Jia et al., 2021 - Google Patents
Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoTJia et al., 2021
- Document ID
- 14995914474605284063
- Author
- Jia B
- Zhang X
- Liu J
- Zhang Y
- Huang K
- Liang Y
- Publication year
- Publication venue
- IEEE Transactions on Industrial Informatics
External Links
Snippet
With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on …
- 230000004224 protection 0 title abstract description 40
Classifications
-
- 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
-
- 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
- G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
-
- 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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/34—User authentication involving the use of external additional devices, e.g. dongles or smart cards
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
- G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
-
- 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
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jia et al. | Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT | |
Xu et al. | Data security issues in deep learning: Attacks, countermeasures, and opportunities | |
Liang et al. | Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems | |
Yazdinejad et al. | Federated learning for drone authentication | |
Zhao et al. | Privacy-preserving blockchain-based federated learning for IoT devices | |
Lyu et al. | Threats to federated learning | |
Zhao et al. | Differential privacy preservation in deep learning: Challenges, opportunities and solutions | |
CN111866869A (en) | Federal learning indoor positioning privacy protection method facing edge calculation | |
Jiang et al. | Data quality detection mechanism against label flipping attacks in federated learning | |
Chen et al. | RNN-DP: A new differential privacy scheme base on Recurrent Neural Network for Dynamic trajectory privacy protection | |
Vasa et al. | Deep learning: Differential privacy preservation in the era of big data | |
Salim et al. | A blockchain-enabled explainable federated learning for securing internet-of-things-based social media 3.0 networks | |
Feng et al. | Privacy-preserving tucker train decomposition over blockchain-based encrypted industrial IoT data | |
Chen et al. | Practical membership inference attack against collaborative inference in industrial IoT | |
Shyla et al. | Cloud security: LKM and optimal fuzzy system for intrusion detection in cloud environment | |
Zhang et al. | DP-TrajGAN: A privacy-aware trajectory generation model with differential privacy | |
Zhang et al. | G-VCFL: Grouped verifiable chained privacy-preserving federated learning | |
Qiu et al. | Mobile semantic-aware trajectory for personalized location privacy preservation | |
Ratnayake et al. | A review of federated learning: taxonomy, privacy and future directions | |
Hu et al. | Source inference attacks: Beyond membership inference attacks in federated learning | |
Pei et al. | Privacy-enhanced graph neural network for decentralized local graphs | |
Qi et al. | Blockchain Data Mining With Graph Learning: A Survey | |
Yu et al. | Security and Privacy in Federated Learning | |
Chen et al. | Rethinking the defense against free-rider attack from the perspective of model weight evolving frequency | |
Gupta et al. | An efficient federated learning based intrusion detection system using LS2DNN with PBKA based lightweight privacy preservation in cloud server |