Homomorphic Encryption based Federated Learning for Financial Data Security

Author:

Dhiman Shalini1,Nayak Sumitra1,Mahato Ganesh Kumar1,Ram Anil1,Chakraborty Swarnendu Kumar1

Affiliation:

1. National Institute of Technology,Department of Computer Science & Engineering,Arunachal Pradesh,India

Publisher

IEEE

Reference22 articles.

1. BatchCrypt: Efficient homomorphic encryption for Cross-Silo federated learning;zhang;USENIX Annual Technical Conf (USENIX ATC 20),2020

2. Homomorphic Encryption Based Privacy-Preservation for IoMT

3. Privacy-Preserving Federated Learning in Fog Computing

4. Model Optimization Method Based on Vertical Federated Learning

5. A compartive review on homomorphic encryption for cloud security;mahato;IEEE Journal of Research,2021

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1. An intelligent homomorphic blockchain approach for securing stock market data;Soft Computing;2024-01-02

2. A Survey on Blockchain and Artificial Intelligence for Improved Security Facilities in Stock Market Data;2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA);2023-12-21

3. Homomorphic Encryption Library, Framework, Toolkit and Accelerator: A Review;SN Computer Science;2023-11-20

4. FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE;Electronics;2023-11-13

5. Privacy-Preserving and Verifiable Decentralized Federated Learning;2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE);2023-06-15

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