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Pasta: A Case for Hybrid Homomorphic Encryption

IACR Cryptology ePrint Archive, 2023
The idea of hybrid homomorphic encryption (HHE) is to drastically reduce bandwidth requirements when using homomorphic encryption (HE) at the cost of more expensive computations in the encrypted domain.
Christoph Dobraunig, Lorenzo Grassi, Lukas Helminger, Christian Rechberger, Markus Schofnegger, Roman Walch   +5 more
openalex   +3 more sources

Experimental Demonstration of Quantum Fully Homomorphic Encryption with Application in a Two-Party Secure Protocol [PDF]

Physical Review X, 2020
A fully homomorphic encryption system hides data from unauthorized parties while still allowing them to perform computations on the encrypted data. Aside from the straightforward benefit of allowing users to delegate computations to a more powerful ...
W. K. Tham, Hugo Ferretti, Kent Bonsma-Fisher, Aharon Brodutch, Barry C. Sanders, Aephraim M. Steinberg, Stacey Jeffery   +6 more
doaj   +3 more sources

HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications. [PDF]

Sensors (Basel), 2023
The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data.
Ali A, Al-Rimy BAS, Alsubaei FS, Almazroi AA, Almazroi AA.   +4 more
europepmc   +2 more sources

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics. [PDF]

Sensors (Basel), 2023
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification.
Yang W, Wang S, Cui H, Tang Z, Li Y.
europepmc   +2 more sources

Stochastic switching and analog-state programmable memristor and its utilization for homomorphic encryption hardware. [PDF]

Nat Commun
Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW)
Cheong WH, In JH, Jeon JB, Kim G, Kim KM.   +4 more
europepmc   +2 more sources

A Survey on Homomorphic Encryption Schemes: Theory and Implementation [PDF]

ACM Computing Surveys, 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, Aksu, Hidayet, Conti, Mauro, Uluagac, A. Selcuk   +3 more
core   +2 more sources

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Future Internet, 2021
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated ...
Haokun Fang, Quan Qian
doaj   +2 more sources

Leveled Homomorphic Encryption Schemes for Homomorphic Encryption Standard

Journal of Mathematical Cryptology
Homomorphic encryption allows for computations on encrypted data without exposing the underlying plaintext, enabling secure and private data processing in various applications such as cloud computing and machine learning.
Shuhong Gao, Kyle Yates
semanticscholar   +2 more sources

FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption

Cybersecurity
Unprotected gradient exchange in federated learning (FL) systems may lead to gradient leakage-related attacks. CKKS is a promising approximate homomorphic encryption scheme to protect gradients, owing to its unique capability of performing operations ...
Yao Pan, Zheng Chao, Wang He, Yang Jing, Li Hongjia, Wang Liming   +5 more
doaj   +2 more sources

FedGraphHE: A privacy-preserving federated graph neural network framework with dynamic homomorphic encryption and robust aggregation. [PDF]

PLoS ONE
Federated learning (FL) enables collaborative model training across distributed intelligent devices while preserving data privacy. In smart healthcare networks, medical institutions can jointly learn from distributed patient data using graph neural ...
Aocheng Zuo, Zhanshen Feng, Yuan Ping, Shaohua Tao, Haonan Sun, Yange Chen   +5 more
doaj   +2 more sources