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WO2024221057A1 - Système d'identité numérique et procédés - Google Patents

Système d'identité numérique et procédés Download PDF

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Publication number
WO2024221057A1
WO2024221057A1 PCT/AU2024/050410 AU2024050410W WO2024221057A1 WO 2024221057 A1 WO2024221057 A1 WO 2024221057A1 AU 2024050410 W AU2024050410 W AU 2024050410W WO 2024221057 A1 WO2024221057 A1 WO 2024221057A1
Authority
WO
WIPO (PCT)
Prior art keywords
biometric data
nft
digital identity
data
blockchain
Prior art date
Application number
PCT/AU2024/050410
Other languages
English (en)
Inventor
Nikola Siljeg
Original Assignee
NFID ICC Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2023901254A external-priority patent/AU2023901254A0/en
Application filed by NFID ICC Limited filed Critical NFID ICC Limited
Publication of WO2024221057A1 publication Critical patent/WO2024221057A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G06Q20/206—Point-of-sale [POS] network systems comprising security or operator identification provisions, e.g. password entry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/308—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using the Internet of Things
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/36—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes
    • G06Q20/367—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes
    • G06Q20/3674—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using electronic wallets or electronic money safes involving electronic purses or money safes involving authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06Q50/265—Personal security, identity or safety
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/761—Proximity, similarity or dissimilarity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G06V40/23—Recognition of whole body movements, e.g. for sport training
    • G06V40/25—Recognition of walking or running movements, e.g. gait recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/40—Spoof detection, e.g. liveness detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3226—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using a predetermined code, e.g. password, passphrase or PIN
    • H04L9/3231—Biological data, e.g. fingerprint, voice or retina
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive

Definitions

  • the present invention relates to a system and methods for using biometric markers for digital identification of an individual utilising non-fungible tokens on a block chain network to create a non-fungible identification (NFID).
  • NFID non-fungible identification
  • NFT Non-Fungible Token
  • the assigned blockchain node and an identity authorization application linked to any blockchain network can be uploaded to and assigned a private key perhaps described as a certificate or smart contract through cryptography providing a Proof of Work (PoW) or Proof of Stake (PoS) and ownership rights over the now digitized biometric asset.
  • PoW Proof of Work
  • PoS Proof of Stake
  • This digital asset and ID tool could also be quickly provided to crypto wallets and apps that provide the users blockchain address/key/password and NFT’s.
  • the object of this invention is to provide a digital identity system and methods to alleviate the above problems, or at least provide the public with a useful alternative.
  • the invention provides a method for creating a digital identity of a person based on biometric data of the persons, comprising the steps of: a) capturing biometric data of the person; b) creating a vector file to represent the biometric data; and c) uploading the vector file into a blockchain network in an original block as a non-fungible token (NFT) to produce an NFT asset.
  • NFT non-fungible token
  • the biometric data is selected from the group of: face print captured using a camera; voice spectrogram captured using a microphone; iris characteristics captured using a camera; gait captured using a camera; and a combination of face print, voice spectrogram, iris characteristics and gait.
  • the method further comprises generating a privacy-preserving vector mask representing the biometric data and uploading the vector mask to the NFT asset.
  • the method further comprises updating the vector file to represent the present biometric data of the person and uploading the vector mask to the NFT asset.
  • the method further comprises a Know Your Customer (KYC) verification process to ensure that the uploaded biometric data is associated with the person.
  • KYC Know Your Customer
  • the method further comprises adding supplementary biometric data to the NFT asset.
  • the supplementary data is added to the blockchain network as a new block which is chained to the NFT asset.
  • new block replaces the original block and uses the credentials of the original block.
  • the method further comprises attaching additional information to the NFT assets, to provide further authentication capabilities for the digital identity and a universal passport.
  • the digital identity is accessible and stored via a crypto wallet.
  • the method further comprises uploading to the NFT asset NFC (Near Field Communication) chip credentials belonging to the person, and using an NFC system to access the NFT asset to determine the NFC chip credentials.
  • NFC Near Field Communication
  • the method further comprises an optional two-factor authentication process for increased security when accessing and managing the digital identity, wherein the user may choose to provide additional verification, such as a one-time password (OTP), secondary or third biometric field (Voice, Fingerprint, Iris), or physical security key.
  • OTP one-time password
  • Voice Fingerprint
  • Iris physical security key
  • the privacy-preserving vector mask is generated using advanced security and or encryption techniques and/or zero-knowledge proof (ZKP) protocols, ensuring that the actual biometric data remains secure and confidential.
  • ZKP zero-knowledge proof
  • the method further comprises detecting and reporting any unauthorized access or usage of the digital identity, including unauthorized copying, sharing, or selling of the biometric data or its associated Image and likeness.
  • the method further comprises revoking access to the digital identity in the event of a security breach, lost or compromised private key, or upon the person’s request, wherein the blockchain network will update the status of the digital identity and prevent further unauthorized access or usage.
  • the method further comprises an adaptive algorithm or Al that learns and updates the biometric data over time, accounting for changes in an individual's facial features and voice due to aging, surgery, or other factors, thereby maintaining the accuracy and relevance of the digital identity.
  • an adaptive algorithm or Al that learns and updates the biometric data over time, accounting for changes in an individual's facial features and voice due to aging, surgery, or other factors, thereby maintaining the accuracy and relevance of the digital identity.
  • the digital identity is used as a decentralized attestation platform, allowing third-party entities to verify and authenticate the user's identity, credentials, or other attributes without having direct access to the sensitive biometric data.
  • the method further comprises selectively granting access to specific portions of the digital identity, allowing the user to maintain control over the sharing and disclosure of personal information.
  • any one of the aspects mentioned above may include any of the features of any of the other aspects mentioned above and may include any of the features of any of the embodiments described below as appropriate.
  • Figure 1 shows the system architecture of the digital identification system of the present invention.
  • Figure 2 shows a system flow diagram for using a person’s face to generate a digital identity.
  • NFID - Non-Fungible Identification a digital identification system and methods (referred to as NFID - Non-Fungible Identification) comprising a decentralized, self-sovereign digital identity and its proof of ownership via a smart contract on a blockchain network.
  • the digital identity specifically represents a person’s physical, real- life, face print, and biometric markers obtained through 2D, 3D, or multi-directional liveness scans of the face as well as other capture methods used to obtain biometric recordings.
  • the biometrics are then digitized into a unique vector file for each individual holder.
  • the generated vector file is uploaded into a blockchain network as a non- fungible token (NFT) asset to provide proof of ownership, preserved on a secure, immutable network.
  • NFT non- fungible token
  • the invention further includes a privacy-preserving vector mask for representing the biometric data, federated and Al learning for improved biometric accuracy, a mechanism for updating biometric data to accommodate changes in an individual's appearance over time, and integration with various applications and platforms for identity verification and authentication purposes.
  • the invention also incorporates voice biometrics in the form of sound spectrograms and a feature that enables integration with video chat and voice call applications to ensure secure communication are protect against deep fakes and cybercrime.
  • the invention provides a system for creating a decentralized, self-sovereign digital identity of a real-life person's face print and voice biometrics, comprising: a) capturing a 2D, 3D, or multi-directional liveness scans or any of the methods mentioned below, of the face including recording the individual's voice or additional biometrics (iris, fingerprint, gate etc.).
  • the system further comprises of a Know Your Customer (KYC) verification process to ensure that the uploaded biometric data is associated with the real-life individual and is not being used for fraudulent or Anti Money Laundering efforts (AML) purposes.
  • KYC Know Your Customer
  • the system further comprises a mechanism for adding supplementary biometric data, such as additional facial scans, voice recordings, or other biometric scans and information, to the NFT assets, wherein the supplementary data is added to the blockchain network as a new block that can be chained to the original block containing the initial biometric data, or destroying the previous block and uploading a new block using the credentials of the last block or in turn creating a new block altogether with all the relevant data.
  • supplementary biometric data such as additional facial scans, voice recordings, or other biometric scans and information
  • the system further comprises a mechanism for attaching additional information to the NFT assets, such as diplomas, certifications, health care information or other personal credentials like bank account details or crypto wallet addresses, to provide further authentication capabilities for the digital identity and universal passport.
  • additional information such as diplomas, certifications, health care information or other personal credentials like bank account details or crypto wallet addresses, to provide further authentication capabilities for the digital identity and universal passport.
  • the system further comprises a mechanism for assigning a copyright or trademark to the NFT assets, acknowledging the individual's rights to their digital identity as a personal asset or brand in the Web3 and Virtual world and or real-world space the likes of YouTube, podcasts or similar as examples but not limited to where they would need protection or monetization of their personal brand.
  • the digital identity is accessible and stored via a crypto wallet (e.g. MetaMask) or NFID purpose built digital wallet of its own or the likes, allowing for seamless integration with various banking and payment solution providers for fiat or crypto currency or blockchain networks, Distributed Ledger Technologies (DLT), crypto networks, applications, platforms and real world internet of things and smart cities, as an example an NFC chip (near field communication) allows a user to upload their NFC chip credentials into their NFID and instead of using an NFC chip for access to a restricted area or office building can instead use a smart device camera and could simply scan the users face and or voice or other biometrics of an NFID holder and they would then if verified be granted access to the area in question.
  • a crypto wallet e.g. MetaMask
  • NFID purpose built digital wallet of its own or the likes allowing for seamless integration with various banking and payment solution providers for fiat or crypto currency or blockchain networks, Distributed Ledger Technologies (DLT), crypto networks, applications, platforms and real world internet
  • a passport for travelling can now be made obsolete and no longer needed to carry a physical passport when abroad.
  • An NFID holder may scan the NFC chip found in a Passport and upload the passport document information into their NFID identity during the said NFID, KYC process to verify authenticity and ownership. This would then allow an actual copy of a government issued document of the highest importance relevant to each countries individual requirements to be verified and now digitized.
  • a person can now be identified, protected and self-sovereign/autonomous by using their biometrics backed by blockchain through NFID.
  • the system further comprises a mechanism for licensing the use of the biometric data, wherein the owner of the NFT assets can establish terms and conditions for third-party usage of their digital identity, including licensing fees and or penalties for non-compliance.
  • the smart contracts associated with the NFT assets are automatically updated to reflect any changes in the individual's biometric data, ensuring that the digital identity remains current and accurate over time.
  • the system further comprises an optional two-factor authentication process for increased security when accessing and managing the digital identity, wherein the user may choose to provide additional verification, such as a one-time password (OTP), secondary or third biometric field (Voice, Fingerprint, Iris), or physical security key, or using a device’s stored biometric data such as a fingerprint to further verify the individual.
  • OTP one-time password
  • Voice Fingerprint
  • Iris physical security key
  • the privacy-preserving vector mask is generated using advanced security and or encryption techniques and/or zero-knowledge proof (ZKP) protocols, ensuring that the actual biometric data remains secure and confidential.
  • ZKP zero-knowledge proof
  • the system further comprises an automated process for detecting and reporting any unauthorized access or usage of the digital identity, such as unauthorized copying, sharing, or selling of the biometric data or its associated Image and likeness, as per any asset or artwork that is afforded such protections via IP laws.
  • the system further comprises a mechanism for revoking access to the digital identity in the event of a security breach, lost or compromised private key, or upon user request, wherein the blockchain network will update the status of the digital identity and prevent further unauthorized access or usage.
  • the system further comprises an adaptive algorithm or Al that learns and updates the biometric data over time, accounting for changes in an individual's facial features and voice due to aging, surgery, or other factors, thereby maintaining the accuracy and relevance of the digital identity.
  • the digital identity may be used as a decentralized, custodial or noncustodial, attestation platform, allowing third-party entities to verify and authenticate the user's identity, credentials, or other attributes without having direct access to the sensitive biometric data.
  • the system further comprises a mechanism for selectively granting access to specific portions of the digital identity, allowing the user to maintain control over the sharing and disclosure of personal information based on the context and purpose of the interaction.
  • the system can be utilized to facilitate secure and efficient peer-to-peer transactions, such as payments, contracts, or data exchange, by leveraging the digital identity as a trust anchor for verifying the parties involved in the transaction.
  • the system can also be used as a foundation for creating and managing a digital reputation, which can be further utilized for various purposes such as risk assessment, trustworthiness evaluation, and access control.
  • the digital identity system can be extended to include other biometric modalities, such as fingerprints, iris scans, or gait patterns, etc. thereby providing a comprehensive and robust digital identity solution.
  • the system can collaborate and or integrate by design to comply with relevant data protection and privacy regulations, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or other applicable legislation, ensuring the user's rights and interests are protected.
  • relevant data protection and privacy regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or other applicable legislation, ensuring the user's rights and interests are protected.
  • GDPR General Data Protection Regulation
  • CCPA California Consumer Privacy Act
  • the system further comprises integration of NFID face mask and vector file and voice biometrics as a sound spectrogram and or an Iris Scan and or a Fingerprint, to provide additional layers of security and authentication.
  • the NFID can be used to integrate into applications capable of having video chat and voice calls, like WhatsApp, Skype online chats like Zoom or VoIP capabilities, periodically running face matches, voice matches, or both during the call to ensure the highest level of security against deep fakes during live communications and interactions.
  • the voice call will cross-reference the voice it hears against the NFID voice database and provide a rating, score and or liveness score percentage or confirmation to indicate authentication and verification when on a call or using VR or in virtual worlds, such as a Metaverse.
  • the same techniques can be used for the Face match for liveness scores, deepfake mitigation, face match scores etc. but not limited to.
  • the NFID through its own or any available channels or networks or applications, owners of an NFID can access exclusive virtual AR and VR corporate offices, spaces and or meeting rooms, medical examinations, confidential gatherings, and conversations but not limited to, with the assurance that they are connecting securely and with verified and authorised identities/parties, or perhaps government officials needing to have a private digital gathering in a totally secure environment.
  • the NFID face and or voice matching can be used during any networked interaction to ensure that participants using any available communication channels such as 3G, 4G, 5G, or future generation of connectivity and telecommunication capabilities including VOIP or direct satellite, are cross referenced and the persons they claim to be are indeed the said persons, providing additional security and trust in digital communications.
  • any available communication channels such as 3G, 4G, 5G, or future generation of connectivity and telecommunication capabilities including VOIP or direct satellite, are cross referenced and the persons they claim to be are indeed the said persons, providing additional security and trust in digital communications.
  • Block 101 NFID website portal; Block 102 Crypto Address Linked to NFID Platform; Block 103 (2FA) Email/Phone Verification; Block 104 KYC Verification Process; Block 105 Wallet Address and Biometrics Data Linked; Block 106 Generate the Digital ID; Block 107 Mint and Link NFID to Blockchain Block; 108 NFID Created;
  • Block 9 Add Verifiers and Credentials; and, Block 110 NFID profile to search and verify other NFID users.
  • Block 101 NFID website portal - a website is a collection of interconnected webpages typically hosted on a web server and accessible through the Internet. Each webpage is constructed using HTML (Hypertext Markup Language), CSS (Cascading Style Sheets) for styling, and JavaScript for interactivity. Websites are accessed by users through browsers like Chrome, Firefox, or Safari, which interpret these codes to present the content.
  • HTML Hypertext Markup Language
  • CSS CSS
  • JavaScript JavaScript
  • Block 102 Crypto Address Linked to NFID Platform - a cryptocurrency wallet is a digital tool that allows users to store and manage their blockchain credentials keys that allow them to transact on the blockchain.
  • Wallets can be software-based (hosted on a device or a cloud service) or hardware-based (physical devices that store keys offline). Wallets do not store the actual cryptocurrencies but rather the keys needed to access them on the blockchain.
  • Crypto Wallet - Public Key comparable to an account number, it can be shared publicly to receive funds.
  • Private Key similar to an account password, it should be kept secure as it allows the signing of transactions, thereby providing control over the associated cryptocurrencies.
  • Crypto Wallet Functionality - Generation of Keys upon setup, a wallet generates a cryptographic key pair: a public key and a private key. The public key is derived mathematically from the private key.
  • Transaction Signing to execute a transaction (e.g., sending crypto), the wallet uses the private key to sign the transaction data, creating a digital signature. This signature proves ownership of the funds being sent.clnteraction with Blockchain: the signed transaction is broadcast to the blockchain network, where miners validate it and, upon successful validation, add it to a block in the blockchain. The transaction then becomes part of the public ledger.
  • the website incorporates a wallet interaction interface using HTML, CSS, and JavaScript. This interface includes buttons and forms to initiate transactions or queries.
  • JavaScript Libraries Libraries such as Web3.js or Ethers.js are integrated into the website. These libraries provide functions that interact with blockchain networks and are crucial for sending transactions and accessing wallet data.
  • Wallet Selection and Connection Browser Extension - Wallets Wallets like MetaMask operate as browser extensions. When a transaction is initiated from the website, the wallet extension is prompted to open, asking for user authentication and permission to access the wallet.
  • Mobile Wallets Using QR codes or deep links, websites can interact with mobile-based wallets. The user scans a QR code with their wallet app, which triggers wallet actions relevant to the website’s request.
  • Authentication and Permissions - OAuth Protocols/Token-based Authentication Secure methods are used to verify the identity of the user and ensure that the wallet connection request is authorized.
  • Permission Requests The website must request permission to view account addresses and initiate transactions. The user must explicitly approve these permissions, which are managed by the wallet software.
  • Transaction Initiation and Signing - Initiating Transactions The website constructs a transaction request (including details like recipient address, amount, and gas fees) and sends it to the wallet. Signing: The wallet, upon user confirmation, signs the transaction using the stored private key. This process is securely handled by the wallet software or hardware, never exposing the private key to the website.
  • Block 103 (2FA) Email/Phone Verification.
  • Two-Factor Authentication Setup - Initial Verification The server sends a verification code via SMS to the provided phone number and an email to the provided email address. Libraries like Twilio or SendGrid might be used for SMS and email services, respectively.
  • Verification Confirmation The user enters the received codes on the website, which are then sent back to the server to confirm the authenticity of the phone number and email address.
  • OCR Optical Character Recognition
  • Google Cloud Vision API Optical Character Recognition
  • This API extracts text data from the images, converting them into structured data.
  • API Calls for Data Validation - extracted data, such as names and document numbers, are validated against public records and databases through RESTful API calls to third-party services that specialize in identity verification.
  • Biometric Verification with Real-Time Facial Recognition and Liveness Detection - Facial Recognition Software the user is required to take a live selfie using their smartphone or webcam. This image is processed using facial recognition software integrated into the KYC system. Technologies like Microsoft Azure's Face API or Amazon Rekognition are commonly employed. Liveness Detection Mechanisms: To ensure the presence of a live person rather than a photo or video, liveness detection software analyses the selfie for signs of life.
  • Capturing and Processing Biometric Data - Biometric Capture the user's facial biometrics are captured using a high-resolution camera integrated into a secure application. This could be part of a KYC process where the user's live facial features are scanned.
  • Feature Extraction advanced facial recognition software processes the captured image to extract key facial landmarks (eyes, nose, mouth, jawline, etc.). This is typically done using deep learning models trained to identify and map these features accurately.
  • Generate Vector Representation instead of storing raw biometric data, which poses privacy risks, the system converts the facial landmarks into a mathematical vector representation. This vector describes the relative positions and distances of facial landmarks and does not recreate the actual facial image.
  • Algorithms such as Principal Component Analysis (PCA) or Convolutional Neural Networks (CNNs) can be used to reduce the dimensionality of the data and generate a unique but anonymized "faceprint” or other biometric data such as a voice print, fingerprint, or the like.
  • PCA Principal Component Analysis
  • CNNs Convolutional Neural Networks
  • NFT Non-Transferable NFT - Smart Contract for NFT: a smart contract is developed on a blockchain platform like Ethereum. This contract contains the logic for minting NFTs and ensures that the NFTs are non-transferable once assigned to a wallet. This can be enforced by coding the transfer function to reject all transactions except those approved by the original minter (typically the issuing authority).
  • Minting the NFT the vector file or faceprint but not limited to is embedded within the NFT as its unique identifier or "tokenURI". This URI points to a secure location where the faceprint vector is stored encrypted.
  • the smart contract mints the NFT with a linkage to the user’s cryptocurrency wallet address. Metadata within the NFT includes cryptographic hashes of the user’s vector file for verification without revealing actual data.
  • Secure Storage and Access Control - Data Encryption the faceprint vector stored as part of the NFT metadata is encrypted using strong encryption algorithms (e.g., AES-256) but not limited to.
  • the decryption key is managed securely, ensuring that only authorized entities can access the actual data.
  • Blockchain Transactions all interactions with the NFT (queries, validations) are managed through blockchain transactions. These transactions are secured by the blockchain’s inherent cryptographic and consensus mechanisms, ensuring that they are tamper-proof and traceable.
  • Access Control access to decrypt and read the faceprint vector is strictly controlled through smart contract rules. Only persons with the correct permissions, such as the matching biometric data can be granted access to their profile.
  • the requesting system can query the blockchain to access the NFT metadata.
  • the verification system uses the provided vector file mask to perform a comparison with a live scan of the user’s face. This process ensures that the user’s privacy is maintained, as the actual biometric data is never exposed. Updating Biometric Data - in cases where biometric data needs updating (e.g., significant physical changes to facial features), a new vector file is generated, and a new NFT is minted. The previous NFT can be burned or invalidated via the smart contract to maintain data integrity.
  • the biometric data can be based on a person’s facial features or voice, or even other distinguishing features such as their gait, or combinations thereof.
  • Biometric Data Capture and Processing utilize secure, high-definition cameras within controlled environments to capture biometric data, specifically facial features. This can be part of a broader KYC process facilitated through a secure application on a smartphone or a dedicated kiosk.
  • Feature Extraction - employ facial recognition algorithms to analyse the captured images and extract crucial facial landmarks. Techniques such as deep learning and convolutional neural networks (CNNs) are used to map features like the distance between eyes, nose shape, and jawline contours.
  • CNNs convolutional neural networks
  • Generate Anonymized Vector Representation convert the raw biometric data into a numerical vector that describes facial features while ensuring privacy. This transformation uses dimensionality reduction techniques like Principal Component Analysis (PCA) to create a unique yet non-reversible vector, referred to as a "faceprint.”
  • PCA Principal Component Analysis
  • Figure 2 provides a system flow diagram 200 for using a person’s face to generate a verified digital identity, including the following functionality blocks.
  • the first step in facial recognition is acquiring a digital image of a person's face. This can be done using various types of cameras, such as RGB, infrared, or depth-sensing cameras, depending on the specific requirements of the application.
  • the camera sensor is responsible for converting light energy into electrical signals.
  • Most facial recognition systems use a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) sensor to capture the image.
  • CCD charge-coupled device
  • CMOS complementary metal-oxide-semiconductor
  • the camera lens focuses the light onto the sensor, determining the sharpness and quality of the captured image.
  • High-resolution images are preferable for facial recognition, as they provide more detail and allow for more accurate feature extraction. However, higher resolution also means larger file sizes and increased processing time.
  • pre-processing techniques are applied to improve the image quality and remove any unwanted noise or distortion.
  • Common preprocessing steps include: a) Grayscale Conversion - Converting the image to grayscale simplifies the data and reduces computational complexity while retaining sufficient information for facial recognition; b) Histogram Equalization - This technique enhances the image's contrast, ensuring that the facial features are more easily distinguishable; c) Noise Reduction - Noise reduction techniques, such as Gaussian smoothing or median filtering, are applied to remove any unwanted noise and artifacts from the image; and d) Geometric Normalization - Scaling, rotation, and translation transformations are applied to the image to normalize the face's position and size, ensuring consistency across different images.
  • Feature extraction involves identifying and extracting distinctive facial features from the pre-processed image.
  • There are several techniques for feature extraction including: a) Eigenfaces - This method uses principal component analysis (PGA) to identify the most significant features (eigenfaces) that represent the face's variations. The face is then projected onto the eigenface space to create a lowerdimensional representation, b) Local Binary Patterns (LBP) - LBP is a texture descriptor that encodes local contrast patterns in the image. LBP histograms can be used to represent facial features, c) Deep Learning - Deep learning techniques, such as convolutional neural networks (CNNs), can automatically learn hierarchical feature representations from the image data. Pre-trained models, such as VGGFace or FaceNet, can be fine-tuned for specific facial recognition tasks.
  • PGA principal component analysis
  • LBP Local Binary Patterns
  • CNNs convolutional neural networks
  • the facial features are represented as a feature vector or embedding.
  • This vector representation is a compact and efficient way of describing the face and can be used for comparison and matching purposes.
  • the vector representation can be generated using methods such as: a) PCA - In the eigenface method, the projection of the face onto the eigenface space creates a feature vector; b) LBP - The LBP histograms can be concatenated to form a feature vector; and c) CNNs - The output of the final fully connected layer in a CNN architecture can be used as the feature vector (embedding).
  • the final steps in facial recognition is comparing the vector representation of the captured face with a database of known faces. Similarity or distance metrics, such as Euclidean distance or cosine similarity, are used to measure the similarity between the captured face's vector representation and the vectors of faces in the database. A threshold value is typically used to determine if a match has been found. Identification - In this scenario, the captured face's vector representation is compared to all the vectors in the database to find the most similar match. If the similarity value exceeds the threshold, the system identifies the person as the corresponding match. Verification - In a verification scenario, the captured face's vector representation is compared to a specific known face's vector representation to confirm the person's claimed identity. If the similarity value exceeds the threshold, the system verifies the person's identity as claimed.
  • Similarity or distance metrics such as Euclidean distance or cosine similarity
  • TPR True Positive Rate
  • FPR False Positive Rate
  • Precision The proportion of correctly identified positive instances among all instances classified as positive
  • F1 -Score The harmonic mean of precision and recall, providing a balanced measure of the system's performance
  • ROC Receiver Operating Characteristic
  • Face Detection The first step in facial recognition is to detect faces in an image.
  • Dlib's pre-trained face detector which is based on a Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, an image pyramid, and sliding window detection scheme.
  • HOG Histogram of Oriented Gradients
  • the HOG feature descriptor captures the structural information of a face by considering the distribution of intensity gradients in localized portions of the image.
  • Facial Landmark Detection After detecting faces, the next step is to identify key facial landmarks, such as the corners of the eyes, nose, and mouth.
  • Dlib's shape predictor which is a pre-trained model based on an ensemble of regression trees. This model, shape_predictor_68_face_landmarks.dat, can detect 68 facial landmarks that are essential for extracting facial features but there is no real limit to captured points and NFID uses up to 400 points/landmarks or more.
  • Feature Extraction To represent the detected faces in a manner suitable for comparison, we need to extract facial features that capture unique information about each face.
  • Dlib's face_recognition_model_v1 based on the ResNet-34 architecture, which computes a 128-dimensional feature vector for each face. This deep learning model is pre-trained on a large dataset of faces and learns to generate a unique vector representation for each individual by considering various facial attributes.
  • Face Vector Comparison To compare two faces and determine their similarity, we calculate the Euclidean distance between their 128-dimensional feature vectors. If the distance is below a predefined threshold, the faces are considered to be similar or belonging to the same person.
  • ASM Active Shape Models
  • CoR Cascade of Regression
  • SDM Supervised Descent Method
  • CLM Constrained Local Model
  • ERT Ensemble of Regression Trees
  • DAN Deep Alignment Network
  • MTCNN Multi-task Cascaded Convolutional Networks
  • Facial recognition is used in conjunction with liveness testing to ensure that the facial features of an actual person co-operating with the system are captured in order to avoid deception by use of photos or videos of a person.
  • Active liveness testing requires user interaction, such as following on-screen prompts or performing specific actions (e.g., blinking, smiling, or turning the head). These tests help ensure that the subject is a live person rather than a static image or pre-recorded video.
  • Algorithms used for active liveness testing can include motion analysis, temporal analysis, and challenge-response methods.
  • Liveness testing using physiological signals typically involves analysing the inherent characteristics of a live person that are difficult to replicate, such as heart rate, blood flow, or skin temperature.
  • One common physiological signal used for liveness testing is the analysis of blood flow patterns in the face, also known as remote photoplethysmography (rPPG).
  • Motion Analysis This approach analyses the movement patterns of the face, such as the natural sway that occurs when a person is standing still. By tracking facial landmarks over time, the algorithm can determine if the motion is consistent with that of a live person.
  • Temporal Analysis This method involves examining the changes in facial appearance over a short period, such as variations in facial expressions or subtle movements. By comparing consecutive frames in a video feed, the algorithm can detect the dynamic changes expected from a live person.
  • Challenge-Response This technique requires the user to respond to a random challenge, such as blinking or smiling when prompted. The algorithm verifies if the subject's actions match the requested challenge, indicating that the subject is a live person.
  • Passive liveness testing does not require user interaction and instead relies on the natural properties of a live face, such as texture, depth, and physiological signals.
  • Algorithms used for passive liveness testing can include texture analysis, depth analysis, and physiological signal analysis.
  • Texture Analysis This approach examines the texture of the face, looking for patterns that are characteristic of live skin, such as micro-movements, skin reflectance, and perspiration.
  • Algorithms such as Local Binary Patterns (LBP) or Histogram of Oriented Gradients (HOG) can be employed to analyse facial texture and distinguish between live faces and spoofing attempts.
  • Depth Analysis Depth information can be used to differentiate between a live face and a flat image or video.
  • algorithms can generate a 3D representation of the face and verify its depth and contours. This can be achieved through methods such as point cloud analysis or depth map comparison.
  • Physiological Signal Analysis This technique involves detecting subtle physiological signals, such as blood flow or heart rate, which are unique to live subjects. Algorithms can analyse temporal changes in facial colour or thermal imaging data to capture these physiological signals and confirm the subject's liveness.
  • a robust facial recognition system incorporates liveness testing follows a multi-step process: a) Face Detection - First, the system detects a face in the input image or video using techniques like Viola-Jones, HOG, or deep learning-based approaches (e.g., MTCNN). b) Face Alignment and Normalization - The detected face is then aligned and normalized to ensure consistency across different input images.
  • Liveness Testing The system performs active or passive liveness testing (as described above) to ensure the presence of a live person, g) Feature Extraction - Once the liveness test is passed, the system extracts facial features using algorithms like Eigenfaces, Fisherfaces, Local Binary Patterns Histograms (LBPH), or deep learning-based approaches (e.g., FaceNet, ArcFace, or VGGFace2).
  • LBPH Local Binary Patterns Histograms
  • a microphone can be used to capture a person’s voiceprint.
  • the voiceprint scans will be represented and preserved as both a visual Waveform and or Spectrogram image/video and assigned as an NFT Non-Fungible Token including an actual sound recording clip of a spoken sentence representing the unique features of a person’s voice.
  • Statistical and technological methods are used to measure and find what is known as a mathematical model of a voice print using a set of numbers to represent shapes, sizes and movements of a person’s vocal organs each unique to every individual.
  • Mint Digital ID as NFT mint an NFT that encapsulates the user’s anonymized biometric vector.
  • the smart contract encodes the vector into the NFT's metadata, stored securely on the blockchain.
  • the metadata includes a link (URI) to the encrypted vector file, which is kept in a decentralized file storage system like IPFS (Interplanetary File System).
  • Secure Storage and Encryption encrypt the vector file using cryptographic algorithms such as AES-256. Store the encryption keys in a secure key management system that allows controlled access based on predefined permissions managed through blockchain transactions.
  • Hash Creation - Generate Hashes create cryptographic hashes of the digital ID’s data.
  • Hash functions such as SHA-256 (Secure Hash Algorithm 256-bit) are used to produce a unique output (hash) that acts as a fingerprint of the data. Storing these hashes on the blockchain allows for the verification of data integrity without exposing the actual data.
  • Blockchain Smart Contract Interaction - Smart Contract Development- Contract Coding develop a smart contract in Solidity (for Ethereum) or another smart contract language that supports the functionality needed. This contract governs how digital IDs are stored, accessed, and managed on the blockchain.
  • Contract Functions include functions to add new digital IDs, update existing ones, and verify stored data against provided hashes.
  • Contract Deployment - Deploy to Blockchain deploy the smart contract to the blockchain. This involves compiling the contract and transmitting it to the blockchain network, where it is propagated across all nodes.
  • Broadcast Transaction Send the signed transaction to the blockchain network, where it will be verified by network participants (miners or validators) and added to a block.
  • Confirmation and Error Handling - Transaction Confirmation - Block Inclusion monitor the transaction until it is included in a block and confirmed by the network. The number of confirmations required can vary depending on the blockchain's security protocols. Receipt Retrieval: retrieve the transaction receipt, which includes the transaction status, block number, and gas used, ensuring everything went as planned.
  • Consolidation of User Data Data Aggregation - after the completion of the KYC and biometric capture processes, the relevant user data, including verified personal details (name, address, date of birth) and the biometric vector (from the KYC process) are aggregated. This aggregation happens in the backend of the website, typically managed by server-side applications developed in languages such as Node.js, Python, or Java.
  • Digital ID Formation The server-side application constructs a digital ID object. This object includes not only the user's personal and biometric information but also references to the blockchain-stored data, such as the link to the non-transferable NFT that represents their biometric vector.
  • Encryption and Security all sensitive data within the digital ID is encrypted using robust encryption standards (e.g., AES-256) to ensure data integrity and confidentiality.
  • the encryption keys are managed via secure key management practices, often involving hardware security modules (HSMs) or similar technologies.
  • Blockchain Integration - Blockchain Record a blockchain transaction is executed to record the creation of the new digital ID. This transaction logs the user’s encrypted digital ID and the hash of the NFT link on the blockchain, providing an immutable ledger entry that verifies the ID’s creation date and time.
  • Smart Contract Interaction the website interacts with a smart contract on the blockchain to register the digital ID. This contract operation includes storing the minimal necessary data on the blockchain to ensure privacy while keeping references that allow for validation and verification of the ID against the blockchain data.
  • Database Storage the complete digital ID, including references to the blockchain data, is stored in the website's database.
  • This database is typically a secure, scalable database like PostgreSQL or MongoDB, which supports large volumes of data and complex data structures.
  • User Access and Interaction User Interface (Ul) Integration - the website provides a user interface where users can view their digital ID and the status of their biometric NFT. This Ul is designed with modern web technologies such as React or Angular, ensuring a responsive and secure experience.
  • Block 109 Add Verifiers and Credentials.
  • Profile Interaction and Management - Profile Dashboard once access is granted, the user is directed to their personal NFID dashboard. This web interface is tailored to display all relevant user information and credentials in a user-friendly layout. The dashboard is designed to be intuitive, allowing users to easily navigate through various sections such as personal information, bank account details, social media links, healthcare data, etc.
  • Adding and Managing Credentials users can add new credentials directly through their dashboard. For instance, to add a bank account detail, users would select the ‘Add New Bank Account’ option, fill in the necessary fields (e.g., account number, SWIFT code), and submit the data for encryption and secure storage.
  • Each section for adding credentials includes inline validation to ensure data integrity and correctness before submission.
  • Updating Existing Information users can update or remove existing information with just a few clicks.
  • Each piece of data listed in the dashboard includes 'edit' and 'delete' options, providing users with complete control over their information. Changes are processed in real-time, with all modifications encrypted and updated securely within the database.
  • Session Security all interactions within the session are conducted over HTTPS, encrypting the data exchange to prevent interception or unauthorized access.
  • the profile-access phase where users interact with their NFID profile following successful biometric authentication provides a secure and user-friendly environment for managing personal verifiers and credentials.
  • the system design emphasizes ease of use, security, and full user control over personal data, aligning with best practices in data protection and user experience design.
  • Block 110 NFID profile to search and verify other NFID users.
  • Profile Cross Referencing - the NFID system involves leveraging it as a cross-referencing tool within the ID profile where NFID users can verify each other's credentials, such as bank accounts, SWIFT or IBAN codes or cryptocurrency wallet addresses. This capability enhances trust and security within the ecosystem, facilitating safe and verified transactions or interactions among users.
  • Credential Sharing and Request Setup - User Consent and Data Sharing Setup - Consent Mechanism before any credential can be shared or verified, the NFID system ensures that user consent is obtained. Users can set permissions in their NFID profile to specify which data can be shared and under what conditions.
  • Credential Sharing Interface the NFID dashboard includes an interface where users can request to verify or share specific credentials with other users. This is managed through a secure form where users specify the credential type and the NFID of the user they wish to verify.
  • Credential Confirmation and Feedback - Verification Feedback once the verification is complete, both parties could receive a confirmation, or it can be automated. The requesting party can now view the verified credential marked as authenticated in their interface, ensuring accuracy and transparency.
  • the NFID system includes a dashboard feature that allows users to manage verification requests. This dashboard displays incoming and outgoing requests, statuses, and user controls for managing these requests. User Controls: Users have tools to revoke previously granted verifications or to update the terms of data sharing based on new consent or changing circumstances.
  • NFID serve as a powerful novel cross-referencing, verification tool that allows users to verify and prove each other’s credentials securely and effectively. This process not only enhances the security and trust within the NFID user community but also supports a wide range of applications, from financial transactions to secure communications, underpinned by robust privacy and security frameworks.
  • the invention provides a comprehensive, decentralized, and self-sovereign digital identity system, encompassing a wide range of unique features that address key challenges in privacy, security, and adaptability.
  • the facial recognition technology used involves capturing a person's face using a camera, pre-processing the image, extracting distinctive facial features, creating a vector representation of those features, and comparing it to a database of known faces for identification or verification .
  • incorporation of NFID face mask and vector files, voice biometrics, integration with video chat and voice call applications, and usage within virtual environments enhances the novelty and patentability of the invention.
  • the inventive steps outlined in this application demonstrate the potential for this system to transform the way digital identities are created, managed, and used, ultimately empowering individuals to take control of their personal data and digital footprint and offers a novel solution for managing and verifying identities in various applications, platforms, and settings.

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Abstract

L'invention concerne une identité numérique et sa preuve de propriété par l'intermédiaire d'un contrat intelligent sur un réseau de chaîne de blocs représentant des traits physiques d'une personne, une empreinte faciale ou des marqueurs biométriques obtenus par l'intermédiaire de balayages du visage ainsi que d'autres méthodes de capture. Les informations biométriques sont numérisées en un fichier vectoriel qui est téléversé dans un réseau de chaîne de blocs sous la forme d'un actif jeton non fongible (NFT). L'invention concerne en outre un masque vectoriel préservant la confidentialité servant à représenter les données biométriques, et un mécanisme de mise à jour de données biométriques pour les adapter à des changements des caractéristiques d'un individu au cours du temps.
PCT/AU2024/050410 2023-04-28 2024-04-28 Système d'identité numérique et procédés WO2024221057A1 (fr)

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AU2023901254A AU2023901254A0 (en) 2023-04-28 Decentralized, Self-Sovereign Digital Identity and Universal Passport System Based on Biometrics and Blockchain Technology

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119312405A (zh) * 2024-12-19 2025-01-14 田楚(上海)科技有限公司 一种基于加密技术的隐私保护社交朋友圈实现方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114817888A (zh) * 2022-06-27 2022-07-29 中国信息通信研究院 证书登记和颁发方法、装置与存储介质
KR102432248B1 (ko) * 2022-03-03 2022-08-12 가천대학교 산학협력단 아바타를 생성하여 외부의 메타버스플랫폼에 제공하여 아바타를 업데이트하고, 업데이트된 아바타에 대한 nft를 제공하는 시스템 및 방법
KR20230011818A (ko) * 2021-07-14 2023-01-25 김현주 Nft를 이용한 디지털 저작물 검색 시스템 및 방법
US20230114650A1 (en) * 2019-12-10 2023-04-13 Winkk, Inc Encryption and privacy protection using human attributes and behaviors
US20230117399A1 (en) * 2021-10-17 2023-04-20 Artema Labs, Inc Systems and Methods for Assessing Content Similarity in NFT-Directed Environments

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230114650A1 (en) * 2019-12-10 2023-04-13 Winkk, Inc Encryption and privacy protection using human attributes and behaviors
KR20230011818A (ko) * 2021-07-14 2023-01-25 김현주 Nft를 이용한 디지털 저작물 검색 시스템 및 방법
US20230117399A1 (en) * 2021-10-17 2023-04-20 Artema Labs, Inc Systems and Methods for Assessing Content Similarity in NFT-Directed Environments
KR102432248B1 (ko) * 2022-03-03 2022-08-12 가천대학교 산학협력단 아바타를 생성하여 외부의 메타버스플랫폼에 제공하여 아바타를 업데이트하고, 업데이트된 아바타에 대한 nft를 제공하는 시스템 및 방법
CN114817888A (zh) * 2022-06-27 2022-07-29 中国信息通信研究院 证书登记和颁发方法、装置与存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHUANG YAN, SHYU CHI-REN, HONG SHENDA, LI PENGFEI, ZHANG LUXIA: "Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology", COMPUTERS IN BIOLOGY AND MEDICINE, NEW YORK, NY, US, vol. 157, 1 May 2023 (2023-05-01), US , pages 106778, XP093233151, ISSN: 0010-4825, DOI: 10.1016/j.compbiomed.2023.106778 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119312405A (zh) * 2024-12-19 2025-01-14 田楚(上海)科技有限公司 一种基于加密技术的隐私保护社交朋友圈实现方法

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