Deebak et al., 2021 - Google Patents
Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurementsDeebak et al., 2021
- Document ID
- 14069379044655681051
- Author
- Deebak B
- Fadi A
- Publication year
- Publication venue
- Journal of Information Security and Applications
External Links
Snippet
Blockchain application and cyber-physical systems play a crucial role to modernize the traditional industrial process, technical procedures, and business models. It uses frame resilient and smart contracts to reduce the complexities of service costs. The blockchain …
- 238000005259 measurement 0 title description 7
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
- G06F21/6263—Protecting personal data, e.g. for financial or medical purposes during internet communication, e.g. revealing personal data from cookies
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
-
- 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 communication
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to network resources
- H04L63/105—Multiple levels of security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/30—Network architectures or network communication protocols for network security for supporting lawful interception, monitoring or retaining of communications or communication related information
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Deebak et al. | Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements | |
| Tanwar et al. | Machine learning adoption in blockchain-based smart applications: The challenges, and a way forward | |
| Silva et al. | Towards federated learning: An overview of methods and applications | |
| Sheth et al. | Deep learning, blockchain based multi-layered authentication and security architectures | |
| US20230316127A1 (en) | Distributed computer system and method of operation thereof | |
| Ganapathy et al. | A blockchain based federated deep learning model for secured data transmission in healthcare IoT networks | |
| Ural et al. | Survey on blockchain-enhanced machine learning | |
| Shenoy et al. | Exploring privacy mechanisms and metrics in federated learning | |
| Demertzis et al. | An explainable semi-personalized federated learning model | |
| US20220318270A1 (en) | Artificial intelligence (ai)-based blockchain management | |
| Kaur et al. | A neutrosophic AHP-based computational technique for security management in a fog computing network: J. Kaur et al. | |
| Chaudhary et al. | A systematic review on federated learning system: a new paradigm to machine learning | |
| Liu et al. | Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain | |
| Xiao et al. | CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representation: L. Xiao et al. | |
| Hajlaoui et al. | Protecting machine learning systems using blockchain: solutions, challenges and future prospects | |
| Guembe et al. | Privacy issues, attacks, countermeasures and open problems in federated learning: a survey | |
| Aounzou et al. | Convergence of blockchain, IoT, and machine learning: exploring opportunities and challenges–a systematic review | |
| Liu et al. | XAI driven intelligent IoMT secure data management framework | |
| Sekhar et al. | A novel blockchain-assisted deep learning model for secure edge intelligence in IoT networks | |
| Kuforiji | Digital Forensics and Incident Response (DFIR) Automation: Leveraging AI to Accelerate Breach Investigation, Evidence Collection, and Cyberattack Mitigation | |
| D’aniello et al. | Blockchain and AI-based methods for trust management in IoT: A comprehensive survey | |
| Bagchi et al. | APDRChain: ANN based predictive analysis of diseases and report sharing through blockchain | |
| Tawfik et al. | FedMedSecure: federated few-shot learning with cross-attention mechanisms and explainable AI for collaborative healthcare cybersecurity | |
| Patel et al. | An exploration to blockchain-based deep learningframework | |
| Sumarlinda et al. | The Improvement Prediction Model Using Anfis for Medical Dataset |