Results 11 to 20 of about 1,338 (191)
Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
CoRRIn contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. Fully homomorphic encryption (FHE) enables inference directly on encrypted inputs, but its practicality is hindered by expensive bootstrapping and inefficient approximations of non-linear activations.
Sayan Biswas +9 more
core +6 more sources
A Comparative Assessment of Homomorphic Encryption Algorithms Applied to Biometric Information
Inventions, 2023 This paper provides preliminary research regarding the implementation and evaluation of a hybrid mechanism of authentication based on fingerprint recognition interconnected with RFID technology, using Arduino modules, that can be deployed in different ...
Georgiana Crihan +2 more
doaj +2 more sources
CryptographyWe propose a new framework for compile-time ciphertext synthesis in fully homomorphic encryption (FHE) systems. Instead of invoking encryption algorithms at runtime, our method synthesizes ciphertexts from precomputed encrypted basis vectors using only ...
Dongfang Zhao
doaj +2 more sources
Journal of Economy and TechnologyThis paper presents a comparative study of various homomorphic encryption models to evaluate their qualitative and quantitative benefits and drawbacks in performing computations on encrypted data.
Aadit Shah +2 more
doaj +2 more sources
Accelerated Multi-Key Homomorphic Encryption via Automorphism-Based Circuit Bootstrapping
IEEE AccessIn FHEW-like bootstrapping, blind rotation is based on the CMUX Gate (The CMux gate is a controlled selector gate that uses a control input to choose between two data inputs for the output), and blind rotation based on automorphism (A bijective ...
Kangwei Xu, Ruwei Huang
doaj +2 more sources
Enhancing Privacy and Efficiency in Federated Learning Through Hybrid Homomorphic Encryption
Lecture Notes in Networks and SystemsFederated Learning (FL) allows the training of models over distributed data sources without compromising the privacy of users in different client devices. Nonetheless, encryption mechanisms, including Homomorphic Encryption (HE) and symmetric encryption, such as the Advanced Encryption Standard (AES), enhance security but usually come at the cost of ...
Domenico Tegolo
exaly +4 more sources
IEEE AccessConvolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the ...
Donghwan Kim +4 more
doaj +3 more sources
Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption [PDF]
, 2023 The rising popularity of machine learning (ML) in modern day data analysis has allowed scientist, businesses and ordinary users to gain access to powerful tools, which provide accurate insight into complex data. However, as more research is done into ML, it has been noticed that standard ML models experience privacy leakage, which can jeopardize the ...
Budžys, Mindaugas
openaire +2 more sources
Towards Globally Optimized Hybrid Homomorphic Encryption - Featuring the Elisabeth Stream Cipher. [PDF]
IACR Cryptol. ePrint Arch., 2022 Hybrid Homomorphic Encryption (HHE) reduces the amount of computation client-side and bandwidth usage in a Fully Homomorphic Encryption (FHE) framework. HHE requires the usage of specific symmetric schemes that can be evaluated homomorphically efficiently.
Cosseron, Orel +3 more
openaire +5 more sources