Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data.
Cryptography and Security Programming Languages
We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
Machine learning algorithms have achieved remarkable results and are widely applied in a variety of domains.
Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key.
Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption.
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
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.
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
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