Method and system for managing carbon data using a blockchain network

Abstract

A method of managing carbon data using a blockchain network is provided. The method includes classifying, by a 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 further 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 the privacy level of the carbon data. The method further 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-references 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] This invention relates broadly, but not exclusively, to methods and systems for managing carbon data using blockchain networks. Background Technology

[0004] As one of the largest consumers of resources 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 building materials and products (CMPs), providing a foundation for carbon management at the CMP level. However, existing carbon management for CMP certification relies heavily on traditional centralized data management tools, which suffer from 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 blockchain networks. 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 having a processing device 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 method further includes having the processing device encrypt each of the one or more classified carbon data using one of a plurality of encryption schemes. The one encryption scheme is determined based on the privacy level of the carbon data. The method further includes having the processing device generate one or more blockchain transactions corresponding to each of the one or more encrypted carbon data, and having the processing device transmit the one or more blockchain transactions to the blockchain network.

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

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

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

[0010] In this embodiment, the multiple encryption schemes may include asymmetric encryption schemes and homomorphic encryption schemes. First-level privacy carbon data and second-level privacy carbon data can be encrypted using an asymmetric encryption scheme, and third-level privacy carbon data can be encrypted using a homomorphic encryption scheme.

[0011] In embodiments 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 allow only 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] Alternatively, this method can be implemented based on a blockchain-based model. A blockchain-based model can include an architecture with 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 in the data privacy layer, and the generation step in the smart contract layer.

[0013] According to a second aspect of the 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 further configured to encrypt each of the one or more classified carbon data using one of a plurality of encryption schemes. The one encryption scheme is determined based on the privacy level of the carbon data. The processing device is further configured to generate one or more blockchain transactions corresponding to each of the one or more encrypted carbon data and to transmit the one or more blockchain transactions to the blockchain network.

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

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

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

[0017] In embodiments 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 allow only 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.

[0018] In embodiments of the 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 carbon data point from one or more carbon data points received from at least one user of the blockchain network into a corresponding privacy level can be performed in the data access layer. Encrypting each carbon data point from one or more classified carbon data points using one of a plurality of encryption schemes can be performed in the data privacy layer. The generation of one or more blockchain transactions corresponding to each of the one or more encrypted carbon data points can be performed in the smart contract layer. Attached Figure Description

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

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

[0021] Figure 2 Another flowchart illustrating an exemplary framework for managing carbon data using a blockchain network, according to an embodiment, is shown.

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

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

[0024] Figure 5 A flowchart illustrating an example process flow for processing carbon data at privacy level 1, according to an embodiment, is shown.

[0025] Figure 6 A flowchart illustrating an example process flow for processing privacy level 2 carbon data, according to an embodiment.

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

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

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

[0029] Figure 10 A flowchart illustrating a method for managing carbon data using a blockchain network, according to an embodiment, is shown.

[0030] Figure 11 The illustration shows a flowchart of an example workflow for implementing a framework for managing carbon data using a blockchain network, according to an embodiment.

[0031] Figure 12 An example of a user interface display for a system that uses a blockchain network to manage carbon data according to an embodiment is shown.

[0032] Figures 13A to 13C The process flow for a first example scenario according to an embodiment is shown.

[0033] Figure 14 The verification results for a first example scenario according to an embodiment are shown.

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

[0035] Figure 16 The verification results for a second example scenario according to the embodiment are shown.

[0036] Figure 17 A graph illustrating the arrival delay of transactions generated based on a cryptographic smart contract, a recording smart contract, and an authentication smart contract, according to an embodiment, is shown.

[0037] Figure 18A graph illustrating the throughput of transactions generated based on cryptographic smart contracts, recording smart contracts, and authentication smart contracts, according to an embodiment, is shown.

[0038] Figure 19 A graph illustrating the costs of transactions generated based on cryptographic smart contracts, recording smart contracts, and authentication smart contracts, according to an embodiment, is shown.

[0039] Figure 20 A schematic diagram of an exemplary computing device is shown according to an embodiment for implementing a system for managing carbon data using a blockchain network. Detailed Implementation

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

[0041] Some parts of the following description are given explicitly or implicitly in the form of algorithms and functions or symbolic representations of operations on data in computer memory. These algorithmic descriptions and functions or symbols are the means by which those skilled in the art of data processing most effectively communicate their substantive work to others skilled in the art. An algorithm herein is generally considered to be a self-consistent sequence of steps that leads to a desired result. Such steps are those that require physical manipulation of physical quantities, such as electrical, magnetic, or optical signals that can be stored, transmitted, combined, compared, and otherwise manipulated.

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

[0043] This specification also discloses apparatus for performing these methods. Such apparatus may be specifically constructed for the desired purpose or may include a computer or other devices selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with the programs taught herein. Alternatively, it may be appropriate to construct more specialized apparatus to perform the desired method steps. The structure of a conventional computer will become apparent from the description below.

[0044] Furthermore, this specification implicitly discloses a computer program, as it will be apparent to those skilled in the art that the various steps of the methods described herein can be implemented using computer code. The computer program is not intended to be limited to any particular programming language or implementation thereof. It is understood that the teachings of the disclosure contained herein can be implemented using a variety of programming languages ​​and their encodings. Moreover, the computer program is not intended to be limited to any particular control flow. Many other variations of the computer program using different control flows exist without departing from the spirit or scope of the invention.

[0045] Furthermore, one or more steps of a computer program can be executed in parallel rather than sequentially. Such a computer program can be stored on any computer-readable medium. Computer-readable media can include storage devices such as disks or optical discs, memory chips, or other storage devices suitable for interfacing with a computer. Computer-readable media can also include hardwired media (such as those exemplified in Internet systems) or wireless media (such as those exemplified in GSM, GPRS, 3G, or 4G mobile phone systems) and other wireless systems (such as Bluetooth, ZigBee, Wi-Fi). When the computer program is loaded and executed on such a computer, it effectively creates means for implementing the steps of the preferred method.

[0046] This invention can also be implemented as a hardware module. More specifically, in a hardware sense, a module is a functional hardware unit designed to be used in conjunction with other components or modules. For example, a module can be implemented using discrete electronic components, or the module can be part of an overall electronic circuit (such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA)). Many other possibilities exist. Those skilled in the art will understand that the system can also be implemented as a combination of hardware and software modules.

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

[0048] Application Programming Interfaces (APIs) enable software and applications to communicate with each other. They are software-to-software interfaces that allow independent parties to communicate with each other without any prior user knowledge or intervention. Generally, they are a well-defined set of communication methods between various software components.

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

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

[0051] introduce

[0052] In 2021, the construction and building industry accounted for over 34% of energy demand and approximately 37% of energy and process-related CO2 emissions. Therefore, the construction and building industry faces increasing pressure to reduce carbon emissions throughout its lifecycle. While various carbon reduction methods are used to reduce energy consumption during the building operation phase, the importance of reducing embedded carbon from the construction phase cannot be diminished, as it accounts for nearly 30% of greenhouse gas (GHG) emissions throughout the building lifecycle. Of the various emission sources during the construction phase, the extraction and manufacturing of building materials contribute approximately 70% of GHG emissions. Specifically, cement production accounts for approximately 5% to 8% of anthropogenic CO2 emissions, while steel production generates approximately 7% of global CO2 emissions. Furthermore, the transportation of raw materials to product manufacturing plants (especially long-haul international transport of raw materials) is energy-intensive. Therefore, developing strategies to promote carbon management at the building materials and products (CMP) level is crucial for achieving the construction and building industry's emissions reduction targets.

[0053] Furthermore, given the importance of reducing carbon emissions from construction companies (CMPs), numerous efforts have focused on carbon management throughout their lifecycle. For example, a series of international standards have been published to identify standardized processes and steps for managing carbon footprints at 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, serving as practical and meaningful benchmarks to help construction companies effectively measure and manage their CMP carbon footprints. These schemes are based on a public commitment, as the CMP carbon footprint has been measured and certified at environmentally friendly levels by professional bodies or government departments, serving 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 the lack of sufficient transparency in 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. Moreover, some companies exploit data transparency loopholes for "greenwashing," such as by falsifying carbon data or using false labels to make misleading environmental claims to obtain more carbon credits or green financing, negatively impacting customer satisfaction and ultimately reducing the company's competitiveness. Others might even attack the servers of centralized environmental platforms to inject false data for sustainability propaganda purposes. Therefore, public concern about carbon data falsification arises due to issues of transparency and reliability in existing carbon management methods.

[0054] As an emerging and promising technology, blockchain offers powerful solutions to alleviate data transparency and reliability issues. Unlike centralized systems, blockchain typically operates under a distributed peer-to-peer network without intermediaries, minimizing reliance on centralized institutions. Therefore, blockchain provides data transparency and reliability, and simultaneously, through easily identifiable and immutable information records, it enhances data traceability. Due to these advantages, blockchain can serve as a transparent, reliable, and traceable data management tool for managing the carbon footprint of CMPs during the certification process. Without intermediaries or central authorities, the carbon footprint of a CMP can be collectively maintained in a transparent environment by the relevant stakeholders / users (e.g., raw material suppliers, manufacturers, and certification bodies), promoting the reliability of carbon footprint data in CMP certification. Furthermore, blockchain can provide users with effective tracking capabilities to help customers or other users quickly identify the carbon footprint of certified CMPs.

[0055] While blockchain offers significant potential for carbon management applications during CMP certification, challenges remain to be addressed in its implementation. A major challenge is the leakage of sensitive carbon footprint data during blockchain-based CMP certification. For a given CMP from a manufacturer, certain carbon data during manufacturing (such as partner material suppliers, material consumption, and energy consumption) is private and sensitive. However, detailed knowledge of material suppliers and consumption is required to assess a CMP's carbon footprint during the certification process. Inappropriate data storage and sharing mechanisms for this private and sensitive carbon data could lead to the leakage of trade secrets (such as manufacturing formulations and procurement strategies for CMP raw materials), potentially benefiting the manufacturer's competitors and harming the CMP manufacturer's interests. Therefore, disclosing such proprietary information often raises concerns among stakeholders. For example, competitors might use the manufacturer's GHG emissions to assess their operational growth and efficiency, while non-governmental organizations (NGOs) might use the same information to pressure the manufacturer to improve environmental performance. Because data stored on a blockchain is typically transparent to all users on that blockchain network, it is difficult to get manufacturers to record the carbon data required for CMP certification on the blockchain network. Another challenge may arise from uncontrolled user interaction within blockchain networks to generate carbon trades. Specifically, interaction with the blockchain can generally be invoked by any user within the blockchain network via smart contracts; that is, any user can interact with the blockchain to generate any type of trade (trades containing authentication results). However, authentication trades are only valid if they are generated by the authority responsible for authentication. Otherwise, the credibility of authentication results may be negatively impacted, and the frequency of invalid authentication trades may increase. Therefore, addressing these challenges is important when applying blockchain to CMP authentication. This disclosure specifically addresses the following issues:

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

[0057] 2) In the decentralized blockchain environment of CMP certification, how can carbon data privacy be promoted and user interaction with the blockchain be protected during the certification process?

[0058] Therefore, embodiments of this disclosure provide a decentralized, blockchain-based framework integrated with carbon data protection schemes and secure user interaction to offer a transparent, reliable, and traceable solution for carbon data management for CMP certification. Two example objectives of this 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 this disclosure provide a decentralized blockchain-based framework for carbon management for CMP certification, which may also be described interchangeably with the term "GPChain" (GP Chain). Figure 1 An exemplary framework of GPChain is shown. (Reference) Figure 1 At point 100, multiple users in the blockchain network (i.e., Manufacturer 1, Material Supplier 1, Material Supplier 2, and Certification Authority) are involved in blockchain-based carbon footprint recording and certification. Furthermore, at point 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 documents for CMP certification. It is understood that 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 point 102, inputs from the three types of users are identified separately based on the different information requirements for the 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 point 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. Considering the privacy requirements of certain carbon data, in embodiments of this disclosure, transactions containing private or sensitive carbon data can be privacy-protected (details will be described in later sections). Furthermore, embodiments of this disclosure can also provide smart contracts with fine-grained access control, allowing user interactions (calling smart contract functions) to be controlled or, in some cases, restricted, to enhance the security of GPChain. At point 106, all user-generated transactions are distributed and synchronized within GPChain for user communication and public oversight of CMP authentication.

[0063] Figure 2 This illustrates an exemplary modular architecture of GPChain. (Example:...) Figure 2As shown, GPChain may specifically include 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 within the blockchain network, namely, the carbon footprint data required for CMP certification, including the data attributes and process documents 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 only accessible to one or more authorized users. Based on this data model, a privacy-preserving carbon data sharing strategy is employed in the data privacy layer, which considers the different needs when sharing sensitive and private carbon data within the blockchain network. The data privacy layer may integrate asymmetric and homomorphic encryption schemes to preprocess carbon data at different privacy levels for secure storage and sharing before generating blockchain transactions based on the received carbon data. For example, allowing only authorized users to access raw sensitive carbon data using the corresponding decryption key for verification and authentication purposes can enhance carbon data security within the blockchain network and alleviate manufacturers' concerns about sensitive carbon data leakage during CMP certification. The processed carbon data is further processed at the smart contract layer using a distributed ledger within the blockchain network for immutable carbon data storage, sharing, and communication. Specifically, fine-grained access control smart contracts can be provided in GPChain to enhance smart contract security and authentication credibility by controlling or, in some cases, restricting user interactions within the blockchain network, allowing only authorized users to invoke and generate corresponding transactions. After blockchain transactions are generated via the smart contract layer based on the received carbon data for CMP certification, these transactions can be distributed across decentralized and transparent blockchain networks (e.g., the Ethereum blockchain network). It is noteworthy that because CMP certification is a public commitment to both industry applications and social oversight, the level of decentralization and transparency required to maintain data integrity and security is far higher than for private enterprise applications. In this context, Ethereum may be the preferred choice compared to other blockchain platforms such as Hyperledger Fabric. However, those skilled in the art will recognize other blockchain platforms suitable for this application. Finally, the data storage layer combines traditional databases and IPFS to store raw carbon data and proof documents provided for authentication, respectively. It is understood that the platform used for storing carbon data and / or carbon-related documents is not limited to IPFS but can encompass other suitable platforms or similar services. Furthermore, when a user attempts to provide raw carbon-related documents for authentication, these documents are first uploaded to the IPFS platform to generate a unique file value, which can then be processed using smart contracts and distributed across the blockchain network.Details of GPChain's key technical components will be described in the following sections.

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

[0065] Figure 3 This diagram illustrates an exemplary process flow for the CMP certification lifecycle using GPChain. Figure 3 The process, as illustrated, involves the main information flow and data exchange across different users in CMP carbon certification. The process diagram has four main phases: (1) Phase I: carbon emissions from raw material extraction are collected and reported by the material supplier; (2) Phase II: carbon emissions from upstream transportation during material delivery are collected and reported by the material supplier; (3) Phase III: carbon emissions from CMP manufacturing are recorded and calculated by the manufacturer; and (4) Phase IV: product certification process and certificate issuance by a professional certification body. In the first two phases (i.e., Phase I and Phase II), the material supplier sends documentation of the material's emission factors and transportation details during delivery to the manufacturer. Upon receiving the materials and related information, the manufacturer begins recording the carbon footprint generated during the CMP manufacturing process, which includes details of material consumption and energy consumption (such as annual energy consumption bills). Simultaneously, the manufacturer collects all relevant documentation from the material supplier and during the manufacturing phase to calculate the product's total carbon footprint (CFP). In Phase III, the manufacturer sends an application with the CFP results and all relevant documentation to the certification body for verification. At Phase IV, the certification body will issue a certificate to the manufacturer upon successful verification.

[0066] Furthermore, the blockchain data model can be configured based on carbon data or documents required for the authentication process. (Reference) Figure 4 For material suppliers, user input can be modeled as material source information. Specifically, it can include basic supply chain information (“material supplier” and “material type”), material emission factors (EF) from material extraction (“material EF” and “units of material EF”), and EF from upstream transportation (“transport EF” and “units of transportation EF”). This information can be obtained from one or more supporting documents provided by the user (in this case, the material supplier). Additionally, the original supporting documents containing information about material EF and transportation are also provided and stored in the blockchain network via a unique document hash value provided by IPFS. For manufacturers, there can be three types of user input corresponding to different activities (e.g., ...). Figure 4(As shown in the diagram). 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 quantitative information affecting CFP calculations (such as "material consumption" and "processing EF"). Another type of user input from manufacturers is sending an application request in GPChain that includes attributes of basic product information and all relevant record IDs ("a set of TX IDs for material and energy consumption") during product manufacturing. For certification bodies, their input is primarily 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 of these user inputs are provided in the data access layer and processed (e.g., encrypted) in the data privacy layer before corresponding blockchain transactions can be generated (in the smart contract layer).

[0067] Furthermore, while blockchain can offer 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 for certification, such as the manufacturer's partners, raw material suppliers, and material consumption during CMP manufacturing. Therefore, considering users' privacy concerns regarding sensitive carbon data during CMP certification, blockchain data models (such as...) Figure 4 (As shown in the diagram) this type of data is categorized into three privacy levels. Details regarding the exemplary three privacy levels are provided below.

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

[0069] - Privacy Level 1: Private information that should only be accessible to the intended recipient / user. For example, sensitive information that can only be shared (1) between material suppliers and manufacturers, and (2) between manufacturers and certification bodies. In particular, manufacturers may be unwilling to share information about their business partners (such as suppliers) with the public (especially information about which raw materials were supplied 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 the manufacturer and / or other authorized users. Similarly, when a manufacturer submits a certification request to a certification body, information (such as certification documents) should also be accessible only to 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" figures for a particular type of CMP may implicitly reveal a manufacturer's trade secrets (such as product design), which could have adverse effects if disclosed to other users, especially the manufacturer's competitors. From the manufacturer's perspective, raw material consumption data should only be accessible to them, and certification bodies should only have access for verification purposes (required in CMP certification) to the total CFP value based on the material consumption data.

[0071] Understandable Figure 4 The information shown is not exhaustive and may include other relevant information that can be assessed and determined by those skilled in the art.

[0072] Therefore, embodiments of this disclosure provide a blockchain data model for CMP authentication with multiple privacy levels at the data access layer of GPChain. This data model can be specifically designed for blockchain-based CMP authentication, and can (1) identify carbon data attributes stored and shared in a transparent blockchain network, and (2) consider these carbon data based on their attributes within the context of CMP authentication and classify them into appropriate privacy levels. However, we wish to emphasize that the number of privacy levels is not limited to those described above (i.e., privacy levels 0 to 2), but can include more privacy levels as needed by the user, such as privacy levels 0 to 3, 0 to 4, 0 to 5, etc.

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

[0074] As described above, data categorized as Privacy Level 1 and Privacy Level 2 should be accessed only 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 arise regarding the security of such carbon data. Therefore, embodiments of this disclosure provide 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, this strategy can be implemented on carbon data at Privacy Level 1 and Privacy Level 2. Specifically, for carbon data categorized under Privacy Level 1 (i.e., carbon data that can be shared with authorized users), an asymmetric encryption scheme can be used to encrypt the Privacy Level 1 carbon data, and the authorized user can possess a private key for decrypting the carbon data encrypted using the corresponding public key, allowing the authorized user to access the carbon data after decryption. Furthermore, it should be noted that each user's public key in a blockchain network is generally available to the public (i.e., all users of the blockchain network), allowing any user in the blockchain network (e.g., a user who wants to send a message to another user) to use that 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 the diagram, at point 500, Material Supplier A and Manufacturer 01 distribute their asymmetric public keys via a transaction in the blockchain network. At point 502, when sharing Privacy Level 1 carbon data (e.g., "Material Supplier," "Material Type," etc.) and the original hash value of the proof document, Material Supplier A uses Manufacturer 01's public key to encrypt the Privacy Level 1 carbon data, and at point 504, a transaction with the encrypted Privacy Level 1 data is generated and broadcast in the blockchain network. At points 506 and 508, Manufacturer 01 can decrypt the encrypted Privacy Level 1 carbon data by using the private key corresponding to Manufacturer 01's public key to access the original Privacy Level 1 carbon data shared by Material Supplier A, while other users without said private key will not be able to decrypt the encrypted Privacy Level 1 carbon data.

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

[0077] Figure 6 A flowchart illustrating an exemplary process flow for processing privacy level 2 carbon data in a transaction between manufacturer 01 and certification authority 01 is shown. Specifically, at 600, manufacturer 01 uses the homomorphic public key of certification authority 01 to encrypt privacy level 2 carbon data (e.g., the amount of raw materials consumed in manufacturing a certain product). Subsequently, at 602, one or more blockchain transactions are generated based on the encrypted privacy level 2 carbon data, along with other processed carbon data at different privacy levels. At 604, during the CMP certification process, certification authority 01 collects all generated blockchain transactions related to manufacturing for the CMP and retrieves the 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, i.e., this is performed while the privacy level 2 data is encrypted. Thus, an encrypted aggregate result based on this calculation can be obtained. Finally, at 608, certification authority 01 can obtain the total CFP result of the CMP by decrypting the encrypted aggregate result using its homomorphic private key. Therefore, secure data aggregation during the authentication process can be achieved by employing homomorphic encryption schemes. This allows carbon computation functions to be performed while avoiding any leakage of the manufacturer's trade secrets (i.e., privacy level 2 carbon data). Furthermore, it is worth emphasizing that... Figure 6 The process flow shown is for processing privacy level 0 and privacy level 1 data. Figure 5 The same or similar as shown.

[0078] In summary, embodiments of this disclosure provide a privacy-preserving carbon data sharing strategy within a data privacy layer. First, based on the privacy requirements of carbon data and a multi-privacy blockchain data model, different types of carbon data are classified into different "privacy levels," such as privacy level 0, privacy level 1, privacy level 2, etc. Furthermore, the privacy-preserving carbon data sharing strategy can implement different encryption schemes for carbon data at 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, allowing operations to be performed on the encrypted privacy level 2 data while it is being encrypted. Therefore, using this carbon data sharing strategy can alleviate manufacturers' concerns about carbon data leakage.

[0079] Fine-grained access control smart contracts based on blockchain CMP authentication

[0080] Additionally, embodiments of this disclosure provide smart contracts with fine-grained access control to control or, in some cases, restrict user interactions within a blockchain network, thereby enhancing the security of smart contracts and the credibility of CMP authentication in a decentralized and transparent blockchain network. Figure 7 A flowchart illustrating the execution workflow of three exemplary smart contracts used in GPChain is shown: (1) a cryptographic smart contract, a typical smart contract that enables all users in the blockchain network to distribute public keys for privacy protection; (2) a recording smart contract with fine-grained access control, which allows authorized users to call different smart contract functions for relevant types of transactions to generate carbon footprint records during CMP certification; and (3) an authentication smart contract with fine-grained access control, which only allows (one or more) specific professional certification authorities to call functions for transactions used to generate CMP certificates. Figure 8 The example algorithms for these smart contracts are shown below.

[0081] Refer back Figure 7 (a) to Figure 7 (c) Further details regarding smart contracts (1) to (3) are also provided below.

[0082] -Crypto smart contracts Figure 7 (a) This type of 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 to the key distribution function is one or more public keys used for asymmetric encryption and homomorphic encryption (only for authentication authorities), and based on the provided input data, it can generate a transaction as output, including the user's address and public key information. Furthermore, as... 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 for data encryption at the data privacy layer.

[0083] -Record smart contracts ( Figure 7 (b) Typically, the manufacturer owns such a smart contract. Fine-grained access control can be provided to the manufacturer within this smart contract to control or restrict user access to certain functions within the smart contract. This can help (1) avoid recording unrelated carbon transactions 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 which the manufacturer has a partnership) to record material origin information under the smart contract address. Furthermore, these two functions can only be successfully called by the owner of the smart contract (i.e., the manufacturer). 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 regarding material origin information (e.g., ...). Figure 4 (As shown in the diagram). 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, used to record energy consumption information during product processing, can only be called by the manufacturer.

[0084] - Authentication smart contracts ( Figure 7 (c) Typically, certification authorities possess such smart contracts. These smart contracts provide fine-grained access control for functions of the smart contract used to generate CMP certification results. Specifically, only the certification authority can call these functions, which prevents attempts to maliciously forge CMP certification information. Furthermore, as... 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 an unauthorized manufacturer to call the Certification() function will be rejected to ensure the authority of the certification authorities 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. Furthermore, manufacturers can better manage their CMP-related carbon record transactions by authorizing their partner material suppliers to call smart contract functions and generate the transactions required for CMP certification. Moreover, through these smart contracts, the credibility of CMP certification results in blockchain transactions can be improved because only authorized certification bodies can call the relevant smart contract functions.

[0086] refer to Figure 9 and Figure 10 This disclosure provides an embodiment of a method for managing carbon data using a blockchain network. Method 1000 can be used as follows: Figure 9 The system 900 shown in the figure is implemented. Figure 9 A schematic diagram of a system 900 using a blockchain network to manage carbon data is shown. System 900 includes a processing device 902. Users or user nodes 904 can, for example, access the system through a user interface of system 900. Figure 9 (Not shown in the image) interacts with system 900. Alternatively, user 904 can use a mobile device (not shown in the image) communicatively coupled to system 900 to interact with system 900. Figure 9 (Not shown) interacts with system 900. Nevertheless, those skilled in the art will recognize any other suitable way to implement the intended function. Furthermore, processing device 902 may be communicatively coupled to blockchain network 906 (e.g., to transmit blockchain transactions to blockchain network 906), and / or communicatively coupled to IPFS platform 908 for storing or receiving carbon-related data.

[0087] refer to Figure 10 Method 1000 may include the following steps.

[0088] Step 1002: The processing device categorizes each carbon data point from one or more carbon data points received from at least one user on the blockchain network into a corresponding privacy level. Each privacy level represents a privacy requirement corresponding to each carbon data point from the one or more carbon data points.

[0089] Furthermore, each of the one or more carbon data sets may include basic product information and / or manufacturing information for at least one building material or product. Additionally, the privacy requirements for each of the one or more carbon data sets may be predetermined based on the implementation of the basic product information and / or manufacturing information.

[0090] Step 1004: The processing device encrypts each carbon data in one or more categories of carbon data using one of a plurality of encryption schemes, wherein the implementation of one of the plurality of encryption schemes is determined based on the 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. Furthermore, carbon data at the first privacy level can be accessed by all users in the blockchain network, carbon data at the second privacy level can be accessed by users authorized by the carbon data owner, and carbon data at the third privacy level can only be accessed by the carbon data owner. In this embodiment, the multiple encryption schemes may include asymmetric encryption schemes and homomorphic encryption schemes. Moreover, the first-level and second-level privacy carbon data can be encrypted using an asymmetric encryption scheme, and the third-level privacy carbon data can be encrypted using a homomorphic encryption scheme.

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

[0093] One or more smart contracts can be used to generate one or more blockchain transactions. Furthermore, at least one of the one or more smart contracts can be configured to allow only 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.

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

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

[0096] Feasibility study

[0097] Figure 11 An exemplary workflow for testing an implementation of GPChain is shown, which includes, but is not limited to, three main parts: (1) blockchain function development, (2) Ethereum node generation, and (3) Ethereum blockchain deployment. Figure 12An example of the GPChain user interface (UI) is shown, which includes, but is not limited to, three main functional modules: (1) cryptographic key management, (2) carbon footprint recording, and (3) application and certification. Users can invoke these modules to achieve different functionalities based on their roles and responsibilities. Furthermore, in this example, Etherscan for the Goerli Testnet is integrated with the UI in the blockchain module, enabling users to effectively track their carbon footprint during CMP certification. To demonstrate the feasibility of GPChain, a real-world CMP carbon certification scenario using a test product (concrete, 45 / 20D, 125mm slump) is selected to illustrate blockchain-based carbon management for CMP certification. A scenario-based approach is adopted, using real data from the test product collected from materials and documents prepared for the Hong Kong CIC Green Product Certification Scheme to demonstrate the carbon certification process. For this demonstration, an Ethereum blockchain network comprising eight participants (i.e., concrete manufacturers, certification bodies, and six material suppliers) is established. This blockchain network is then deployed in two example carbon certification scenarios (given below) to demonstrate the feasibility of GPChain.

[0098] Example Scenario 1: Recording the carbon footprint for CMP

[0099] This example scenario primarily describes the process of recording the CMP's carbon footprint before performing further verification and certification. It is designed to verify the workflow within GPChain used for CMP certification, as well as the functionality of the developed carbon data privacy protection strategy and secure smart contract interaction scheme. Figures 13A to 13C The process flow of Example Scenario 1, comprising three key activities, is shown. In Activities 1 and 2, all users in the blockchain network distribute their public keys (for asymmetric and homomorphic encryption) within a UI developed for sharing sensitive carbon data. The manufacturer then authorizes its partner material suppliers via blockchain transactions, enabling these suppliers to successfully invoke recording functions based on recording smart contracts 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) encrypting its company name and material type using the manufacturer's asymmetric public key; (2) directly inputting the EF and transport EF information of the material; and (3) encrypting the hash value (generated by IPFS) of the proof document corresponding to the material's EF information using the manufacturer's asymmetric public key. It should be noted that... Figures 13A to 13C This section primarily provides examples of how material suppliers can record their carbon footprint for CMP certification. The process for recording a manufacturer's carbon footprint in GPChain is similar, but can include an additional step of performing homomorphic encryption on material consumption (i.e., privacy level 2 carbon data) using the certification authority's homomorphic public key.

[0100] The verification result of Example Scenario 1 is in Figure 14 As shown in the figure. Specifically, the results show that: (1) multiple users in Example Scenario 1 have successfully participated in GPChain via Ethereum transactions and recorded their corresponding carbon footprints under the same recording smart contract; (2) the material supplier, after obtaining authorization, successfully recorded its carbon footprint in GPChain based on a secure smart contract interaction scheme; (3) the carbon footprint data in GPChain transactions is well protected by encryption, and the original carbon data can only be accessed by authorized users with the corresponding private key. Therefore, the objective of this example scenario has been successfully verified.

[0101] Example Scenario 2: CMP Application and Certification

[0102] Figure 15A and Figure 15B The illustration shows another example scenario used to verify the application and certification process for tested products. For example... Figure 15A and Figure 15B As shown in Activity 1, the manufacturer first reports basic information about the test product, such as product type, batch number, and total CFP. The manufacturer then collects all corresponding transactions, prepares other application documents via IPFS and encryption strategies, and sends the application request in the UI. Activity 2 illustrates the detailed verification process implemented by the certification authority upon receiving the application request. The certification authority can access the application materials from IPFS after obtaining the decrypted hash value. 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 that the original information is consistent with the raw material transaction recorded by the material supplier, and (2) retrieves the total material EF information and the homomorphically encrypted quantity information. The certification authority then performs a secure aggregation to obtain the actual CFP value and compares this 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 body attaches the issuance and certificate information to generate a formal transaction for the certified test product, which is broadcast on the blockchain network. The verification result of Example Scenario 2 is... Figure 16 As shown in the diagram. Specifically, the verification results show that the transactions for the application and authentication processes were generated in the Ethereum blockchain network and were successfully distributed within the network, making them searchable in Etherscan. Therefore, the objective of Example Scenario 2 was successfully verified.

[0103] Advantages, such as Figure 2The well-structured blockchain data format shown (i.e., breaking down the blockchain model into "data access layer", "data privacy layer", etc.) combines the structure described above and as follows: Figures 5 to 7 The privacy schemes illustrated (i.e., classifying carbon data into different privacy levels, privacy-preserving carbon data sharing strategies, etc.) can enhance GPChain's performance by reducing blockchain transaction latency and increasing throughput. Therefore, the cost of completing transactions using GPChain can be reduced. For example, as... Figure 14 and Figure 16 As shown, due to the transparent carbon certification feature 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 transfers (i.e., the use of tokenization is eliminated), which improves GPChain's performance. The following sections provide a performance and cost evaluation of GPChain.

[0104] Performance evaluation

[0105] To evaluate the performance of GPChain, two performance metrics (i.e., arrival latency and throughput) were selected and tested for GPChain smart contracts. Arrival latency is generally defined as the time interval between when a transaction is first sent to the blockchain and when the transaction is confirmed by a user of the blockchain network (as shown in Equation (1)). In this case, it is a measure of the data delivery efficiency of transactions generated using GPChain smart contracts. Therefore, arrival latency can be used to assess the latency of transactions generated in GPChain via different smart contracts. 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 smart contracts and can be used to show the user bandwidth or range of GPChain that can access different smart contracts within a given time frame. Furthermore, TPS can be calculated using Equation (2) below, which is based on dividing the number of transactions per block by the block generation cycle. In this performance evaluation, the arrival latency and TPS of three smart contracts (i.e., the cryptographic smart contract, the recording smart contract, and the authentication smart contract) were measured separately by calling all functions of the respective smart contracts. The final values ​​for arrival latency and TPS for each smart contract are obtained by averaging the arrival latency and TPS calculated or measured for different calls to the smart contract function. Considering the limited Testnet ETH available from Goerli, each activity that calls the smart contract function is measured ten times. Figure 17 and Figure 18 The performance evaluation results for the arrival delay and throughput of the three smart contracts are shown respectively.

[0106] Arrival delay = t Tx确认 -t Tx发送 (1)

[0107]

[0108] like Figure 17 As shown, the average arrival latency for encrypted smart contracts, recording smart contracts, and authentication smart contracts is around 10 seconds. This means that it takes users approximately 10 seconds to generate a transaction containing carbon footprint information or carbon data and for it to be confirmed in the distributed ledger of the blockchain network. Since the Ethereum-based public blockchain network is used for this evaluation, there is a time interval before most blockchain network users accept the transaction. In this respect, the average time for Ethereum-based transactions to complete is approximately 15 seconds. It can be seen that the arrival latency of the three smart contracts (approximately 10 seconds) is faster than the average known arrival latency of the Ethereum network (15 seconds).

[0109] Figure 18 This displays the throughput results for encrypted smart contracts, recording smart contracts, and authentication smart contracts. For example... Figure 18 As shown, for encrypted smart contracts, recording smart contracts, and authentication smart contracts, approximately 26.76, 25.72, and 25.53 transactions per second can be processed, respectively. Considering that the average known throughput of the Ethereum platform is approximately 5.55 transactions per second (e.g., ...), ... Figure 18 As shown in the figure, the three smart contracts exhibit significantly higher throughput (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, users incur transaction fees when executing transactions on public blockchain platforms (such as Ethereum). Besides the technical development and maintenance costs of the blockchain-based platform, measuring the execution cost of blockchain transactions on public blockchain platforms is also important, as each blockchain transaction incurs a fee for storing data. In Ethereum, this transaction fee is represented by "Gas," which refers to the cost required to successfully process a blockchain transaction. As the basic unit of network cost, Gas is typically paid in Ether (the cryptocurrency used in Ethereum) and is commonly used to evaluate the economic performance and resource consumption of smart contracts. Therefore, in this evaluation, the Gas price (in Ether) is used to estimate the cost of each of the three smart contracts to assess their execution costs. The final execution cost of the three smart contracts is calculated by taking the average cost of the called functions (i.e., each function call is implemented ten times, and then the average is taken).

[0112] Figure 19 This shows the average execution cost of each of the three smart contracts (i.e., the crypto smart contract, the recording smart contract, and the authentication smart contract). To provide a real-world market-relevant assessment, the Gas price generated per transaction (in Ether) has been converted to Hong Kong Dollars (HKD) based on the real-time Ethereum price monitored by Google Finance on December 29, 2022. Reference Figure 19 The average cost per smart contract ranges from approximately HKD 0.001 to HKD 0.105. The evaluation results show that transactions based on the cryptographic smart contract incurred low transaction fees because these transactions contain the least amount of information compared to the other two smart contracts. On the other hand, transactions based on the authentication smart contract had the highest cost per transaction, approximately HKD 0.105 (because such transactions typically contain the most information), and transactions based on the recording smart contract cost approximately HKD 0.035 per transaction. Therefore, assuming five users (i.e., three material suppliers, one manufacturer, and one certification body) participate in the certification process, the total cost of using GPChain to complete the entire product certification cycle (distributing encryption keys, authorizing material suppliers, recording carbon footprint information, sending application requests, and broadcasting product certificates) would be approximately HKD 0.46. Compared to similar distributed services offered by Google Cloud Platform for five participating users at a fixed component cost of $244.59 per month (approximately HKD 1,960), GPChain clearly demonstrates better economic performance and can perform more than 4,000 certification cycles at the same maintenance cost of $244.59 per month.

[0113] Figure 20 An exemplary computing device 2000 is depicted, which is hereinafter interchangeably referred to 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 Figure 20 The example computing device 2000 shown includes a processor 2002 for executing software routines. Although a single processor is shown for clarity, the computing device 2000 may also include a multiprocessor system. The processor 2002 is connected to a communication infrastructure 2004 to communicate 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 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 magnetic tape drive, an optical disk drive, etc. The removable storage drive 2012 reads from and / or writes to the removable storage unit 2014 in a well-known manner. The removable storage unit 2014 may include floppy disks, magnetic tapes, optical disks, etc., read from and written to by the removable storage drive 2012. Those skilled in the art will understand that the removable storage unit 2014 includes a computer-readable storage medium in which computer-executable program code instructions and / or data are stored.

[0116] In an alternative implementation, the auxiliary storage 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, removable storage units 2016 and interfaces 2018. Examples of removable storage units 2016 and interfaces 2018 include program cassette tapes and cassette tape interfaces (such as those 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 transferred between the computing device 2000 and external devices via a communication path 2022. In various embodiments of the invention, the communication interface 2020 permits data to be transferred between the computing device 2000 and a data communication network (such as a public or private data communication network). The communication interface 2020 can be used to exchange data between different computing devices 2000 that 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 circuitry, etc. The communication interface 2020 can be wired or wireless. The software and data transmitted via the communication interface 2020 are in the form of signals, which can be electronic, electromagnetic, optical, or other signals that can be received by the communication interface 2020. These signals are provided to the communication interface via the communication path 2022.

[0118] like Figure 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 a corresponding display 2026, and the audio interface 2028 performs operations for playing audio content via a corresponding speaker 2030.

[0119] As used herein, the term "computer program product" may refer in part to removable storage unit 2014, removable storage unit 2016, hard disk installed in hard disk drive 2010, or a carrier wave carrying software to communication interface 2020 via communication path 2022 (wireless link or cable). A computer-readable storage medium is any non-transitory tangible storage medium that provides recorded instructions and / or data to computing device 2000 for execution and / or processing. Examples of such storage media include floppy disks, magnetic tapes, CD-ROMs, DVDs, and Blu-ray discs. TM Disks, hard disk drives, ROMs or integrated circuits, USB storage devices, magneto-optical disks, or computer-readable cards such as PCMCIA cards, etc., whether such devices are internal or external to the computing device 2000. Examples of temporary or intangible computer-readable transmission media that may also participate in providing software, applications, instructions, and / or data to the computing device 2000 include radio or infrared transmission channels and network connections to another computer or networked device, as well as the Internet or intranet containing email transmissions and information recorded on websites, etc.

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

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

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

[0123] Understandable Figure 20 The components shown are used to provide means for performing the various functions and operations of the server described in the embodiments described above.

[0124] In implementations, a server can generally be described as a physical device comprising 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, together with the at least one processor, to enable the physical device to perform necessary operations.

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

[0126] Step 1002: The processing device categorizes each carbon data point from one or more carbon data points received from at least one user on the blockchain network into a corresponding privacy level. Each privacy level represents a privacy requirement corresponding to each carbon data point from the one or more carbon data points.

[0127] Furthermore, each of the one or more carbon data sets may contain basic product information and / or manufacturing information for at least one building material or product. Additionally, the privacy requirements for each of the one or more carbon data sets may be predetermined based on the basic product information and / or manufacturing information.

[0128] Step 1004: For each carbon data in one or more categories of carbon data, the processing device encrypts it using one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on the 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. Furthermore, carbon data at the first privacy level can be accessed by all users in the blockchain network, carbon data at the second privacy level can be accessed by users authorized by the carbon data owner, and carbon data at the third privacy level can only be accessed by the carbon data owner. In this embodiment, the multiple encryption schemes may include asymmetric encryption schemes and homomorphic encryption schemes. Moreover, the first-level and second-level privacy carbon data can be encrypted using an asymmetric encryption scheme, and the third-level privacy carbon data can be encrypted using a homomorphic encryption scheme.

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

[0131] One or more smart contracts can be used to generate one or more blockchain transactions. Furthermore, at least one of the one or more smart contracts can be configured to allow only 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.

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

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

[0134] in conclusion

[0135] In summary, embodiments of this disclosure provide a blockchain-based framework for secure carbon management during CMP certification. Compared to existing processes for proposing theoretical frameworks for building carbon management, the blockchain-based framework and workflow proposed in this application are designed based on real-world research, i.e., not at the conceptual level. First, embodiments of this disclosure provide a blockchain data model with multiple privacy levels for CMP certification. This blockchain data model can identify carbon data provided by one or more users in a blockchain network and their sensitivity / privacy requirements, where data can be categorized into different privacy levels based on the privacy requirements of each carbon data point. Additionally, the blockchain-based framework can provide structured carbon data and information flow for CMP certification in a blockchain environment. Furthermore, embodiments of this disclosure provide a fine-grained privacy-preserving carbon data sharing strategy for sensitive carbon data required in CMP certification. This privacy-preserving carbon data sharing strategy enables fine-grained security protection for sensitive carbon data by integrating asymmetric and homomorphic encryption schemes with the blockchain, which specifically meets the security carbon data sharing requirements of different privacy levels. Furthermore, embodiments of this 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 could point to (1) developing a comprehensive blockchain-based CMP carbon certification application that includes user registration and access control for reading and writing data, which would help improve user experience for further adoption across the industry; and (2) integrating with international standards for carbon footprint quantification and reporting to provide users with a standardized carbon management process.

[0137] Those skilled in the art will understand that various changes and / or modifications can be made to the invention shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. Therefore, the present embodiments are to be considered illustrative rather than restrictive in all respects.

Claims

1. A method for managing carbon data for CMP (Construction Management Professional) certification of building materials and products in the construction and building industry using a blockchain network, the method comprising: The processing device categorizes each of 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, wherein at least one user of the blockchain network includes at least one of manufacturers, material suppliers, and certification bodies, and the one or more carbon data are obtained from the original carbon database or from the output of the InterPlanetary File System (IPFS) for storing and sharing relevant carbon data and files for CMP certification; The processing device encrypts each carbon data in one or more categories of carbon data using one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on the privacy level of the carbon data; The processing device generates one or more blockchain transactions corresponding to each carbon data in one or more encrypted carbon data sets; and The processing device transmits the one or more blockchain transactions to the blockchain network; 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 the privacy requirements of each of the one or more carbon data are predetermined based on the basic product information and / or the manufacturing information; Each of the one or more carbon data includes carbon footprint data required for CMP certification, the carbon footprint data includes data attributes and process documents required for CMP certification, and the carbon footprint data is verified and certified by a professional certification body during CMP certification.

2. The method according to claim 1, wherein the method further comprises: The processing device maps each of the one or more carbon data sets to a corresponding privacy level based on the predetermined privacy requirements.

3. The method according to claim 1, wherein the corresponding privacy level includes 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.

4. The method according to claim 3, wherein the plurality of encryption schemes includes an asymmetric encryption scheme and a homomorphic encryption scheme, wherein the first privacy level carbon data and the second privacy level carbon data are encrypted using the asymmetric encryption scheme, and wherein the third privacy level carbon data is encrypted using the homomorphic encryption scheme.

5. 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 allow only 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.

6. The method of claim 5, wherein the method is implemented based on a blockchain-based model, and wherein the blockchain-based model comprises an architecture having the following characteristics: A data access layer, wherein the classification steps are performed in the data access layer; A data privacy layer, wherein the encryption steps are performed within the data privacy layer; and The smart contract layer, wherein the generation steps are performed in the smart contract layer.

7. A system for managing carbon data for CMP (Construction Management Processing) of building materials and products in the construction and building industry using a blockchain network, wherein the system includes a processing device configured to: classifying each of one or more carbon data received from at least one user of the blockchain network into a respective privacy level, wherein each of the respective privacy levels represents a privacy requirement corresponding to each of the one or more carbon data, wherein, At least one user of the blockchain network includes at least one of manufacturers, material suppliers, and certification bodies, and the one or more carbon data are obtained from the original carbon database or from the output of the InterPlanetary File System (IPFS) for storing and sharing relevant carbon data and files for CMP certification; For each carbon data in one or more categories of carbon data, an encryption scheme is used to encrypt it using one of a plurality of encryption schemes, wherein the one of the plurality of encryption schemes is determined based on the privacy level of the carbon data; Generate one or more blockchain transactions corresponding to each carbon data in one or more encrypted carbon data sets; as well as Transmit the one or more blockchain transactions to the blockchain network; 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 the privacy requirements of each of the one or more carbon data are predetermined based on the basic product information and / or the manufacturing information; Each of the one or more carbon data includes carbon footprint data required for CMP certification, the carbon footprint data includes data attributes and process documents required for CMP certification, and the carbon footprint data is verified and certified by a professional certification body during CMP certification.

8. The system of claim 7, wherein the processing device is further configured to: Each carbon data point in the one or more carbon data sets is mapped to a corresponding privacy level based on the predetermined privacy requirements.

9. The system of claim 7, 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.

10. The system of claim 9, wherein the plurality of encryption schemes includes an asymmetric encryption scheme and a homomorphic encryption scheme, wherein the first privacy level carbon data and the second privacy level carbon data are encrypted using the asymmetric encryption scheme, and wherein the third privacy level carbon data is encrypted using the homomorphic encryption scheme.

11. The system of claim 7, 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 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.

12. The system of claim 11, 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 characteristics: A data access layer, wherein the classification of each carbon data point from one or more carbon data points 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 on each carbon data in one or more categories of carbon data using one of a plurality of encryption schemes within the data privacy layer; and The smart contract layer, wherein the generation of 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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