Method and system for managing carbon data using blockchain network

Abstract

A method of managing carbon data using a blockchain network is provided. The method includes classifying, by the processing device, each of one or more carbon data received from at least one user of the blockchain network into a respective privacy level. Each of the respective privacy levels represents a privacy requirement corresponding to each of the one or more carbon data. The method also includes encrypting, by the processing device, each of the one or more classified carbon data using one of a plurality of encryption schemes. The one of the plurality of encryption schemes is determined based on a privacy level of the carbon data. The method also includes generating, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data and transmitting, by the processing device, the one or more blockchain transactions into the blockchain network.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 606,563, filed on December 5, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present invention relates broadly, but not exclusively, to methods and systems for managing carbon data using a blockchain network. Background Art

[0004] As one of the largest resource consumers and carbon emitters, the construction industry plays a vital role in global carbon reduction. Carbon certification or labeling programs are an effective way to assess and report the carbon footprint of construction materials and products (CMPs), providing a basis for carbon management at the CMP level. However, existing carbon management for CMP certification relies heavily on traditional centralized data management tools, which have data opacity and manipulation issues, making carbon footprints unreliable and difficult to track.

[0005] Therefore, there is a need to provide methods and systems for managing carbon data using a blockchain network. Summary of the invention

[0006] According to a first aspect of the present invention, a method for managing carbon data using a blockchain network is provided. The method includes classifying each of one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level by a processing device. Each of the corresponding privacy levels represents a privacy requirement corresponding to each of the one or more carbon data. The method also includes encrypting each of the one or more classified carbon data using one of a plurality of encryption schemes by the processing device. The one of the plurality of encryption schemes is determined based on the privacy level of the carbon data. The method also includes generating one or more blockchain transactions corresponding to each of the one or more encrypted carbon data by the processing device, and transmitting the one or more blockchain transactions to the blockchain network by the processing device.

[0007] Each of the one or more carbon data may include basic product information and / or manufacturing information of at least one building material or product. In addition, the privacy requirement of each of the one or more carbon data may be predetermined based on the basic product information and / or manufacturing information.

[0008] In an embodiment of the present invention, the method may include mapping, by the processing device, each of the one or more carbon data to a corresponding privacy level based on a predetermined privacy requirement.

[0009] In one embodiment, the corresponding privacy levels may include a first privacy level, a second privacy level, and a third privacy level. The carbon data at the first privacy level may be accessible to all users in the blockchain network, the carbon data at the second privacy level may be accessible to users authorized by the owner of the carbon data, and the carbon data at the third privacy level may be accessible only to the owner of the carbon data.

[0010] In this embodiment, the plurality of encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. The first level privacy carbon data and the second level privacy carbon data may be encrypted using an asymmetric encryption scheme, and the third level privacy carbon data may be encrypted using a homomorphic encryption scheme.

[0011] In an embodiment of the present invention, one or more smart contracts may be used to generate one or more blockchain transactions. At least one of the one or more smart contracts may be configured to only allow at least one user authorized by the owner of at least one of the one or more smart contracts to call at least one function of at least one of the one or more smart contracts.

[0012] Additionally, the method can be implemented based on a blockchain-based model. The blockchain-based model can include an architecture having a data access layer, a data privacy layer, and a smart contract layer. The classification step can be performed in the data access layer, the encryption step can be performed in the data privacy layer, and the generation step can be performed in the smart contract layer.

[0013] According to a second aspect of the present invention, a system for managing carbon data using a blockchain network is provided. The system includes a processing device configured to classify each of one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level. Each of the corresponding privacy levels represents a privacy requirement corresponding to each of the one or more carbon data. The processing device is also configured to encrypt each of the one or more classified carbon data using one of a plurality of encryption schemes. The one of the plurality of encryption schemes is determined based on the privacy level of the carbon data. The processing device is also configured to generate one or more blockchain transactions corresponding to each of the one or more encrypted carbon data and transmit the one or more blockchain transactions to the blockchain network.

[0014] In an embodiment of the present invention, the processing device may also be configured to map each of the one or more carbon data to a corresponding privacy level based on a predetermined privacy requirement.

[0015] In one embodiment, the corresponding privacy levels may include a first privacy level, a second privacy level, and a third privacy level. The carbon data at the first privacy level may be accessible to all users in the blockchain network, the carbon data at the second privacy level may be accessible to users authorized by the owner of the carbon data, and the carbon data at the third privacy level may be accessible only to the owner of the carbon data.

[0016] In this embodiment, the plurality of encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. The carbon data of the first privacy level and the carbon data of the second privacy level may be encrypted using an asymmetric encryption scheme, and the carbon data of the third privacy level may be encrypted using a homomorphic encryption scheme.

[0017] In an embodiment of the present invention, one or more smart contracts may be used to generate one or more blockchain transactions. At least one of the one or more smart contracts may be configured to only allow at least one user authorized by the owner of at least one of the one or more smart contracts to call at least one function of the at least one of the one or more smart contracts.

[0018] In an embodiment of the present invention, the processing device may also be configured to implement a blockchain-based model. The blockchain-based model may include an architecture having a data access layer, a data privacy layer, and a smart contract layer. Classifying each of the one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level may be performed in the data access layer. Encrypting each of the one or more classified carbon data using one of a plurality of encryption schemes may be performed in the data privacy layer. Generating one or more blockchain transactions corresponding to each of the one or more encrypted carbon data may be performed in the smart contract layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Embodiments of the present invention will be better understood and apparent to those of ordinary skill in the art from the following written description which is given by way of example only and in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flow chart illustrating an exemplary framework for managing carbon data using a blockchain network is shown, according to an embodiment.

[0021] Figure 2 Another flow chart illustrating an exemplary framework for managing carbon data using a blockchain network is shown in accordance with an embodiment.

[0022] Figure 3 A flow chart illustrating an exemplary process for a CMP certification lifecycle is shown according to an embodiment.

[0023] Figure 4 A flow chart illustrating an exemplary data model with multiple privacy levels is shown according to an embodiment.

[0024] Figure 5 A flow chart is shown for illustrating an example process flow of how to handle privacy level 1 carbon data, according to an embodiment.

[0025] Figure 6 A flow chart illustrating an example process flow of how to process privacy level 2 carbon data is shown according to an embodiment.

[0026] Figure 7 A flowchart is shown for illustrating example workflows for different smart contracts according to an embodiment.

[0027] Figure 8 Exemplary algorithms for implementing different smart contracts according to embodiments are shown.

[0028] Fig. 9 A schematic diagram illustrating an exemplary system for managing carbon data using a blockchain network according to an embodiment.

[0029] Fig.10 A flow chart illustrating a method of managing carbon data using a blockchain network according to an embodiment is shown.

[0030] Fig.11 A flow diagram illustrating an example workflow for implementing a framework for managing carbon data using a blockchain network is shown, according to an embodiment.

[0031] Fig.12 An example of a user interface display of a system for managing carbon data using a blockchain network according to an embodiment is shown.

[0032] FIG. 13A to FIG. 13C A process flow of a first example scenario according to an embodiment is shown.

[0033] Fig.14 The verification result of the first example scenario according to the embodiment is shown.

[0034] Fig.15A and Fig. 15B A process flow for a second example scenario according to an embodiment is shown.

[0035] Fig.16 The verification result of the second example scenario according to the embodiment is shown.

[0036] Fig.17 A chart illustrating the delay in the arrival of transactions generated based on an encryption smart contract, a recording smart contract, and an authentication smart contract, respectively, according to an embodiment is shown.

[0037] Fig.18A chart illustrating the throughput of transactions generated based on encryption smart contracts, recording smart contracts, and authentication smart contracts, respectively, according to an embodiment is shown.

[0038] Fig.19 A chart illustrating the costs of transactions generated based on an encryption smart contract, a recording smart contract, and an authentication smart contract, respectively, according to an embodiment is shown.

[0039] Fig. 20 A schematic diagram illustrating an exemplary computing device for implementing a system for managing carbon data using a blockchain network, according to an embodiment. DETAILED DESCRIPTION

[0040] Embodiments of the present invention will be described by way of example only with reference to the accompanying drawings, in which like reference numerals and characters indicate like elements or equivalents.

[0041] Some portions of the following description are presented explicitly or implicitly in the form of algorithms and functions or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functions or symbols are the means used by those skilled in the art of data processing to most effectively convey the substance of their work to others skilled in the art. An algorithm is generally considered here to be a self-consistent sequence of steps leading to a desired result. Such steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated.

[0042] Unless otherwise specifically noted, and as will become apparent from the following text, it will be understood that throughout this specification, discussions using terms such as "scan," "calculate," "determine," "replace," "generate," "initialize," "output," and the like refer to the actions and processes of a computer system or similar electronic device that manipulate and transform data represented as physical quantities within a computer system into other data similarly represented as physical quantities within a computer system or other information storage, transmission, or display device.

[0043] This specification also discloses a device for performing the operation of these methods. Such a device may be specially constructed for the desired purpose, or may include a computer or other equipment selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays given herein are not inherently related to any particular computer or other device. Various machines may be used together with the program according to the teachings of this article. Alternatively, it may be appropriate to construct a more specialized device to perform the required method steps. The structure of a conventional computer will emerge from the following description.

[0044] In addition, this specification also implicitly discloses a computer program, because it will be apparent to those skilled in the art that the various steps of the method described herein can be implemented by computer code. The computer program is not intended to be limited to any particular programming language and its implementation. It is understood that the teachings of the disclosure contained herein can be implemented using a variety of programming languages ​​and their encodings. In addition, the computer program is not intended to be limited to any particular control flow. Without departing from the spirit or scope of the present invention, there are many other variations of computer programs that can use different control flows.

[0045] In addition, one or more steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include a storage device, such as a disk or optical disk, a memory chip or other storage device suitable for being connected to a computer interface. The computer readable medium may also include a hardwired medium (such as illustrated in an Internet system) or a wireless medium and other wireless systems (such as Bluetooth, ZigBee, Wi-Fi) (such as illustrated in a GSM, GPRS, 3G or 4G mobile phone system). The implementation computer program, when loaded and executed on such a computer, effectively generates a device for implementing the steps of the preferred method.

[0046] The present invention may also be implemented as a hardware module. More specifically, in a hardware sense, a module is a functional hardware unit designed to be used with other components or modules. For example, a module may be implemented using discrete electronic components, or the module may form part of an entire electronic circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). There are many other possibilities. It will be appreciated by those skilled in the art that the system may also be implemented as a combination of hardware modules and software modules.

[0047] In the following description, the term "module" may refer to a software, a hardware element, or a combination of both.

[0048] An Application Programming Interface (API) enables software and applications to communicate with each other. It is a software-to-software interface that allows independent parties to communicate with each other without any prior user knowledge or intervention. Generally speaking, it is a set of well-defined communication methods between various software components.

[0049] This specification uses the term "configured to" in conjunction with systems, devices, and computer program components. For a system of one or more computers configured to perform specific operations or actions, it means that the system has installed on its software, firmware, hardware, or a combination thereof, software, firmware, hardware, or a combination thereof that causes the system to perform these operations or actions in operation. One or more computer programs configured to perform specific operations or actions means that one or more programs include instructions that, when executed by a data processing device, cause the device to perform these operations or actions. For a special-purpose logic circuit to be configured to perform specific operations or actions means that the circuit has electronic logic that performs these operations or actions.

[0050] As used herein, the term "processing device" refers to any hardware or system configured to perform computing tasks. In the context of blockchain, a processing device can perform processes related to the confirmation, verification, and recording of transactions on a blockchain network. This may include tasks such as executing consensus algorithms (e.g., proof of work, proof of equity), generating and verifying cryptographic hashes, and verifying digital signatures of secure transactions. The processing device can also handle the execution of smart contracts, data encryption, and the propagation of blockchain data across distributed networks. Additionally, the processing device can store some parts of the blockchain ledger, maintain transaction history, and ensure the integrity and immutability of blockchain data. The processing device can be configured to communicate with other nodes in the network, synchronize blockchain status, and perform other blockchain-based services.

[0051] introduce

[0052] In 2021, the building and construction industry accounted for more than 34% of energy demand and 2.5% of energy and process-related CO 2 The building and construction industry is therefore under increasing pressure to reduce its lifecycle carbon emissions. While various carbon reduction methods are used to reduce energy consumption during the building operation phase, the importance of reducing embodied carbon from the construction phase cannot be undermined, where embodied carbon from the construction phase accounts for nearly 30% of a building’s lifecycle greenhouse gas (GHG) emissions. Among the various emission sources during the construction phase, the mining and manufacturing of building materials contributes approximately 70% of GHG emissions. In particular, cement production accounts for approximately 2 Steel production generates 5% to 8% of global CO emissions. 2 Carbon emissions account for approximately 7% of total emissions in the building and construction industry. In addition, transporting raw materials to product manufacturing plants (especially for international long-distance transport of raw materials) is energy intensive. Therefore, developing strategies to promote carbon management at the building materials and products (CMP) level is critical to achieving emission reduction targets in the building and construction industry.

[0053] In addition, due to the importance of reducing carbon emissions from CMPs, many efforts have been directed to carbon management in their life cycle. For example, a series of international standards have been published to identify standardized processes and steps for managing carbon footprints from the product level. Based on the principles and guidelines provided by these standards, various carbon certification or labeling schemes for CMPs have been developed in different regions, and these carbon certification or labeling schemes are used as practical and meaningful metrics to help construction companies effectively measure and manage the carbon footprint of CMPs. These schemes are public commitments because the carbon footprint of CMPs has been measured and certified by professional institutions or government departments at an environmentally friendly level, which serves as an effective carbon management tool for credible assessment and public reporting of CMP carbon footprints. However, existing CMP carbon certification or labeling schemes are challenged by not providing sufficiently transparent carbon footprint data. The use of traditional centralized data management methods in these schemes also makes it difficult to verify the reliability and authenticity of carbon footprints. In addition, some companies take advantage of data transparency loopholes to "greenwash", such as making misleading environmental claims by falsifying carbon data or worshipping false labels to achieve more carbon credits or green financing, resulting in a negative impact on customer satisfaction and ultimately reducing company competitiveness. Others may even attack the servers of centralized environmental platforms to inject false data for sustainability propaganda. Therefore, due to the transparency and reliability issues of carbon data caused by existing carbon management methods, public concerns about carbon data falsification have been raised.

[0054] As an emerging and promising technology, blockchain can provide a powerful solution to alleviate data transparency and reliability issues. Unlike centralized systems, blockchains usually operate under a distributed peer-to-peer network without intermediaries, which minimizes reliance on centralized institutions. Therefore, the use of blockchain can obtain data transparency and reliability, and at the same time, through easily identifiable and immutable information records, blockchain can improve data traceability. Due to these advantages that blockchain can provide, blockchain can be used as a transparent, reliable and traceable data management tool for managing the carbon footprint of CMP during the certification process. In the absence of intermediaries or central agencies, the carbon footprint of CMP can be collectively maintained by the corresponding stakeholders / users (e.g., raw material suppliers, manufacturers and certification agencies) in a transparent environment, which promotes the reliability of carbon footprint data in CMP certification. In addition, blockchain can also provide users with effective tracking functions to help customers or other users quickly identify the carbon footprint of certified CMPs.

[0055] Although blockchain can offer great potential in applying carbon management during CMP certification, challenges in applying blockchain in this area remain to be addressed. One major challenge is the leakage of sensitive carbon footprint data during blockchain-based CMP certification. For a certain CMP from a manufacturer, certain carbon data during manufacturing (such as partner material suppliers, material consumption, energy consumption) are private and sensitive. However, in order to assess the carbon footprint of a CMP during the certification process, detailed knowledge of material suppliers and consumption is required. Inappropriate data storage and sharing mechanisms for private and sensitive carbon data may lead to the leakage of trade secrets (such as manufacturing formulas and procurement strategies for CMP raw materials), which may benefit the manufacturer's competitors and harm the interests of the CMP manufacturer. Therefore, the disclosure of such proprietary information often raises concerns among stakeholders. For example, competitors may use the manufacturer's GHG emissions to assess its operational growth and efficiency, while non-governmental organizations (NGOs) may use the same information to put pressure on the manufacturer to improve environmental performance. Since the data stored in the blockchain is generally transparent to all users of the blockchain network, it is difficult for manufacturers to record the carbon data required for CMP certification on the blockchain network. Another challenge may come from uncontrolled user interactions in the blockchain network to generate carbon transactions. Specifically, interactions with the blockchain can generally be called by any user in the blockchain network via smart contracts, that is, any user can interact with the blockchain to generate any type of transaction (including transactions with certification results). However, certification transactions are only valid if they are generated by the institution responsible for certification. Otherwise, the credibility of the certification results may be negatively affected, and the frequency of invalid certification transactions may also increase. Therefore, it is important to address these challenges when applying blockchain to CMP certification. The present disclosure particularly addresses the following issues:

[0056] 1) How can blockchain be applied to enable decentralization and transparency in both reporting and certification of CMP carbon footprints?

[0057] 2) In the decentralized blockchain environment of CMP certification, how to promote carbon data privacy and protect user interactions with the blockchain during the certification process?

[0058] Therefore, embodiments of the present disclosure provide a decentralized blockchain-based framework that is integrated with a carbon data protection scheme and secure user interaction to provide a solution for transparent, reliable, and traceable carbon data management for CMP certification. Two example objectives of the present disclosure are as follows:

[0059] 1) Provide a blockchain-based framework and workflow for decentralized and transparent carbon management for CMP certification.

[0060] 2) Use a blockchain-based framework to provide carbon data privacy protection solutions and secure user interaction mechanisms.

[0061] Design and development of GPChain

[0062] As described above, embodiments of the present disclosure provide a decentralized blockchain-based framework for carbon management for CMP certification, which may also be described interchangeably with the term “GPChain”. Figure 1 An exemplary framework of GPChain is shown. Figure 1 , at 100, multiple users in a blockchain network (i.e., manufacturer 1, material supplier 1, material supplier 2, and certification body) are involved in blockchain-based carbon footprint recording and certification. In addition, at 100, for example, the Ethereum blockchain network is integrated with a traditional database, and the Interplanetary File System (IPFS) is used to store and share relevant carbon data and files for CMP certification. It will be understood that the users are not limited to those previously described and those skilled in the art will be able to recognize other users in the CMP supply chain. At 102, inputs from three types of users are identified respectively according to different information requirements for corresponding types of users in CMP certification. For example, these data inputs can be obtained from the original carbon database or the IPFS system output. At 104, corresponding smart contracts can be used to process these data inputs to generate different types of transactions for CMP certification in the blockchain network. Taking into account the privacy requirements of certain carbon data, in an embodiment of the present disclosure, transactions containing private or sensitive carbon data can be privacy-protected (details will be described in later chapters). In addition, embodiments of the present disclosure may also provide smart contracts with fine-grained access control so that user interactions (calling smart contract functions) can be controlled, or in some cases restricted, to enhance the security of GPChain. At 106, all transactions generated by users are distributed and synchronized in GPChain for user communication and public supervision of CMP certification.

[0063] Figure 2 An exemplary modular architecture of GPChain is shown. Figure 2As shown in , GPChain may include, among other things, i) a data access layer, ii) a data privacy layer, iii) a smart contract layer, iv) a blockchain network layer, and v) a data storage layer. First, the data access layer is designed to receive user input from different types of users in the blockchain network, i.e., carbon footprint data required for CMP certification, which includes data attributes and process files required for CMP certification. In this layer, a data model with multiple privacy levels for blockchain-based CMP certification is provided to identify which received carbon data should be transparent or accessible only to (one or more) authorized users. Based on the data model, a privacy-preserving carbon data sharing strategy is adopted in the data privacy layer, which takes into account the different requirements when sharing sensitive and private carbon data in the blockchain network. The data privacy layer can integrate asymmetric encryption schemes and homomorphic encryption schemes to pre-process carbon data at different privacy levels for secure storage and sharing before generating blockchain transactions based on the received carbon data. For example, only authorized users can use the corresponding decryption key to access the original sensitive carbon data to achieve verification and certification goals, which can promote the security of carbon data in the blockchain network and relieve manufacturers from concerns about the leakage of sensitive carbon data during CMP certification. The processed carbon data is further processed using a distributed ledger in the blockchain network in the smart contract layer for immutable carbon data storage, sharing and communication. In particular, a smart contract with fine-grained access control can be provided in GPChain to control or in some cases limit user interactions within the blockchain network, allowing only authorized users to call and generate corresponding transactions, which enhances the security and certification credibility of the smart contract. After blockchain transactions are generated based on the received carbon data for CMP certification via the smart contract layer, these transactions can be distributed in a decentralized and transparent blockchain network (e.g., an Ethereum blockchain network, etc.). It is worth noting that since CMP certification is a public commitment to both industry applications and social supervision, the level of decentralization and transparency required to maintain data integrity and security is much higher than that of private enterprise applications. In this case, Ethereum may be preferred compared to other blockchain platforms (such as Hyperledger Fabric). However, those skilled in the art will be able to recognize other blockchain platforms suitable for such applications. Finally, the data storage layer combines traditional databases and IPFS to store the original carbon data and the certification documents provided by the certification, respectively. It is understood that the platform for storing carbon data and / or carbon-related documents is not limited to IPFS, but may cover other suitable platforms or similar services. Additionally, when a user attempts to provide original carbon-related documents for certification, these files are first uploaded to the IPFS platform to generate a unique file value, which can then be processed using a smart contract and distributed in the blockchain network.The details of the key technical components of GPChain will be described in the following sections.

[0064] Multi-privacy carbon data model based on blockchain-based CMP certification

[0065] Figure 3 An exemplary process diagram showing the life cycle of CMP certification using GPChain. Figure 3 As shown in , the process includes the main information flows and data exchanges across different users in CMP carbon certification. The process diagram has four main stages, namely (1) Stage I: Collection and reporting of carbon emissions from raw material extraction by material suppliers, (2) Stage II: Collection and reporting of carbon emissions from upstream transportation during material delivery by material suppliers, (3) Stage III: Recording and calculation of carbon emissions from CMP manufacturing by manufacturers, and (4) Stage IV: Product certification process and certificate issuance by professional certification bodies. In the first two stages (i.e., Stage I and Stage II), the material supplier sends the certification documents of the emission factors of the materials and the transportation details in the delivery to the manufacturer. After receiving the materials and related information, the manufacturer begins to record the carbon footprint generated during the CMP manufacturing process, which includes material consumption details and energy consumption details (such as annual energy consumption bills). At the same time, the manufacturer collects all relevant certification documents from the material supplier and during the manufacturing stage to calculate the total carbon footprint (CFP) of the product. In Stage III, the manufacturer sends the application documents with the CFP results and all relevant certification documents to the certification body for verification. In Phase IV, the certification body will issue a certificate to the manufacturer after successful verification.

[0066] In addition, the blockchain data model can be configured based on the carbon data or documents required for the certification process. Figure 4 , for material suppliers, user input can be modeled as material source information. In particular, it can include basic supply chain information ("material supplier" and "material type"), material emission factors (EF) from material extraction ("material EF" and "unit of material EF"), and EF from upstream transportation ("transport EF" and "unit of transportation EF"). This information can be obtained from one or more certification documents provided by the user (in this case, the user is the material supplier). In addition, the original certification documents containing information about the material EF and transportation are also provided and stored in the blockchain network via the unique file hash value provided by IPFS. For manufacturers, there can be three types of user input corresponding to different activities (such as Figure 4). For example, material consumption and energy consumption information can be modeled to cover: (1) specific product information (such as "Manufacturer", "Product Category", and "Product Batch ID"), and (2) detailed quantity information that affects CFP calculations (such as "Material Consumption" and "Processing EF"). Another type of user input from manufacturers is to send an application request in GPChain, which includes attributes of basic product information and all related information record IDs during product manufacturing ("Set of TX IDs for material and energy consumption"). For certification authorities, their input is mainly related to certificate information, which can be modeled in GPChain to cover certificate-related attributes (such as "Issuing Authority" and "Certificate Number"). Return to reference Figure 2 , all these user inputs are provided in the data access layer, and after being processed (e.g., encrypted, etc.) in the data privacy layer, corresponding blockchain transactions can be generated (in the smart contract layer).

[0067] Additionally, although blockchain can provide significant advantages in carbon management during CMP certification, the deployment of a blockchain-based CMP certification framework may raise concerns about the protection of sensitive carbon data required in the certification (such as the manufacturer's partner raw material suppliers and material consumption during CMP manufacturing). Therefore, considering users' concerns about the privacy of sensitive carbon data during CMP certification, blockchain data models (such as Figure 4 ) classifies such data into three privacy levels. Details about the exemplary three privacy levels are provided below.

[0068] -Privacy Level 0: Non-sensitive information accessible to all users of GPChain. For example, the information provided in the certificate is transparent to all users because it has been verified and authenticated by a professional certification body. In addition, since GPChain aims to provide effective and reliable tracking of CMP carbon footprints, the carbon emissions from the components and transportation of CMPs (especially their EFs) can also be transparently available for public inspection.

[0069] -Privacy Level 1: Private information that should only be accessible by the intended recipient / user. For example, sensitive information that can only be shared (1) between a material supplier and a manufacturer, and (2) between a manufacturer and a certification body. In particular, manufacturers may be reluctant to share information about their business partners (such as suppliers) with the public (especially information about which raw materials are provided by which material supplier). Therefore, "Material Supplier", "Material Type" and certification documents provided by suppliers can be identified as privacy level 1 information that can only be accessed by manufacturers and / or other authorized users. Similarly, when a manufacturer submits a certification request to a certification body, the information (such as certification documents) should also only be accessed by the certification body and / or authorized users for verification.

[0070] -Privacy Level 2: The most private and sensitive information that should be kept confidential. For example, the "material consumption" amount of a particular type of CMP may implicitly reveal the manufacturer's company's trade secrets (such as product design), which may cause adverse effects if it is disclosed to other users, especially the manufacturer's competitors. From the manufacturer's perspective, the raw material consumption data should only be accessible to themselves, while the certification body can only access the CFP sum value based on the material consumption data (required in CMP certification) for verification purposes.

[0071] Understandably, Figure 4 The information shown in is not exhaustive and may include other relevant information, and a person skilled in the art will be able to assess and determine the privacy level of such other information.

[0072] Therefore, embodiments of the present disclosure provide a blockchain data model for CMP certification with multiple privacy levels at the data access layer of GPChain. This data model can be specifically used for blockchain-based CMP certification, which can (1) identify the attributes of carbon data stored and shared in a transparent blockchain network, and (2) consider these carbon data based on the attributes of carbon data in the context of CMP certification and classify these carbon data into appropriate privacy levels. However, we would like to emphasize that the number of privacy levels is not limited to the number described above (i.e., privacy levels 0 to 2), but can include more privacy levels according to the needs of the user, for example, privacy levels 0 to 3, privacy levels 0 to 4, privacy levels 0 to 5, etc.

[0073] Privacy-preserving carbon data sharing strategy for CMP certification

[0074] As described above, data marked with privacy level 1 and privacy level 2 should only be accessed by one or more authorized users to promote carbon data security during CMP certification. However, due to the distributed nature of blockchain, which allows all users in the blockchain network to access blockchain data, concerns have arisen about the data security of such carbon data. Therefore, an embodiment of the present disclosure provides a privacy-preserving carbon data sharing strategy that integrates asymmetric encryption and homomorphic encryption to promote data security and confidentiality during CMP certification. In an exemplary embodiment, the strategy can be implemented on carbon data at privacy level 1 and privacy level 2. In particular, for carbon data classified under privacy level 1 (i.e., carbon data that can be shared with authorized users), an asymmetric encryption scheme can be used to encrypt carbon data at privacy level 1, and authorized users can have a private key for decrypting carbon data encrypted using a corresponding public key, so that authorized users can access the carbon data after decryption. Furthermore, it should be noted that the public key of each user in a blockchain network is typically available to the public (i.e., all users of the blockchain network), such that any user in the blockchain network (e.g., a user who wants to send a message to another user) can use the other user's public key to encrypt the message.

[0075] Figure 5 The flowchart shown illustrates an exemplary process flow for processing privacy level 1 carbon data in a transaction between material supplier A and manufacturer 01 using an asymmetric encryption scheme as described above. Figure 5 As shown in FIG. 5 , at 500, material supplier A and manufacturer 01 distribute their asymmetric public keys via transactions in a blockchain network. At 502, when sharing privacy level 1 carbon data (e.g., “material supplier”, “material type”, etc.) and the original hash value of the certification document, material supplier A uses the public key of manufacturer 01 to encrypt the privacy level 1 carbon data, and at 504, a transaction with encrypted privacy level 1 data is generated and broadcast in the blockchain network. At 506 and 508, manufacturer 01 can decrypt the encrypted privacy level 1 carbon data by using the private key corresponding to the public key of manufacturer 01 to access the original privacy level 1 carbon data shared by material supplier A, and other users without the private key will not be able to decrypt the encrypted privacy level 1 carbon data.

[0076] For carbon footprint data classified under privacy level 2 (e.g., information that should be accessible only to the owner, such as raw material consumption during CMP manufacturing), the challenge is to perform the calculation of the CFP of the CMP without revealing the consumption of each raw material in the blockchain network. However, asymmetric encryption generally does not allow algebraic operations to be performed while hiding the content of the data. Therefore, embodiments of the present disclosure provide solutions to such problems by integrating homomorphic encryption into a privacy-preserving carbon data sharing strategy. In particular, homomorphic encryption is an encryption scheme that can allow specific types of calculations / operations to be performed on encrypted ciphertext and generate encrypted results. In other words, such an encryption scheme can allow a user to perform operations such as algebraic aggregation on a set of encrypted data without first decrypting any data, which allows the privacy of sensitive data to be protected when performing specific operations.

[0077] Figure 6 A flowchart is shown for illustrating an exemplary process flow of how privacy level 2 carbon data is processed in a transaction between manufacturer 01 and certification authority 01. In particular, at 600, manufacturer 01 uses the homomorphic public key of certification authority 01 to encrypt privacy level 2 carbon data (e.g., raw material consumption for manufacturing a certain product, etc.). Subsequently, at 602, one or more blockchain transactions are generated based on the encrypted privacy level 2 carbon data together with other processed carbon data at different privacy levels. At 604, during the CMP certification process, certification authority 01 collects all generated blockchain transactions about the manufacture of CMP and retrieves homomorphically encrypted privacy level 2 data. At 606, the CFP of the product can be calculated by obtaining the sum of each carbon emission (equal to the product of EF and consumption) based on the encrypted privacy level 2 data, that is, it is performed when the privacy level 2 data is encrypted. Therefore, an encrypted aggregate result based on the calculation can be obtained. Finally, at 608, certification authority 01 can obtain the total CFP result of CMP by decrypting the encrypted aggregate result using its homomorphic private key. Therefore, secure data aggregation during the authentication process can be achieved by adopting a homomorphic encryption scheme, which can allow the carbon computing function to be performed while avoiding any leakage of the manufacturer's trade secrets (i.e., privacy level 2 carbon data). In addition, it is worth emphasizing that Figure 6 The process flow for handling privacy level 0 and privacy level 1 data shown in Figure 5 The same or similar as shown in .

[0078] In summary, the embodiments of the present disclosure provide a privacy-preserving carbon data sharing strategy in the data privacy layer as follows. First, according to the privacy requirements of the carbon data, different types of carbon data are classified into different "privacy levels", such as privacy level 0, privacy level 1, privacy level 2, etc., based on a blockchain data model with multiple privacy. In addition, the privacy-preserving carbon data sharing strategy can implement different encryption schemes for carbon data of different privacy levels, such as asymmetric encryption schemes and homomorphic encryption schemes. For example, a homomorphic encryption scheme can be used to encrypt privacy level 2 data, so that operations can be performed on the encrypted privacy level 2 data while encrypting the data. Therefore, the use of the carbon data sharing strategy can alleviate manufacturers' concerns about carbon data leakage.

[0079] Blockchain-based CMP-certified fine-grained access control smart contract

[0080] Additionally, embodiments of the present disclosure provide smart contracts with fine-grained access control to control or in some cases limit user interactions in a blockchain network, which enhances smart contract security and CMP certification credibility in a decentralized and transparent blockchain network. Figure 7 A flowchart is shown for illustrating the execution workflow of three exemplary smart contracts used in GPChain, namely: (1) an encryption smart contract, which is a typical smart contract that enables all users in a blockchain network to distribute public keys for privacy protection, (2) a record smart contract with fine-grained access control, which can allow authorized users to call different smart contract functions for transactions related to the generation of carbon footprint records during CMP certification, and (3) a certification smart contract with fine-grained access control, which only allows (one or more) specific professional certification bodies to call functions for transactions used to generate CMP certificates. Figure 8 Exemplary algorithms for these smart contracts are shown in .

[0081] Back to reference Figure 7 (a) to Figure 7 (c), further details about smart contracts (1) to (3) are also provided as follows.

[0082] -Encrypted Smart Contracts( Figure 7 (a)): This smart contract can include two main functions: key distribution and key retrieval. All users in the blockchain network can call these two functions. The main input of the key distribution function is one or more public keys for asymmetric encryption and homomorphic encryption (only for certification authorities), and based on the input data provided, a transaction including user address and public key information can be generated as output. In addition, if Figure 7As shown in (a), an array can be developed to store public key information, and the array can be called by a key retrieval function to retrieve the public key to perform data encryption at the data privacy layer.

[0083] - Record smart contracts ( Figure 7 (b)): Typically, the manufacturer owns this smart contract. Fine-grained access control can be provided to the manufacturer in this smart contract to control or limit the access of users to call some functions in this smart contract function, which can help (1) avoid recording irrelevant carbon transactions conducted by non-cooperative material suppliers, and (2) improve the efficiency of transaction management and carbon tracking by collecting relevant carbon record transactions under the manufacturer's own smart contract. For example, the manufacturer can use the AddSupplier() and RevokeSupplier() functions to dynamically authorize specific material suppliers (e.g., suppliers with whom the manufacturer has a partnership) to record material origin information under the smart contract address. In addition, these two functions can only be successfully called by the owner of the smart contract (i.e., the manufacturer). In order to better manage the generation of different types of transactions, only users who have obtained valid authorization from the manufacturer through the AddSupplier() function can call the RecordMaterialOrigin() function to generate transactions about material origin information (such as Figure 4 ). Recording / uploading material consumption information (i.e., privacy level 2 data) to the blockchain network can only be done by the manufacturer by calling the RecordMaterialConsumption() function. Similarly, the RecordEnergyConsumption() function is used to record energy consumption information during product processing, which can only be called by the manufacturer.

[0084] - Authentication Smart Contract( Figure 7 (c)): Usually, the certification authority owns this smart contract. In this smart contract, fine-grained access control is provided for the function of the smart contract for the transaction that generates the CMP certification result. In particular, only the certification authority can call this function, which can prevent malicious attempts to forge CMP certification information. In addition, if Figure 7 As shown in (c), all relevant users can call the Application() function of this smart contract to generate an application transaction. However, only specific or authorized certification authorities can call the Certification() function, which is used to generate certification results. Any attempt by unauthorized manufacturers to call the Certification() function will be rejected to ensure that the authority of the certification authority and the credibility of the certification results are maintained.

[0085] In short, compared to traditional smart contracts, the smart contracts (1) to (3) described above can include fine-grained access control, thereby providing the ability to control or restrict certain smart contract functions to be called only by authorized users, which in turn controls user interactions in the blockchain network. Therefore, malicious activities such as forging CMP certification results and extending the validity period of CMP certification during blockchain-based CMP certification can be avoided at the very beginning of the CMP certification lifecycle. In addition, manufacturers can also better manage carbon record transactions related to their CMPs by authorizing their partner material suppliers to call smart contract functions and generate transactions required for CMP certification. In addition, through these smart contracts, since only authorized certification bodies can call relevant smart contract functions, the credibility of CMP certification results in blockchain transactions can be improved.

[0086] refer to Fig. 9 and Fig.10 , the embodiment of the present disclosure provides a method for managing carbon data using a blockchain network. The method 1000 can be used as follows Fig. 9 The system 900 shown in FIG. Fig. 9 A schematic diagram of a system 900 for managing carbon data using a blockchain network is shown. The system 900 includes a processing device 902. A user or user node 904 can, for example, access the system 900 through a user interface ( Fig. 9 ) to interact with the system 900. Alternatively, the user 904 may use a mobile device (not shown) that is communicatively coupled to the system 900. Fig. 9 900). Nevertheless, those skilled in the art will recognize any other suitable ways to implement the intended functionality. In addition, the processing device 902 can be communicatively coupled to the blockchain network 906 (e.g., to transmit blockchain transactions to the blockchain network 906), and / or communicatively coupled to the IPFS platform 908 for storing or receiving carbon-related data.

[0087] refer to Fig.10 , method 1000 may include the following steps.

[0088] Step 1002: Classifying, by a processing device, each of one or more carbon data received from at least one user of a blockchain network into a corresponding privacy level. Each privacy level in the corresponding privacy level represents a privacy requirement corresponding to each of the one or more carbon data.

[0089] In addition, each of the one or more carbon data may include basic product information and / or manufacturing information of at least one building material or product. In addition, the privacy requirements of each of the one or more carbon data may be predetermined based on implementing the basic product information and / or manufacturing information.

[0090] Step 1004: Encrypting, by the processing device, each carbon data in the one or more classified carbon data using one of a plurality of encryption schemes, wherein implementation of one of the plurality of encryption schemes is determined based on a privacy level of the carbon data.

[0091] In one embodiment, the corresponding privacy levels may include a first privacy level, a second privacy level, and a third privacy level. In addition, the carbon data at the first privacy level can be accessed by all users in the blockchain network, the carbon data at the second privacy level can be accessed by users authorized by the owner of the carbon data, and the carbon data at the third privacy level can only be accessed by the owner of the carbon data. In this embodiment, the multiple encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. In addition, the first level privacy carbon data and the second level privacy carbon data can be encrypted using an asymmetric encryption scheme, and the third level privacy level carbon data can be encrypted using a homomorphic encryption scheme.

[0092] Step 1006: Generate, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data.

[0093] One or more smart contracts may be used to generate one or more blockchain transactions. In addition, at least one of the one or more smart contracts may be configured to only allow at least one user authorized by an owner of at least one of the one or more smart contracts to call at least one function of the at least one of the one or more smart contracts.

[0094] Step 1008: The processing device transmits one or more blockchain transactions to the blockchain network.

[0095] Optionally, the method 1000 may include a further step of mapping, by the processing device, each of the one or more carbon data to a corresponding privacy level based on a predetermined privacy requirement.

[0096] Feasibility study

[0097] Fig.11 An exemplary workflow for testing an implementation of GPChain is shown, which includes but is not limited to three main parts, namely (1) blockchain function development, (2) Ethereum node generation, and (3) Ethereum blockchain deployment. Fig.12An example of a user interface (UI) of GPChain is shown, which includes but is not limited to three main functional modules, namely (1) encryption key management, (2) carbon footprint recording, and (3) application and certification. Users can call these modules to achieve different functionalities according to their roles and responsibilities. In addition, in this example, Etherscan for Goerli Testnet is integrated with the UI in the blockchain module so that users can effectively track carbon footprints during CMP certification. In order to demonstrate the feasibility of GPChain, a real CMP carbon certification situation of a test product (concrete, 45 / 20D, 125mm slump) is selected to show blockchain-based carbon management for CMP certification. In a scenario-based manner, real data of the test product collected from materials and documents prepared for the CIC Green Product Certification Scheme in Hong Kong, China is used to demonstrate the carbon certification process. For this demonstration, an Ethereum blockchain network including eight participants (i.e., concrete manufacturers, certification agencies, and six material suppliers) is established. Then, the blockchain network is deployed in two example carbon certification scenarios (given below) to demonstrate the feasibility of GPChain.

[0098] Example Scenario 1: Recording Carbon Footprint for CMP

[0099] This example scenario mainly describes the process of recording the carbon footprint of a CMP before performing further verification and certification. It is designed to verify the workflow in GPChain for CMP certification, as well as the functionality of the developed carbon data privacy protection strategy and secure smart contract interaction scheme. FIG. 13A to FIG. 13C The process flow of Example Scenario 1 including three key activities is shown. In Activities 1 and 2, all users in the blockchain network distribute their public keys (for asymmetric encryption and homomorphic encryption) in the UI developed for sensitive carbon data sharing. The manufacturer then authorizes its partner material suppliers via blockchain transactions so that these partner material suppliers can successfully call the recording function based on the recording smart contract to record the carbon footprint of a specific CMP. In Activity 3, an authorized partner material supplier records the carbon footprint data of the material by: (1) using the manufacturer's asymmetric public key to encrypt its company name and material type; (2) directly entering the EF and transportation EF information of the material; and (3) using the manufacturer's asymmetric public key to encrypt the hash value of the certification document corresponding to the material EF information (generated by IPFS). It should be noted that FIG. 13A to FIG. 13C This paper mainly provides an example of how material suppliers can record carbon footprints for CMP certification. The process of recording carbon footprints for manufacturers in GPChain is similar, but can include an additional step of performing homomorphic encryption on material consumption (i.e., privacy level 2 carbon data) (by using the homomorphic public key of the certification authority).

[0100] The verification results of example scenario 1 are in Fig.14 In particular, the results show that: (1) multiple users in Example Scenario 1 have successfully participated in GPChain via Ethereum transactions and recorded corresponding carbon footprints under the same recording smart contract; (2) material suppliers successfully recorded carbon footprints in GPChain based on a secure smart contract interaction scheme after obtaining authorization; (3) carbon footprint data in GPChain transactions are well protected via encryption and the original carbon data can only be accessed by authorized users with corresponding private keys. Therefore, the objectives of this example scenario are successfully verified.

[0101] Example Scenario 2: Application and Certification of CMP

[0102] Fig.15A and Fig. 15B Another example scenario for the application and certification process for verifying a tested product is shown below. Fig.15A and Fig. 15B As shown in , in Activity 1, the manufacturer first reports basic information about the tested product, such as product type, product batch, and total CFP, etc. Then, the manufacturer collects all corresponding transactions, prepares other application documents via IPFS and encryption strategy, and sends the application request in the UI. Activity 2 illustrates the detailed verification process implemented by the certification authority after receiving the application request. The certification authority can access the application materials from IPFS after obtaining the decrypted hash value. By using the transaction ID provided in Activity 1, the certification authority can track each transaction for further verification. For the transaction in this example scenario, the certification authority (1) decrypts the ciphertext of the material type and material EF proof with an asymmetric private key to access the original information, and verifies whether the original information is consistent with the transaction of the raw material recorded by the material supplier, and (2) retrieves the material total EF information and the homomorphically encrypted quantity information. The certification authority then performs secure aggregation to obtain the actual CFP value, and compares the actual CFP value with the value reported in the application transaction. After successfully verifying the corresponding transaction and supporting documents, in activity 3, the certification authority attaches the issuance and certificate information to generate a formal transaction for the certified tested product, which is broadcast in the blockchain network. The verification result of example scenario 2 is Fig.16 In particular, the verification results show that the transactions of the application and certification processes are generated in the Ethereum blockchain network, and they are successfully distributed in the blockchain network, and they can be searched in Etherscan. Therefore, the goal of Example Scenario 2 is successfully verified.

[0103] Advantageously, if Figure 2The well-structured blockchain data format shown in (i.e., splitting the blockchain model into "data access layer", "data privacy layer", etc.) is combined with the above-described and Figures 5 to 7 The privacy scheme shown in (i.e., classifying carbon data into different privacy levels, privacy-preserving carbon data sharing strategies, etc.) can enhance the performance of GPChain by reducing blockchain arrival latency and improving throughput. Therefore, the cost of completing transactions using GPChain can be reduced. For example, Fig.14 and Fig.16 As shown, due to the transparent carbon certification function designed for public services and the well-structured blockchain data format for complete information coverage, the transactions in example scenarios 1 and 2 are generated without token transfer (i.e., eliminating the use of tokenization), which improves the performance of GPChain. The following sections provide a performance and cost evaluation of GPChain.

[0104] Performance Evaluation

[0105] In order to evaluate the performance of GPChain, two performance indicators (i.e., latency and throughput) were selected and tested for GPChain's smart contracts. The latency is usually defined as the time interval between the time when a transaction is first sent to the blockchain and the time when the transaction is confirmed by the user of the blockchain network (as shown in formula (1)), and in this case, it is a measure of the data delivery efficiency of transactions generated using GPChain's smart contracts. Therefore, the latency can be used to assess the latency of transactions generated via different smart contracts in GPChain. Throughput can be defined by the number of transactions per second (TPS) that the blockchain network can successfully process or submit to the blockchain ledger within a given time frame. In this case, it is a measure of the scalability of GPChain's smart contracts, which can be used to show the user bandwidth or range of GPChain that can access different smart contracts within a given time frame. In addition, TPS can be calculated using the following formula (2), which is based on dividing the number of transactions per block by the period of block generation. In this performance evaluation, the latency and TPS of three smart contracts (i.e., encryption smart contract, recording smart contract, and authentication smart contract) are measured separately by calling all functions of the corresponding smart contracts. The final values ​​of the deposit delay and TPS for each smart contract are obtained by averaging the calculated or measured deposit delay and TPS of different smart contract function calls. Considering the limited Testnet ETH available from Goerli, each activity of calling the smart contract function is measured ten times. Fig.17 and Fig.18 The performance evaluation results of the arrival delay and throughput of the three smart contracts are shown respectively.

[0106] Delay in arrival = t Tx确认 -t Tx发送 (1)

[0107]

[0108] like Fig.17 As shown in , the average account delay for encrypted smart contracts, record smart contracts, and certified smart contracts is about 10 seconds, which means that it takes about 10 seconds for a user to generate a transaction containing carbon footprint information or carbon data and get it confirmed in the distributed ledger of the blockchain network. Since the public blockchain network based on Ethereum is used for this evaluation, a certain time interval is required before most blockchain network users accept the transaction. In this regard, the average time for Ethereum-based transactions to be completed is about 15 seconds. It can be seen that the account delay of the three smart contracts (i.e., about 10 seconds) is faster than the average known account delay of the Ethereum network (i.e., 15 seconds).

[0109] Fig.18 The throughput results of encryption smart contract, recording smart contract and authentication smart contract are shown. Fig.18 As shown in Figure 2, approximately 26.76, 25.72, and 25.53 transactions can be processed per second for encryption smart contracts, record smart contracts, and certification smart contracts, respectively. Considering that the average known throughput of the Ethereum platform is approximately 5.55 transactions per second (e.g. Fig.18 As shown in Figure 2, the throughput of the three smart contracts shows significantly higher performance (almost five times) compared to the average known throughput of the Ethereum platform, which means that more transactions can be processed using GPChain.

[0110] Cost Assessment

[0111] Unlike private blockchain platforms, when users execute transactions on public blockchain platforms (e.g., Ethereum, etc.), they need to pay transaction fees. In addition to the technical development and maintenance costs of blockchain-based platforms, it is also important to measure the execution cost of blockchain transactions in public blockchain platforms, because fees for storing data will be generated for each blockchain transaction. In Ethereum, this transaction fee is represented by "Gas" (fuel fee), which refers to the fee required to successfully process blockchain transactions. As the basic unit of network cost, Gas is usually paid with Ether (the cryptocurrency used in Ethereum) and is often used to evaluate the economic performance and resource consumption of smart contracts. Therefore, in this evaluation, the Gas price (in Ether) is used for the cost estimation of each of the three smart contracts to evaluate their execution costs. The final execution cost of the three smart contracts is calculated by obtaining the average cost of the called function (i.e., each function call is implemented ten times and then the average is obtained).

[0112] Fig.19 The average execution cost of each of the three smart contracts (i.e., encryption smart contract, record smart contract, and certification smart contract) is shown. In order to provide an assessment relevant to the real-world market, the Gas price generated by each transaction (in ETH) is converted into Hong Kong dollars (HKD) based on the real-time Ethereum price monitored by Google Finance on December 29, 2022. Fig.19 , the average cost per smart contract ranges from approximately HKD 0.001 to HKD 0.105. The evaluation results show that low transaction fees are incurred for transactions based on the encryption smart contract, because transactions based on this smart contract contain the least information relative to the other two smart contracts. On the other hand, the cost per transaction based on the certification smart contract is the highest, approximately HKD 0.105 (because such transactions usually contain the most information), and the cost per transaction based on the recording smart contract is approximately HKD 0.035. Therefore, assuming that five users (i.e., three material suppliers, one manufacturer, and one certification body) participate in the certification process, the total cost of fully executing the entire product certification cycle (distributing encryption keys, authorizing material suppliers, recording carbon footprint information, sending application requests, and broadcasting product certificates) using GPChain will cost approximately HKD 0.46. Compared to a similar distributed service provided by Google Cloud Platform with a fixed component cost of USD 244.59 per month (approximately HKD 1,960) for five participating users, GPChain clearly shows better economic performance and can perform more than 4,000 certification cycles at the same maintenance cost of USD 244.59 per month.

[0113] Fig. 20 Depicted is an exemplary computing device 2000, which may be referred to interchangeably hereinafter as computer system 2000. The following description of computing device 2000 is provided by way of example only and is not intended to be limiting.

[0114] like Fig. 20 As shown in the example computing device 2000, the example computing device 2000 includes a processor 2002 for executing software routines. Although a single processor is shown for clarity, the computing device 2000 may also include a multi-processor system. The processor 2002 is connected to a communication infrastructure 2004 for communication with other components of the computing device 2000. For example, the communication infrastructure 2004 may include a communication bus, a crossbar switch, or a network.

[0115] The computing device 2000 also includes a main memory 2006, such as a random access memory (RAM), and a secondary memory 2008. For example, the secondary memory 2008 may include a hard disk drive 2010 and / or a removable storage drive 2012, which may include a floppy disk drive, a tape drive, an optical disk drive, etc. The removable storage drive 2012 reads from and / or writes to a removable storage unit 2014 in a well-known manner. The removable storage unit 2014 may include a floppy disk, a tape, an optical disk, etc., which is read and written by the removable storage drive 2012. It will be appreciated by those skilled in the relevant art that the removable storage unit 2014 includes a computer-readable storage medium having computer-executable program code instructions and / or data stored therein.

[0116] In alternative embodiments, the secondary memory 2008 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into the computing device 2000. Such means may include, for example, a removable storage unit 2016 and an interface 2018. Examples of removable storage units 2016 and interfaces 2018 include program cartridges and cartridge interfaces (such as found in video game console devices) that allow software and data to be transferred from the removable storage unit 2016 to the computer system 2000, removable memory chips (such as EPROM or PROM) and associated sockets, and other removable storage units 2016 and interfaces 2018.

[0117] The computing device 2000 also includes at least one communication interface 2020. The communication interface 2020 allows software and data to be transmitted between the computing device 2000 and an external device via a communication path 2022. In various embodiments of the present invention, the communication interface 2020 allows data to be transmitted between the computing device 2000 and a data communication network (such as a public data or private data communication network). The communication interface 2020 can be used to exchange data between different computing devices 2000, which form part of an interconnected computer network. Examples of the communication interface 2020 may include a modem, a network interface (such as an Ethernet card), a communication port, an antenna with associated circuits, etc. The communication interface 2020 may be wired or wireless. The software and data transmitted via the communication interface 2020 are in the form of a signal, which may be an electronic, electromagnetic, optical or other signal that can be received by the communication interface 2020. These signals are provided to the communication interface via the communication path 2022.

[0118] like Fig. 20As shown, the computing device 2000 also includes a display interface 2024 and an audio interface 2028 . The display interface 2024 performs operations for presenting images to an associated display 2026 , and the audio interface 2028 performs operations for playing audio content via an associated speaker 2030 .

[0119] As used herein, the term "computer program product" may refer in part to a removable storage unit 2014, a removable storage unit 2016, a hard disk installed in the hard disk drive 2010, or a carrier that carries the software through a communication path 2022 (wireless link or cable) to the communication interface 2020. Computer-readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and / or data to the computing device 2000 for execution and / or processing. Examples of such storage media include floppy disks, magnetic tapes, CD-ROMs, DVDs, Blu-ray TM The computer readable medium may be a disk, a hard drive, a ROM or integrated circuit, a USB memory, a magneto-optical disk, or a computer-readable card such as a PCMCIA card, etc., whether such a device is internal or external to the computing device 2000. Examples of transient or non-tangible computer-readable transmission media that may also be involved in providing software, applications, instructions and / or data to the computing device 2000 include radio or infrared transmission channels and a network connection to another computer or networked device, as well as the Internet or an intranet including electronic mail transmissions and information recorded on websites, etc.

[0120] The computer program (also referred to as computer program code) is stored in the main memory 2006 and / or the secondary memory 2008. The computer program may also be received via the communication interface 2020. Such a computer program, when executed, enables the computing device 2000 to perform one or more features of the embodiments discussed herein. In various embodiments, the computer program, when executed, enables the processor 2002 to perform the various features of the embodiments described above. Thus, such a computer program represents a controller of the computer system 2000.

[0121] The software may be stored in a computer program product and loaded into the computing device 2000 using the removable storage drive 2012, the hard drive 2010, or the interface 2018. Alternatively, the computer program product may be downloaded to the computer system 2000 via the communication path 2022. The software, when executed by the processor 2002, causes the computing device 2000 to perform the functions of the embodiments described herein.

[0122] Understandably, Fig. 20The embodiments are given by way of example only. Therefore, in some embodiments, one or more features of computing device 2000 may be omitted. Furthermore, in some embodiments, one or more features of computing device 2000 may be combined together. Additionally, in some embodiments, one or more features of computing device 2000 may be separated into one or more components.

[0123] Understandably, Fig. 20 The various elements shown in the figure are used to provide means for executing various functions and operations of the server described in the above-described embodiments.

[0124] In an embodiment, a server can be generally described as a physical device including at least one processor and at least one memory containing computer program code. The at least one memory and the computer program code are configured to work with the at least one processor to cause the physical device to perform necessary operations.

[0125] When the computing device 2000 is configured to implement the system 900 for managing carbon data using a blockchain network, the system 900 may have a non-transitory computer-readable medium having an application stored thereon, which, when executed, causes the system 900 to perform a method 1000 comprising the following steps 1000 to 1008.

[0126] Step 1002: Classifying, by a processing device, each of one or more carbon data received from at least one user of a blockchain network into a corresponding privacy level. Each privacy level in the corresponding privacy level represents a privacy requirement corresponding to each of the one or more carbon data.

[0127] In addition, each of the one or more carbon data may include basic product information and / or manufacturing information of at least one building material or product. In addition, the privacy requirements of each of the one or more carbon data may be predetermined based on the basic product information and / or manufacturing information.

[0128] Step 1004: For each carbon data of the one or more classified carbon data, encrypting, by a processing device, one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on a privacy level of the carbon data.

[0129] In one embodiment, the corresponding privacy levels may include a first privacy level, a second privacy level, and a third privacy level. In addition, the carbon data at the first privacy level can be accessed by all users in the blockchain network, the carbon data at the second privacy level can be accessed by users authorized by the owner of the carbon data, and the carbon data at the third privacy level can only be accessed by the owner of the carbon data. In this embodiment, the multiple encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. In addition, the first level privacy carbon data and the second level privacy carbon data can be encrypted using an asymmetric encryption scheme, and the third level privacy level carbon data can be encrypted using a homomorphic encryption scheme.

[0130] Step 1006: Generate, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data.

[0131] One or more smart contracts may be used to generate one or more blockchain transactions. In addition, at least one of the one or more smart contracts may be configured to only allow at least one user authorized by an owner of at least one of the one or more smart contracts to call at least one function of the at least one of the one or more smart contracts.

[0132] Step 1008: The processing device transmits one or more blockchain transactions to the blockchain network.

[0133] Optionally, the method 1000 may include a further step of mapping, by the processing device, each of the one or more carbon data to a corresponding privacy level based on a predetermined privacy requirement.

[0134] in conclusion

[0135] In summary, the embodiments of the present disclosure provide a blockchain-based framework for secure carbon management during CMP certification. Compared with the existing process of proposing a theoretical framework for building carbon management, the blockchain-based framework and workflow proposed in this application are designed based on real-world research, that is, not at a conceptual level. First, the embodiments of the present disclosure provide a blockchain data model with multiple privacy levels for CMP certification, which can identify carbon data provided by one or more users in a blockchain network and their sensitivity / privacy requirements, where the data can be classified into different privacy levels based on the privacy requirements of each carbon data. In addition, the blockchain-based framework can also provide structured carbon data and information flows for CMP certification in a blockchain environment. In addition, the embodiments of the present disclosure provide a fine-grained privacy-preserving carbon data sharing strategy for sensitive carbon data required in CMP certification. The privacy-preserving carbon data sharing strategy can enable fine-grained security protection of sensitive carbon data by integrating asymmetric encryption schemes and homomorphic encryption schemes with blockchain, which particularly meets the security carbon data sharing requirements of different privacy levels. In addition, the embodiments of the present disclosure also provide fine-grained access control smart contracts to enable secure user interaction in blockchain-based CMP carbon certification. In particular, fine-grained access control can directly control or restrict users from calling certain smart contracts or smart contract functions.

[0136] Finally, future work can be directed towards (1) developing a comprehensive blockchain-based CMP carbon certification application that includes user registration and access control for reading and writing data, which will help improve the user experience for further promotion throughout the industry; and (2) integrating with international standards for carbon footprint quantification and reporting to provide users with a standardized carbon management process.

[0137] It will be appreciated by those skilled in the art that, without departing from the spirit or scope of the invention as broadly described, various changes and / or modifications may be made to the invention shown in the specific embodiments. Therefore, the present embodiments are considered in all aspects to be illustrative and not restrictive.

Claims

1. A method for managing carbon data using a blockchain network, the method comprising: classifying, by a processing device, each of the one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level, wherein each of the corresponding privacy levels represents a privacy requirement corresponding to each of the one or more carbon data; encrypting, by the processing device, each of the one or more classified carbon data using one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on a privacy level of the carbon data; generating, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data; as well as The one or more blockchain transactions are transmitted by the processing device to the blockchain network.

2. A method according to claim 1, wherein each of the one or more carbon data includes basic product information and / or manufacturing information of at least one building material or product, and wherein the privacy requirement of each of the one or more carbon data is predetermined based on the basic product information and / or the manufacturing information.

3. The method according to claim 2, wherein the method further comprises: Each of the one or more carbon data is mapped, by the processing device, to a corresponding privacy level based on the predetermined privacy requirement.

4. The method according to claim 1, wherein the corresponding privacy levels include a first privacy level, a second privacy level, and a third privacy level, and wherein the carbon data of the first privacy level is accessible to all users in the blockchain network, the carbon data of the second privacy level is accessible to users authorized by the owner of the carbon data, and the carbon data of the third privacy level is accessible only to the owner of the carbon data.

5. A method according to claim 4, wherein the multiple encryption schemes include an asymmetric encryption scheme and a homomorphic encryption scheme, wherein the first level privacy carbon data and the second level privacy carbon data are encrypted using the asymmetric encryption scheme, and wherein the third level privacy carbon data is encrypted using the homomorphic encryption scheme.

6. The method of claim 1 , wherein the one or more blockchain transactions are generated using one or more smart contracts, and wherein at least one of the one or more smart contracts is configured to only allow at least one user authorized by an owner of at least one of the one or more smart contracts to call at least one function of the at least one of the one or more smart contracts.

7. The method of claim 6, wherein the method is implemented based on a blockchain-based model, and wherein the blockchain-based model includes an architecture having the following features: A data access layer, wherein the step of classifying is performed in the data access layer; a data privacy layer, wherein said step of encrypting is performed in said data privacy layer; and A smart contract layer, wherein the generating step is performed in the smart contract layer.

8. A system for managing carbon data using a blockchain network, wherein the system comprises a processing device configured to: classifying each of the one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level, wherein each of the corresponding privacy levels represents a privacy requirement corresponding to each of the one or more carbon data; encrypting each carbon data of the one or more classified carbon data using one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on a privacy level of the carbon data; generating one or more blockchain transactions corresponding to each of the one or more encrypted carbon data; as well as Transmitting the one or more blockchain transactions to the blockchain network.

9. A system according to claim 8, wherein each of the one or more carbon data includes basic product information and / or manufacturing information of at least one building material or product, and wherein the privacy requirement of each of the one or more carbon data is predetermined based on the basic product information and / or the manufacturing information.

10. The system of claim 9, wherein the processing device is further configured to: Each of the one or more carbon data is mapped to a corresponding privacy level based on the predetermined privacy requirement.

11. A system according to claim 8, wherein the corresponding privacy levels include a first privacy level, a second privacy level, and a third privacy level, and wherein the carbon data at the first privacy level is accessible to all users in the blockchain network, the carbon data at the second privacy level is accessible to users authorized by the owner of the carbon data, and the carbon data at the third privacy level is accessible only to the owner of the carbon data.

12. A system according to claim 11, wherein the multiple encryption schemes include an asymmetric encryption scheme and a homomorphic encryption scheme, wherein the first level privacy carbon data and the second level privacy carbon data are encrypted using the asymmetric encryption scheme, and wherein the third level privacy carbon data is encrypted using the homomorphic encryption scheme.

13. The system of claim 8, wherein the one or more blockchain transactions are generated using one or more smart contracts, and wherein at least one of the one or more smart contracts is configured to only allow at least one user authorized by an owner of at least one of the one or more smart contracts to call at least one function of the at least one of the one or more smart contracts.

14. The system of claim 13, wherein the processing device is further configured to implement a blockchain-based model, and wherein the blockchain-based model comprises an architecture having the following features: a data access layer, wherein classifying each of the one or more carbon data received from at least one user of the blockchain network into a corresponding privacy level is performed in the data access layer; a data privacy layer, wherein encryption is performed in the data privacy layer on each of the one or more classified carbon data using one of a plurality of encryption schemes; and A smart contract layer, wherein the generating one or more blockchain transactions corresponding to each of the one or more encrypted carbon data is performed in the smart contract layer.

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