Results 21 to 30 of about 163,266 (144)
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
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Puncturable fully homomorphic encryption based on Bloom filter [PDF]
PeerJ Computer ScienceFully Homomorphic Encryption (FHE) empowers secure computation over encrypted data, offering a crucial solution for privacy-preserving applications in cloud computing. However, the security of FHE is contingent upon robust key management practices.
Jhih-Wei Shen, Po-Wen Chi, Shao-Yuan Lin
doaj +2 more sources
The Gauss-Green theorem in stratified groups [PDF]
, 2019 We lay the foundations for a theory of divergence-measure fields in noncommutative stratified nilpotent Lie groups. Such vector fields form a new family of function spaces, which generalize in a sense the $BV$ fields.
Comi, Giovanni E., Magnani, Valentino
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Batch Symmetric Fully Homomorphic Encryption Using Matrix Polynomials
Труды Института системного программирования РАН, 2018 Fully homomorphic encryption (FHE) is a recognized tool to obtain the cryptographic protec-tion of cloud computing. However, the characteristics of existing FHE schemes are not suffi-cient for use in practice the security of some FHE is unsatisfying ...
Ph. Burtyka
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Reionization of Hydrogen and Helium by Early Stars and Quasars [PDF]
, 2002 We compute the reionization histories of hydrogen and helium due to the ionizing radiation fields produced by stars and quasars. For the quasars we use a model based on halo-merger rates that reproduces all known properties of the quasar luminosity ...
Abraham Loeb +2 more
core +2 more sources
Fully Homomorphically Encrypted Deep Learning as a Service
Machine Learning and Knowledge Extraction, 2021 Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both addition and multiplication, and thus the application of abelian/polynomial ...
George Onoufriou +2 more
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A Survey on Homomorphic Encryption Schemes: Theory and Implementation
, 2017 Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns.
Acar, Abbas +3 more
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Frontiers in Bioengineering and Biotechnology, 2023 Features of black phosphorous (BP) nano sheets such as enhancing mineralization and reducing cytotoxicity in bone regeneration field have been reported. Thermo-responsive FHE hydrogel (mainly composed of oxidized hyaluronic acid (OHA), poly-ε-L-lysine (ε-
Eunshinae Cho +6 more
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Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs [PDF]
, 2020 Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model.
Aung, Khin Mi Mi +8 more
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CHIMERA: Combining Ring-LWE-based Fully Homomorphic Encryption Schemes
Journal of Mathematical Cryptology, 2020 This paper proposes a practical hybrid solution for combining and switching between three popular Ring-LWE-based FHE schemes: TFHE, B/FV and HEAAN. This is achieved by first mapping the different plaintext spaces to a common algebraic structure and then ...
Boura Christina +3 more
doaj +1 more source