Openfhe: Open-source fully homomorphic encryption library
A Al Badawi, J Bates, F Bergamaschi… - proceedings of the 10th …, 2022 - dl.acm.org
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables
performing computations over encrypted data without having access to the secret key. We …
performing computations over encrypted data without having access to the secret key. We …
Craterlake: a hardware accelerator for efficient unbounded computation on encrypted data
Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …
Privacy-preserving machine learning with fully homomorphic encryption for deep neural network
Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine
learning (PPML). Several PPML models have been proposed based on various FHE …
learning (PPML). Several PPML models have been proposed based on various FHE …
Survey on fully homomorphic encryption, theory, and applications
Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …
cloud services, edge computing, artificial intelligence applications, and other applications …
Bts: An accelerator for bootstrappable fully homomorphic encryption
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions
Recently, the standard ResNet-20 network was successfully implemented on the fully
homomorphic encryption scheme, residue number system variant Cheon-Kim-Kim-Song …
homomorphic encryption scheme, residue number system variant Cheon-Kim-Kim-Song …
Bolt: Privacy-preserving, accurate and efficient inference for transformers
The advent of transformers has brought about significant advancements in traditional
machine learning tasks. However, their pervasive deployment has raised concerns about …
machine learning tasks. However, their pervasive deployment has raised concerns about …
POSEIDON: Privacy-preserving federated neural network learning
S Sav, A Pyrgelis, JR Troncoso-Pastoriza… - arXiv preprint arXiv …, 2020 - arxiv.org
In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …
networks in an $ N $-party, federated learning setting. We propose a novel system …
Attacks Against the IND-CPAD Security of Exact FHE Schemes
A recent security model for fully homomorphic encryption (FHE), called IND-CPAD security
and introduced by Li and Micciancio [Eurocrypt'21], strengthens IND-CPA security by giving …
and introduced by Li and Micciancio [Eurocrypt'21], strengthens IND-CPA security by giving …
Sok: Fully homomorphic encryption accelerators
Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …
computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to …