data and prevent information from being revealed through the commitment. Then, the blinding factor and the data ciphertext are input into the hash function together to generate the commitment value and upload it to the blockchain;

[0029] Step 3: The upstream raw material supplier generates a digital signature based on the RSA algorithm and sends the data to the cloud platform of the chain leader enterprise. The cloud platform authenticates the data source enterprise and completes the privacy-protected carbon footprint calculation in a ciphertext state. Finally, the ciphertext result is returned to the upstream supplier to complete the enterprise-level carbon footprint calculation;

[0030] Furthermore, in step 3, the specific process is as follows: Step 1: Take two secret large prime numbers p and q and calculate them according to the following formula: N=p·q Next, calculate the public key (e, N) and private key (d, N) according to the following formula: e·d≡1(modφ(N) Step 2: The upstream supplier generates a digital signature: RsaSign(Sk,PCF)→Sig. The algorithm generates the corresponding digital signature, and the input parameters are the secret key and the carbon footprint value; Step 3: The chain leader enterprise verifies the digital signature: RsaVerify(Pk,Sig,PCF)→IsSignatureValid. This algorithm generates the verification result of the digital signature. The input parameters are the secret key, digital signature and related data. Step 4: Calculate product carbon footprint. Supply chain companies transmit the generated activity data ciphertext to the cloud server, which calculates the product carbon footprint. Completing the product carbon footprint calculation requires homomorphic addition and homomorphic multiplication operations of the Paillier algorithm. The homomorphic addition process (assuming the ciphertext is c1, c2) is: c=(c 1 ×c 2 )modn 2 Paillier's homomorphic multiplication process (assuming the ciphertext is c1 and the constant is k): c=(c 1 ×k)modn 2 Therefore, the carbon footprint of the product calculated based on the above activity data is: At the same time, the committed random factors are aggregated. Step 5: Decrypt the result ciphertext. First, use the commitment mechanism to verify the calculation result. The product carbon footprint calculation results are then decrypted. m=Dec SK (C PCF ) =L(C PCF ×λmod n 2 )×μmodn

[0031] Step 4: The chain leader integrates the data of supply chain members through the cloud platform, calculates the final product carbon footprint value based on the determined system boundaries, verifies the validity of the calculation process based on zero-knowledge proof technology and realizes automated verification in combination with smart contracts.

[0032] Furthermore, in step 4, the specific process is as follows: Step 1: Define the product carbon footprint calculation process. In order to clearly express the calculation process, the product carbon footprint calculation process is defined according to the formula: PCF up +AD×EF=CPF Step 2: Convert the product carbon footprint calculation process into R1CS constraints: In order to protect private data, R1CS constraints (also called circuit constraints) are needed to equivalently describe the algorithm's operation rules, that is, convert the calculation formula satisfied by the private data in the product carbon footprint calculation process into the public R1CS constraints satisfied by the private data: s 1 =AD×EF s2 =(s 1 +PCF up )×1 PCF=(s 1 +s 2 )×1 Step 3: Convert R1CS constraints to vectors The inner product of a multidimensional vector, where the vector Contains public data and private data, vector The definition of is shown in the following formula: Step 4: Convert the vector Inner product with multidimensional vector converted to vector The inner product of the matrix is ​​given by the following formula: Next, the following conversion relationship can be determined through the specific representation of matrices W, U, and V, as shown in the following formula: Step 5: Calculate the vector The linear combination operation of three sets of polynomials is shown in the following formula: W(x)=[w 0 (x),w 1 (x),w 2 (x),w 3 (x),w 4 (x),w 5 (x),w 6 (x)] U(x)=[u 0 (x),u 1 (x),u 2 (x),u 3 (x),u 4 (x),u 5 (x),u 6 (x)] V(x)=[v 0 (x),v 1 (x),v 2 (x),v 3 (x),v 4 (x),v 5 (x),v 6 (x)] Step 6: Construct the QAP polynomial, target polynomial and quotient polynomial. The QAP polynomial is shown in the following formula: The target polynomial is shown in the following formula: z(x)=(x-1)(x-2)(x-3),x=1,2,3 The quotient polynomial is shown in the following formula: Step 7: Expose the coefficients of the three polynomials to the elliptic curve discrete logarithm to generate a proof, select a random number r, a 0 =1, based on vector And the generated CRS is used to prove the generation of π, which is composed of three elliptic curve discrete logarithm points, as shown in the following formula: π=([pi.A] 1 ,[pi.B] 2 ,[pi.C] 1 ) Step 8: Upload the generated proof to the blockchain. Step 9: Call the smart contract to verify the correctness of the calculation process. The algorithm is as follows: VerProof(proof cal ,vk cal ,publicWitness cal )→Bool. This algorithm is run by a smart contract to verify the product carbon footprint calculation process proof. The input parameter is the product carbon footprint calculation process proof cal 、Verification key vk cal , public Witness cal , returns verification success or failure.

[0033] Step 5: Summarize the data and results of the product carbon footprint calculation process, comprehensively explain the LCA process, and form meaningful conclusions and recommendations.

[0034] The above-described embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain, characterized by: Step 1: The chain leader defines the purpose, system boundaries and functional units of the product life cycle assessment, and initializes the method-related parameters; Step 2: The chain leader sends a data request to the supply chain members based on the product's bill of materials (BOM). Each supply chain member performs a list analysis. For the upstream raw material suppliers in the supply chain, the list data of raw material acquisition and parts manufacturing are homomorphically encrypted and a data commitment is generated and uploaded to the blockchain. Step 3: The upstream raw material supplier generates a digital signature based on the RSA algorithm and sends the data to the cloud platform of the chain leader enterprise. The cloud platform authenticates the data source enterprise and completes the privacy-protected carbon footprint calculation in a ciphertext state. Finally, the ciphertext result is returned to the upstream supplier to complete the enterprise-level carbon footprint calculation; Step 4: The chain leader integrates the data of supply chain members through the cloud platform, calculates the final product carbon footprint value based on the determined system boundaries, verifies the validity of the calculation process based on zero-knowledge proof technology, and realizes automated verification in combination with smart contracts; Step 5: Summarize the data and results of the product carbon footprint calculation process, comprehensively explain the LCA process, and form meaningful conclusions and recommendations.

2. A privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain as claimed in claim 1, characterized in that: In step 2, the specific process is as follows: Step 1: Generate secret keys; the audit agency generates corresponding public and private key pairs (PK, SK) using homomorphic encryption algorithms based on relevant parameters and distributes them; Step 2: Activity data encryption: After generating the public key, the upstream supplier performs homomorphic encryption on the activity data to generate activity data ciphertext; the activity data includes energy consumption indicators such as electricity consumption, water consumption, and natural gas consumption. The public key PK is used to convert the provided activity data into ciphertext; Step 3: Generate data commitment; Generate the commitment value of the ciphertext with the help of the RSA commitment mechanism. The specific operation is to first select a random number as the blinding factor r, which is used to hide the true value of the original data and prevent information from being disclosed through the commitment; then input the blinding factor and the data ciphertext into the hash function together to generate the commitment value and upload it to the blockchain.

3. A privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain as claimed in claim 1, characterized in that: In step 3, the specific process is as follows: Step 1: The upstream supplier generates a digital signature using the RSA algorithm, and then sends the data and signature to the chain leader enterprise; Step 2: The chain leader enterprise uses the digital signature to verify the correctness of the data source. If the verification passes, the data is accepted; if the verification fails, the data is rejected. Step 3: Ciphertext homomorphic calculation; the upstream supplier uploads the ciphertext to the cloud server deployed by the manufacturing enterprise to calculate the product carbon footprint, and uploads the ciphertext commitment to the blockchain; the cloud platform first verifies the authenticity of the data through the data commitment of the blockchain. After the verification, the cloud platform uses the homomorphic addition and homomorphic multiplication operations of the homomorphic encryption algorithm to complete the product carbon footprint calculation in the ciphertext state. After the calculation is completed, the ciphertext result is obtained, and finally the ciphertext result is returned to the corresponding upstream enterprise; Step 4: Decrypt the resulting ciphertext; The upstream supplier first verifies the calculation results using the commitment mechanism, and then decrypts the carbon footprint calculation results to obtain the plaintext carbon footprint value.

4. A privacy-preserving and verifiable product carbon footprint evaluation method based on blockchain as claimed in claim 1, characterized in that: In step 4, the process is as follows: Step 1: Manufacturing enterprises collect their own activity data; for enterprises with a high degree of informatization, they can automatically collect activity data such as electricity consumption, water consumption, and natural gas consumption by deploying IoT detection equipment; for enterprises with a low degree of informatization, they can collect activity data manually; Step 2: Manufacturing companies use the Pedersen commitment algorithm to generate commitments for the collected upstream carbon footprint values ​​and activity data and upload them to the blockchain for evidence storage; Step 3: Manufacturing companies upload the collected upstream carbon footprint values ​​and activity data to the cloud platform for final product carbon footprint calculation; Step 4: Use a non-interactive zero-knowledge proof algorithm to generate a proof of correctness for the product carbon footprint calculation process. First, define the circuit that describes the product carbon footprint calculation process, define private data and public data descriptions, and then compile the circuit into constraints r1ca cal , initialize the algorithm's proof key pk cal and verification key vk cal , using carbon emission factors as public data to generate witness cal and publicWitness cal , and finally use r1cs cal ,pk cal ,witness cal Generate proof of computational process cal ; Step 5: vk cal and prof cal Upload to the blockchain and deploy the smart contract for verifying the correctness of the product carbon footprint calculation process. The smart contract will verify the correctness of the product carbon footprint calculation process.

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