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A General Purpose Transpiler for Fully Homomorphic Encryption

2 code implementations15 Jun 2021

Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data.

Cryptography and Security Programming Languages

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

4 code implementations27 Dec 2019

We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

1 code implementation20 Mar 2023

Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.

Federated Learning Privacy Preserving

TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

2 code implementations7 Apr 2021

Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains.

BIG-bench Machine Learning Privacy Preserving

OpenFHE: Open-Source Fully Homomorphic Encryption Library

1 code implementation WAHC 2022 – 10th Workshop on Encrypted Computing & Applied Homomorphic Cryptography 2022

Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.

Deep Neural Networks for Encrypted Inference with TFHE

1 code implementation13 Feb 2023

Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption.

Privacy Preserving

One Glitch to Rule Them All: Fault Injection Attacks Against AMD's Secure Encrypted Virtualization

5 code implementations10 Aug 2021

Furthermore, using our approach, we can extract endorsement keys of SEV-enabled CPUs, which allows us to fake attestation reports or to pose as a valid target for VM migration without requiring physical access to the target host.

Cryptography and Security

Crypto-Nets: Neural Networks over Encrypted Data

1 code implementation18 Dec 2014

To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model.

PySEAL: A Python wrapper implementation of the SEAL homomorphic encryption library

2 code implementations5 Mar 2018

Motivation: The ability to perform operations on encrypted data has a growing number of applications in bioinformatics, with implications for data privacy in health care and biosecurity.

Quantitative Methods Cryptography and Security

nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data

2 code implementations23 Oct 2018

Homomorphic encryption (HE)--the ability to perform computations on encrypted data--is an attractive remedy to increasing concerns about data privacy in the field of machine learning.

Cryptography and Security