Patent Application
20250184115
The present invention relates broadly, but not exclusively, to methods of managing carbon data using a blockchain network and systems for the same.
As one of the biggest resource consumers and carbon emitters, the construction industry plays a crucial role in global carbon reduction. Carbon certification or labelling schemes are efficient ways to assess and report the carbon footprints of construction materials and products (CMPs), providing the 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 non-transparency and manipulation problems, making carbon footprints unreliable and hard to track.
Therefore, a need exists to provide methods of managing carbon data using a blockchain network and systems for the same.
According to a first aspect of the present invention, there is provided a method of managing carbon data using a blockchain network. The method includes categorizing, by a processing device, each of one or more carbon data received from at least one user of the blockchain network into respective privacy levels. 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 categorized 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.
Each of the one or more carbon data may include basic product information and/or manufacturing information of at least one construction material or product. Further, the privacy requirement of each of the one or more carbon data may be predetermined based on the basic product information and/or the manufacturing information.
In embodiments 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 the predetermined privacy requirement.
In one embodiment, the respective privacy levels may include first, second and third privacy levels. The first privacy level carbon data may be accessible to all users in the blockchain network, the second privacy level carbon data may be accessible to users authorized by the owner of the carbon data, and the third privacy level carbon data may be accessible only by the owner of the carbon data.
In this embodiment, the plurality of encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. The first and second level privacy carbon data may be encrypted using the asymmetric encryption scheme, and the third level privacy level carbon data may be encrypted using the homomorphic encryption scheme.
In embodiments of the present invention, the one or more blockchain transactions may be generated using one or more smart contracts. 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 the at least one of the one or more smart contracts to invoke at least one function of the at least one of the one or more smart contracts.
Additionally, the method may be implemented based on 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. The categorizing step may be performed in the data access layer, the encrypting step may be performed in the data privacy layer, and the generating step may be performed in the smart contract layer.
According to a second aspect of the present invention, there is provided a system for managing carbon data using a blockchain network. The system includes a processing device configured to categorize each of one or more carbon data received from at least one user of the blockchain network into respective privacy levels. Each of the respective 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 categorized 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 further 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 into the blockchain network.
In embodiments of the present invention, the processing device may be further configured to map each of the one or more carbon data to a corresponding privacy level based on the predetermined privacy requirement.
In one embodiment, the respective privacy levels may include first, second and third privacy levels. The first privacy level carbon data may be accessible to all users in the blockchain network, the second privacy level carbon data may be accessible to users authorized by the owner of the carbon data, and the third privacy level carbon data may be accessible only by the owner of the carbon data.
In this embodiment, the plurality of encryption schemes may include an asymmetric encryption scheme and a homomorphic encryption scheme. The first and second level privacy carbon data may be encrypted using the asymmetric encryption scheme, and the third level privacy level carbon data may be encrypted using the homomorphic encryption scheme.
In embodiments of the present invention, the one or more blockchain transactions may be generated using one or more smart contracts. 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 the at least one of the one or more smart contracts to invoke at least one function of the at least one of the one or more smart contracts.
In embodiments of the present invention, the processing device may be further 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. The categorizing of each of one or more carbon data received from at least one user of the blockchain network into respective privacy levels may be performed in the data access layer. The encrypting of each of the one or more categorized carbon data using one of a plurality of encryption schemes may be performed in the data privacy layer. The generating of one or more blockchain transactions corresponding to each of the one or more encrypted carbon data may be performed in the smart contract layer.
Embodiments of the invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the drawings, in which:
Embodiments of the present invention will be described, by way of example only, with reference to the drawings. Like reference numerals and characters in the drawings refer to like elements or equivalents.
Some portions of the description which follows are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The 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.
Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as “scanning”, “calculating”, “determining”, “replacing”, “generating”, “initializing”, “outputting”, or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms 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.
The present specification also discloses apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a computer or other device 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 programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a conventional computer will appear from the description below.
In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the invention.
Furthermore, one or more of the 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 storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the GSM, GPRS, 3G or 4G mobile telephone systems, as well as other wireless systems such as Bluetooth, ZigBee, Wi-Fi. The computer program when loaded and executed on such a computer effectively results in an apparatus that implements the steps of the preferred method.
The present invention may also be implemented as hardware modules. More particularly, in the hardware sense, a module is a functional hardware unit designed for use with other components or modules. For example, a module may be implemented using discrete electronic components, or it can form a portion of an entire electronic circuit such as an Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA). Numerous other possibilities exist. Those skilled in the art will appreciate that the system can also be implemented as a combination of hardware and software modules.
In the following description, the term “module” can refer to software, a hardware element, or a combination of both.
An Application Programming Interface (API) enables software and applications to communicate with each other. It is a software-to-software interface that allows for separate parties to communicate with each other without any previous user knowledge or intervention. In general terms, it is a set of clearly defined methods of communication between various software components.
This specification uses the term “configured to” in connection with systems, apparatus, and computer program components. For a system of one or more computers to be configured to perform particular operations or actions means that the system has installed on its software, firmware, hardware, or a combination of them that in operation cause the system to perform the operations or actions. For one or more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions. For special-purpose logic circuitry to be configured to perform particular operations or actions means that the circuitry has electronic logic that performs the operations or actions.
As used herein, the term “processing device” refers to any hardware or system configured to perform computational tasks. In the context of blockchain, the processing device can execute processes related to the validation, verification, and recording of transactions on a blockchain network. This can include tasks such as executing consensus algorithms (e.g., Proof of Work, Proof of Stake), generating and verifying cryptographic hashes, and validating digital signatures for secure transactions. The processing device may further handle the execution of smart contracts, data encryption, and the propagation of blockchain data across a distributed network. Additionally, the processing device can store portions of the blockchain ledger, maintaining transaction history, and ensuring the integrity and immutability of the blockchain data. The processing device may be configured to communicate with other nodes in the network, synchronize blockchain states, and perform other blockchain-based services.
The building and construction industry accounted for over 34% of energy demand and around 37% of energy and process-related CO2 emissions in 2021. Therefore, the building and construction industry is facing increasing pressure to reduce its life cycle carbon emissions. While various carbon reduction methods are used for reducing energy consumption during the building operation stage, the importance of reducing embodied carbon from the construction stage cannot be undermined, of which embodied carbon from the construction stage accounts for nearly 30% of life cycle greenhouse gas (GHG) emissions for buildings. Of various emission sources at the construction stage, extraction and manufacturing of construction materials contribute around 70% of the GHG emissions. In particular, cement production accounts for about 5-8% of manmade CO2 emissions, and the steel and iron production generates around 7% of CO2 emissions globally. Moreover, the transportation of raw materials to product manufacturing plants is energy-intensive, especially for the international transportation of raw materials over long distances. Therefore, developing strategies to facilitate carbon management at the construction material and product (CMP) level is essential to meet emission reduction targets in the building and construction industry.
Further, due to the importance of reducing carbon emissions from CMPs, numerous efforts have been directed towards carbon management over their life cycles. For example, a series of international standards have been published to identify the standardized process and steps of managing carbon footprints from the product level. Based on the principles and guidelines provided by these standards, various carbon certification or labelling schemes for CMPs have been developed in different regions and they serve as practical and meaningful yardsticks to help construction companies effectively measure and manage the carbon footprints of CMPs. These schemes are public commitments in that the carbon footprints of CMPs have been measured and certified by professional organizations or governmental departments at environmentally friendly levels, which serve as efficient carbon management tools for the credible assessment and public reporting of CMP carbon footprints. However, the existing carbon certification or labelling schemes for CMPs have been challenged for not providing enough 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. Moreover, some companies take advantage of the data transparency loophole for “greenwashing” such that misleading environmental claims are made through falsifying carbon data or worshipping false labels to achieve more carbon credits or green financing, resulting in negative effects on customer satisfaction and ultimately reducing company competitiveness. Others may even attack centralized environmental platforms' servers to inject false data to realize sustainability propaganda. Therefore, due to the carbon data transparency and reliability problems caused by the existing carbon management methods, public concerns about carbon data fraud have been raised.
As an emerging and promising technology, blockchain can offer a powerful solution to mitigate data transparency and reliability problems. Unlike centralized systems, blockchain typically operates under a distributed peer-to-peer network without intermediaries, which minimizes reliance on centralized organizations. Thus, data transparency and reliability can be obtained using blockchain, and at the same time, data traceability can be improved by blockchain via easy-to-identify and immutable information records. Due to these advantages that the blockchain can offer, it can serve as a transparent, reliable, and traceable data management tool for managing the carbon footprints of CMPs during the certification process. Without an intermediary or central authority, the carbon footprints of CMPs can be maintained collectively by corresponding stakeholders/users (e.g., raw material suppliers, manufacturers, and certification organizations) in a transparent environment, which facilitates the reliability of carbon footprint data in CMP certification. In addition, blockchain can also provide users with efficient tracking functions, helping clients or other users to quickly identify the certified CMPs' carbon footprints.
Although blockchain can provide great potential in the application of carbon management during CMP certification, challenges faced through applying blockchain in this field has yet to be addressed. One main challenge is the leakage of sensitive carbon footprint data during blockchain-based CMP certification. For a type of CMP from a manufacturer, certain carbon data, such as partner material suppliers, consumption quantity of materials and consumption of energy, during manufacturing is private and sensitive. Yet, in order to assess the carbon footprints of the CMP during the certification process, detailed knowledge of material suppliers and consumption quantities are required. Inappropriate data storage and sharing mechanisms of private and sensitive carbon data may cause leakage of commercial secrets, such as the CMP manufacturing formula of raw materials and procurement strategies, which could benefit manufacturers' competitors and damage the interests of CMP manufacturers. Hence, disclosing such proprietary information tends to raise stakeholders' concerns. For instance, competitors might use the manufacturer's GHG emission to assess its operational growth and efficiency, while non-governmental organizations (NGOs) may use the same information to exert pressure on the manufacturer to improve environmental performance. Since data stored in the blockchain is typically transparent to all users in the blockchain network, it can be difficult to engage manufacturers to record the carbon data required for CMP certification on the blockchain network. Another challenge can arise from uncontrolled user interactions in the blockchain network to generate carbon transactions. Particularly, interactions with the blockchain can typically be invoked by any user in the blockchain network via smart contracts, i.e., any users can interact with the blockchain to generate any type of transaction including transactions of certification results. However, certification transactions are only valid when they are generated by the responsible certification organizations. Otherwise, the credibility of certification results could be negatively affected and the frequency of having invalid certification transactions can also be increased. Therefore, it is important to address these challenges when applying blockchain to CMP certification. The present disclosure addresses, among other things, the following questions:
Thus, embodiments of the present disclosure provide a decentralized blockchain-based framework that is integrated with carbon data protection schemes and secure user interactions to provide a solution for transparent, reliable, and traceable carbon data management towards CMP certification. Two example objectives of the present disclosure are as follows:
As described above, embodiments of the present disclosure provide a decentralized blockchain-based framework, also described interchangeably with the term “GPChain”, for carbon management towards CMP certification.
Further, the blockchain data model can be configured based on the carbon data or documents required for the certification process. With reference to
Additionally, while blockchain can offer considerable advantages in carbon management during CMP certification, the deployment of a blockchain-based CMP certification framework may raise concerns regarding the protection of sensitive carbon data, such as partner raw material suppliers of manufacturers and material consumption during CMP manufacturing, required in certification. Therefore, the blockchain data model, as shown in
It should be understood that the information illustrated in
Therefore, embodiments of the present disclosure provide a blockchain data model for CMP certification with multi-privacy levels at the data access layer of the GPChain. This data model can be specific for blockchain-based CMP certification, which can (1) identify carbon data attributes that are stored and shared in the transparent blockchain network, and (2) consider and categorize these carbon data into the appropriate privacy levels based on the carbon data attributes under the context of CMP certification. However, we wish to highlight that the number of privacy levels is not limited to that described above, i.e., privacy levels 0 to 2, but can include more privacy level(s), e.g., privacy levels 0 to 3, 0 to 4, 0 to 5, etc., depending on the user's needs.
As described above, data labelled Privacy Level 1 and Privacy Level 2 should only be accessed by authorized user(s) to facilitate carbon data security during CMP certification. However, due to the distributed nature of blockchain that allows all users in the blockchain network to access the blockchain data, concerns regarding data security of such carbon data are raised. Therefore, embodiments of the present disclosure provide a privacy-preserving carbon data-sharing strategy, which integrates asymmetric and homomorphic encryption to facilitate data security and confidentiality during CMP certification. In an exemplary embodiment, the strategy can be implemented on Privacy Levels 1 and 2 carbon data. Specifically, for carbon data categorized under Privacy Level 1, i.e., carbon data that can be shared to the authorized user(s), the asymmetric encryption scheme can be used to encrypt the Privacy Level 1 carbon data and the authorized user(s) can own a private key for decrypting the carbon data that is encrypted using a corresponding public key so that the authorized user(s) can access the carbon data after decryption. Further, it should be noted that the public key of each user in the blockchain network is typically available to the public, i.e., all user(s) of the blockchain network, so that any user in the blockchain network who, for example, wants to send a message to another user can use the another user's public key to encrypt the message.
For carbon footprint data categorized under Privacy Level 2, e.g., information that should only be accessed by the owner such as the consumption quantity of raw materials during the CMP manufacturing, the challenge is to perform calculation of the CMP's CFP without revealing each consumption quantity of raw materials in the blockchain network. However, asymmetric encryption typically does not enable algebraic operations to be performed while hiding the content of the data. Thus, embodiments of the present disclosure provide a solution to such problem by integrating homomorphic encryption into the privacy-preserving carbon data-sharing strategy. Particularly, homomorphic encryption is an encryption scheme that t can allow specific types of computation/operation to be performed on encrypted ciphertexts and generate an encrypted result. In other words, this encryption scheme can allow users to perform operations such as algebraic aggregation on a set of encrypted data without decrypting any data first, which allows the privacy of sensitive data to be preserved when performing specific operations.
In summary, embodiments of the present disclosure provide a privacy-preserving carbon data-sharing strategy in the data privacy layer as follows. First, different types of carbon data is categorized into different “Privacy Level”, e.g., Privacy Level 0, Privacy Level 1, Privacy Level 2, etc., based on a multi-privacy blockchain data model, depending on the privacy requirement of the carbon data. Further, the privacy-preserving carbon data-sharing strategy can implement different encryption schemes, e.g., asymmetric and homomorphic encryption schemes, etc., on carbon data with different Privacy Levels. For example, the homomorphic encryption scheme can be used to encrypt Privacy Level 2 data such that operations can be performed on the encrypted Privacy Level 2 data while the data is encrypted. Therefore, the manufacturers' concerns on carbon data leakage may be mitigated using the carbon data-sharing strategy.
Additionally, embodiments of the present disclosure provide smart contracts with granular access control to control, or in some cases restrict, user interactions in the blockchain network, which enhances smart contract security and CMP certification credibility in a decentralized and transparent blockchain network.
Referring back to
In short, compared to traditional smart contracts, the above described smart contracts (1) to (3) can include granular access control, thereby providing the function of controlling or restricting certain smart contract functions to be invoked by authorized users only, which in turn controls user interactions within the blockchain network. Consequently, malicious activities, such as falsifying CMP certification results and extending CMP certification validity during blockchain-based CMP certification, can be avoided at the very beginning of the CMP certification life cycle. In addition, manufacturers may also better manage carbon recording transactions relevant to their CMPs by authorizing their partner material suppliers to invoke smart contract functions and generate transactions required in CMP certification. Moreover, the credibility of CMP certification results in blockchain transactions can be improved through these smart contracts since only the authorized certification organization(s) can invoke the relevant smart contract function(s).
With reference to
With reference to
Step 1002: categorizing, by a processing device, each of one or more carbon data received from at least one user of the blockchain network into respective privacy levels. Each of the respective privacy levels represents a privacy requirement corresponding to each of the one or more carbon data.
Further, each of the one or more carbon data can include basic product information and/or manufacturing information of at least one construction material or product. In addition, the privacy requirement of each of the one or more carbon data can be predetermined based on the basic product information and/or the manufacturing information.
Step 1004: encrypting, by the processing device, each of the one or more categorized 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.
In one embodiment, the respective privacy levels can include first, second and third privacy levels. Further, the first privacy level carbon data can be accessible to all users in the blockchain network, the second privacy level carbon data can be accessible to users authorized by the owner of the carbon data, and the third privacy level carbon data can be accessible only by the owner of the carbon data. In this embodiment, the plurality of encryption schemes can include an asymmetric encryption scheme and a homomorphic encryption scheme. Furthermore, the first and second level privacy carbon data can be encrypted using the asymmetric encryption scheme, and the third level privacy level carbon data can be encrypted using the homomorphic encryption scheme.
Step 1006: generating, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data.
The one or more blockchain transactions can be generated using one or more smart contracts. Further, at least one of the one or more smart contracts can be configured to only allow at least one user authorized by an owner of the at least one of the one or more smart contracts to invoke at least one function of the at least one of the one or more smart contracts.
Step 1008: transmitting, by the processing device, the one or more blockchain transactions into the blockchain network.
Optionally, the method 1000 can 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 the predetermined privacy requirement.
This example scenario largely describes the process of recording the carbon footprints of a CMP before further verification and certification are performed. It is designed to validate the workflow in GPChain for CMP certification, as well as validate the functionality of the developed carbon data privacy protection strategy and secure smart contract interaction scheme.
The validation results of example scenario 1 are shown in
Advantageously, the well-structured blockchain data format, i.e., splitting the blockchain model into “Data Access Layer”, “Data Privacy Layer”, etc., as shown in
In order to evaluate the performance of GPChain, two performance indicators, i.e., latency and throughput, are selected and tested for GPChain's smart contracts. Latency is typically defined as the time interval between the time when a transaction is first sent to the blockchain and the time when it is confirmed (as shown in Equation (1)) by the users of the blockchain network and is a measure of the data delivery efficiency of, in this case, transactions generated using GPChain's smart contracts. As such, latency can be used to assess the delay in transactions generated via different smart contracts in the GPChain. Throughput can be defined by the number of transactions per second (TPS) that a blockchain network can successfully process or be committed into blockchain ledgers within a given timeframe. It is the measure of the scalability of, in this case, GPChain's smart contracts, which can be used to show the bandwidth or scope of users that can access GPChain for different smart contracts at a given timeframe. Further, TPS can be calculated using Equation (2) below, which is based on dividing the number of transactions per block and 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 Certification Smart Contract, are measured respectively by invoking all functions of the respective smart contracts. The final values of latency and TPS for each smart contract are derived from taking the average of the latency and TPS calculated or measured for different invoked smart contract functions. Considering the limited testnet ETHs that can be obtained from Goerli, each activity to invoke the smart contract function is measured ten times.
As shown in
Differing from private blockchain platforms, users are required to pay transaction fees when executing transactions on a public blockchain platform, e.g., Ethereum, etc. Apart from the technical development and maintenance costs for a blockchain-based platform, it is also important to measure the execution cost of blockchain transactions in public blockchain platforms since fees are generated for each blockchain transaction to store data. In Ethereum, this transaction fee is represented by “Gas”, which refers to the fee required to process a blockchain transaction successfully. As a fundamental network cost unit, Gas is typically paid using Ether (the cryptocurrency used in Ethereum) and is commonly used to evaluate a smart contract's economic performance and resource consumption. 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 costs for the three smart contracts are calculated by taking the average cost of the invoked functions, i.e., each function call is implemented ten times, then the average is taken.
As shown in
The computing device 2000 further includes a main memory 2006, such as a random access memory (RAM), and a secondary memory 2008. The secondary memory 2008 may include, for example, 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, or the like. 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, magnetic tape, optical disk, or the like, which is read by and written to by removable storage drive 2012. As will be appreciated by persons skilled in the relevant art(s), the removable storage unit 2014 includes a computer readable storage medium having stored therein computer executable program code instructions and/or data.
In an alternative implementation, 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 can include, for example, a removable storage unit 2016 and an interface 2018. Examples of a removable storage unit 2016 and interface 2018 include a program cartridge and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM or PROM) and associated socket, and other removable storage units 2016 and interfaces 2018 which allow software and data to be transferred from the removable storage unit 2016 to the computer system 2000.
The computing device 2000 also includes at least one communication interface 2020. The communication interface 2020 allows software and data to be transferred between computing device 2000 and external devices via a communication path 2022. In various embodiments of the inventions, the communication interface 2020 permits data to be transferred between the computing device 2000 and a data communication network, such as a public data or private data communication network. The communication interface 2020 may be used to exchange data between different computing devices 2000 which such computing devices 2000 form part an interconnected computer network. Examples of a communication interface 2020 can include a modem, a network interface (such as an Ethernet card), a communication port, an antenna with associated circuitry and the like. The communication interface 2020 may be wired or may be wireless. Software and data transferred via the communication interface 2020 are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communication interface 2020. These signals are provided to the communication interface via the communication path 2022.
As shown in
As used herein, the term “computer program product” may refer, in part, to removable storage unit 2014, removable storage unit 2016, a hard disk installed in hard disk drive 2010, or a carrier wave carrying software over communication path 2022 (wireless link or cable) to 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 tape, CD-ROM, DVD, Blu-Ray™ Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a magneto-optical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computing device 2000. Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and/or data to the computing device 2000 include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.
The computer programs (also called computer program code) are stored in main memory 2006 and/or secondary memory 2008. Computer programs can also be received via the communication interface 2020. Such computer programs, when executed, enable the computing device 2000 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 2002 to perform features of the above-described embodiments. Accordingly, such computer programs represent controllers of the computer system 2000.
Software may be stored in a computer program product and loaded into the computing device 2000 using the removable storage drive 2012, the hard disk drive 2010, or the interface 2018. Alternatively, the computer program product may be downloaded to the computer system 2000 over the communications path 2022. The software, when executed by the processor 2002, causes the computing device 2000 to perform functions of embodiments described herein.
It is to be understood that the embodiment of
It will be appreciated that the elements illustrated in
In an implementation, a server may be generally described as a physical device comprising at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform the requisite operations.
When the computing device 2000 is configured to realise the system 900 for managing carbon data using a blockchain network, the system 900 can have a non-transitory computer readable medium having stored thereon an application which when executed causes the system 900 to perform the method 1000, which includes steps 1000 to 1008 as follows.
Step 1002: categorizing, by a processing device, each of one or more carbon data received from at least one user of the blockchain network into respective privacy levels. Each of the respective privacy levels represents a privacy requirement corresponding to each of the one or more carbon data.
Further, each of the one or more carbon data can include basic product information and/or manufacturing information of at least one construction material or product. In addition, the privacy requirement of each of the one or more carbon data can be predetermined based on the basic product information and/or the manufacturing information.
Step 1004: encrypting, by the processing device, each of the one or more categorized 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.
In one embodiment, the respective privacy levels can include first, second and third privacy levels. Further, the first privacy level carbon data can be accessible to all users in the blockchain network, the second privacy level carbon data can be accessible to users authorized by the owner of the carbon data, and the third privacy level carbon data can be accessible only by the owner of the carbon data. In this embodiment, the plurality of encryption schemes can include an asymmetric encryption scheme and a homomorphic encryption scheme. Furthermore, the first and second level privacy carbon data can be encrypted using the asymmetric encryption scheme, and the third level privacy level carbon data can be encrypted using the homomorphic encryption scheme.
Step 1006: generating, by the processing device, one or more blockchain transactions corresponding to each of the one or more encrypted carbon data.
The one or more blockchain transactions can be generated using one or more smart contracts. Further, at least one of the one or more smart contracts can be configured to only allow at least one user authorized by an owner of the at least one of the one or more smart contracts to invoke at least one function of the at least one of the one or more smart contracts.
Step 1008: transmitting, by the processing device, the one or more blockchain transactions into the blockchain network.
Optionally, the method 1000 can 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 the predetermined privacy requirement.
In conclusion, embodiments of the present disclosure provide a blockchain-based framework for secure carbon management during CMP certification. Compared with existing processes that proposed theoretical frameworks for construction carbon management, the proposed blockchain-based framework and workflow are designed based on real-life case studies, i.e., not at the conceptual level. Firstly, embodiments of the present disclosure provide a blockchain data model with multi-privacy levels for CMP certification that can identify the carbon data provided by user(s) in the blockchain network and their sensitivity/privacy requirement, of which the data can be categorized into different privacy levels based on the privacy requirement of each carbon data. In addition, the blockchain-based framework can also provide structured flow of carbon data and information for CMP certification in the blockchain environment. Further, 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 for sensitive carbon data by integrating asymmetric encryption and homomorphic encryption schemes with blockchain, which particularly satisfies secure carbon data-sharing requirements from different privacy levels. Furthermore, embodiments of the present disclosure also provide granular access control smart contracts to enable secure user interactions in blockchain-based CMP carbon certification. Specifically, the granular access control can directly control or restrict users from invoking certain smart contracts or smart contract functions.
Finally, future work can be directed to (1) developing a comprehensive blockchain-based application for CMP carbon certification by including user registration and access control of reading and writing data, which would be helpful to improve the user experience for further promotion in the whole industry and (2) integration with international standards for carbon footprint quantification and reporting to provide a standardized carbon management process for users.
It will be appreciated by a person skilled in the art that numerous variations and/or modifications may be made to the present invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects to be illustrative and not restrictive.
The present application claims priority from U.S. Provisional Patent Application No. 63/606,563, filed on Dec. 5, 2023, which is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63606563 | Dec 2023 | US |
