Results 211 to 220 of about 196,760 (255)

Homomorphic Encryption using Enhanced Data Encryption Scheme for Cloud Security

, 2018
S. Karthiga, G. Rajarajacholan
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APPLICATION OF FULLY HOMOMORPHIC ENCRYPTION IN SECURING AND PROCESSING ELECTRICAL ENERGY CONSUMPTION DATA IN SMART BUILDINGS

Lê Hoàn, Nguyễn Tùng Linh
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OpenFHE: Open-Source Fully Homomorphic Encryption Library

IACR Cryptology ePrint Archive, 2022
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.
A. Al Badawi, Jack Bates, Flávio Bergamaschi, David Cousins, Saroja Erabelli, N. Genise, S. Halevi, Hamish Hunt, Andrey Kim, Yongwoo Lee, Zeyu Liu, D. Micciancio, Ian Quah, Yuriy Polyakov, Saraswathy R.V., Kurt Rohloff, Jonathan Saylor, Dmitriy Suponitsky, M. Triplett, V. Vaikuntanathan, Vincent Zucca   +20 more
semanticscholar   +1 more source

Unification Modulo Homomorphic Encryption

Journal of Automated Reasoning, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anantharaman, Siva, Lin, Hai, Lynch, Christopher, Narendran, Paliath, Rusinowitch, Michael   +4 more
openaire   +3 more sources

Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System

IEEE Transactions on Network Science and Engineering, 2023
In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system.
Li Zhang, Jianbo Xu, P. Vijayakumar, P. Sharma, Uttam Ghosh   +4 more
semanticscholar   +1 more source

Privacy-Preserving Distributed Economic Dispatch of Microgrids: A Dynamic Quantization-Based Consensus Scheme With Homomorphic Encryption

IEEE Transactions on Smart Grid, 2023
This paper is concerned with the privacy-preserving distributed economic dispatch problem (ED) of microgrids. A homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the ...
Wei Chen, Lurao Liu, Guohua Liu
semanticscholar   +1 more source

Optimized Privacy-Preserving CNN Inference With Fully Homomorphic Encryption

IEEE Transactions on Information Forensics and Security, 2023
Inference of machine learning models with data privacy guarantees has been widely studied as privacy concerns are getting growing attention from the community.
Dongwoo Kim, Cyril Guyot
semanticscholar   +1 more source

MaskCrypt: Federated Learning With Selective Homomorphic Encryption

IEEE Transactions on Dependable and Secure Computing
The federated learning paradigm protects private data from explicit leakage, yet exposing the model weights still raises serious privacy concerns with well-known attacks, such as membership inference attacks. It has been acknowledged that mechanisms such
Chenghao Hu, Baochun Li
semanticscholar   +1 more source

Poseidon: Practical Homomorphic Encryption Accelerator

International Symposium on High-Performance Computer Architecture, 2023
With the development of the important solution for privacy computing, the explosion of data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to the hardware design.
Yinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu, M. Zhang, Xiaowei Li   +5 more
semanticscholar   +1 more source

THOR: Secure Transformer Inference with Homomorphic Encryption

IACR Cryptology ePrint Archive
As large language models are increasingly deployed in cloud environments, privacy concerns have become a significant issue. To address this challenge, we present THOR, a non-interactive framework for secure transformer inference using homomorphic ...
Jungho Moon, Dongwoo Yoo, Xiaoqian Jiang, Miran Kim   +3 more
semanticscholar   +1 more source