Results 221 to 230 of about 196,760 (255)

Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications

IEEE Internet of Things Journal
The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks.
Neveen Mohammad Hijazi, Moayad Aloqaily, M. Guizani, B. Ouni, F. Karray   +4 more
semanticscholar   +1 more source

Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

IEEE Transactions on Industrial Informatics, 2021
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.
Bin Jia, Xiaosong Zhang, Jiewen Liu, Y. Zhang, Kejia Huang, Yongquan Liang   +5 more
semanticscholar   +1 more source

Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey

IEEE Internet of Things Journal
Federated learning (FL) offers distributed machine learning on edge devices. However, the FL model raises privacy concerns. Various techniques, such as homomorphic encryption (HE), differential privacy, and multiparty cooperation, are used to address the
Qipeng Xie, Siyang Jiang, Linshan Jiang, Yongzhi Huang, Zhihe Zhao, Salabat Khan, Wangchen Dai, Zhe Liu, Kaishun Wu   +8 more
semanticscholar   +1 more source

Improved Gentry–Halevi's fully homomorphic encryption‐based lightweight privacy preserving scheme for securing medical Internet of Things

Transactions on Emerging Telecommunications Technologies, 2023
Internet of Medical Things (IoMT) solutions have proliferated rapidly in the COVID‐19 pandemic era. The smart medical sensors capture real‐time data from remote patients and communicate it to medical servers in a secure and privacy‐preserving manner.
Ramalingam Praveen, P. Pabitha
semanticscholar   +1 more source

General Bootstrapping Approach for RLWE-Based Homomorphic Encryption

IEEE transactions on computers
Homomorphic Encryption (HE) makes it possible to compute on encrypted data without decryption. In lattice-based HE, a ciphertext contains noise, which accumulates along with homomorphic computations.
Andrey Kim, M. Deryabin, Jieun Eom, Rakyong Choi, Yongwoo Lee, Whan Ghang, Donghoon Yoo   +6 more
semanticscholar   +1 more source

HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption

International Conference on Machine Learning
Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datasets.
Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee   +4 more
semanticscholar   +1 more source

Homomorphic Encryption-Based Resilient Distributed Energy Management Under Cyber-Attack of Micro-Grid With Event-Triggered Mechanism

IEEE Transactions on Smart Grid
Privacy disclosures and malevolent data intrusions targeting adversarial agents pose significant menaces to cyber-physical systems, a reality that extends to the intricate realm of micro-grid energy management.
Huifeng Zhang, Chengqian Yu, Meilian Zeng, Tao Ye, Dong Yue, Chunxia Dou, Xiang-peng Xie, Gerhard P. Hancke   +7 more
semanticscholar   +1 more source

Blockchain-Based Federated Learning With Enhanced Privacy and Security Using Homomorphic Encryption and Reputation

IEEE Internet of Things Journal
Federated learning, leveraging distributed data from multiple nodes to train a common model, allows for the use of more data to improve the model while also protecting the privacy of original data.
Ieee Ruizhe Yang Member, Tonghui Zhao, Ieee F. Richard Yu Fellow, Dajun Zhang, Xuehui Zhao, Meng Li   +5 more
semanticscholar   +1 more source

Homomorphic Encryption

2021
Michael Lahzi Gaid, Said A. Salloum
  +4 more sources

A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic Encryption

IEEE Transactions on Information Forensics and Security
Federated learning has gained prominence as an effective solution for addressing data silos, enabling collaboration among multiple parties without sharing their data.
Maoguo Gong, Yuanqiao Zhang, Yuan Gao, A. K. Qin, Yue Wu, Shanfeng Wang, Yihong Zhang   +6 more
semanticscholar   +1 more source