Results 41 to 50 of about 18,513 (227)
ANT-ACE: An FHE Compiler Framework for Automating Neural Network Inference
IEEE/ACM International Symposium on Code Generation and OptimizationFully Homomorphic Encryption (FHE) facilitates computations on encrypted data without requiring access to the decryption key, offering substantial privacy benefits for deploying neural network applications in sensitive sectors such as healthcare and ...
Long Li +9 more
semanticscholar +1 more source
Array, 2022 Fully Homomorphic Encryption (FHE) and Trusted Execution Environ-ments (TEEs) are complementing approaches that can both secure computa-tions running remotely on a public cloud.
Lars Brenna +3 more
doaj +1 more source
ReSBM: Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut
International Conference on Architectural Support for Programming Languages and Operating SystemsThe RNS-CKKS scheme in Fully Homomorphic Encryption (FHE) supports crucial features for privacy-preserving machine learning, such as fixed-point arithmetic and SIMD-style vectorization.
Yan Liu +9 more
semanticscholar +1 more source
CoRRFully Homomorphic Encryption (FHE) enables the evaluation of programs directly on encrypted data. However, because only basic operations can be performed on ciphertexts, programs must be expressed as boolean or arithmetic circuits. This low-level representation makes implementing applications for FHE significantly more cumbersome than writing code in a
Anne Müller +2 more
openaire +2 more sources
Attacks Against the IND-CPAD Security of Exact FHE Schemes
IACR Cryptology ePrint ArchiveA 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 the attacker access to a decryption oracle for ciphertexts for which it ...
Hee-Che-On Jung +4 more
semanticscholar +1 more source
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network
IEEE Access, 2022 Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches.
Joon-Woo Lee +10 more
doaj +1 more source
Designing a General-Purpose 8-bit (T)FHE Processor Abstraction
IACR Cryptology ePrint ArchiveMaking the most of TFHE programmable bootstrapping to evaluate functions or operators otherwise challenging to perform with only the native addition and multiplication of the scheme is a very active line of research.
Daphné Trama +4 more
semanticscholar +1 more source
PLANTS, PEOPLE, PLANET, EarlyView.As climate change alters seasonal patterns, temperate tree populations face a growing risk of phenological mismatch, where seed dispersal and germination no longer align with favourable conditions for survival. This study predicted how warming by the end of the century will affect seed dormancy breaking and germination in populations of three UK tree ...
Roberta L. C. Dayrell +8 more
wiley +1 more source
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT The increasing integration of distributed energy resources demands enhanced coordination between transmission system operators (TSOs) and distribution system operators (DSOs) to maintain reliable grid operation. Traditional approaches rely on hierarchical or distributed optimization schemes for power flow management, but these methods either ...
Janis Adamek +4 more
wiley +1 more source
A survey on fully homomorphic encryption compilers
CybersecurityFully Homomorphic Encryption (FHE) enables computations on encrypted data without decryption, ensuring data privacy in cloud computing and preventing sensitive information leakage.
Zhuoyu Tian +5 more
doaj +1 more source