Results 21 to 30 of about 196,760 (255)

Hardware acceleration of number theoretic transform for zk‐SNARK

Engineering Reports, EarlyView., 2023
An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao, Dong Ding, Feng Wang, Pengcheng Hua, Ning Wang, Qin Wu, Zhilei Chai   +6 more
wiley   +1 more source

Experimental quantum homomorphic encryption [PDF]

npj Quantum Information, 2021
AbstractQuantum computers promise not only to outperform classical machines for certain important tasks, but also to preserve privacy of computation. For example, the blind quantum computing protocol enables secure delegated quantum computation, where a client can protect the privacy of their data and algorithms from a quantum server assigned to run ...
Zeuner, Jonas, Pitsios, Ioannis, Tan, Si-Hui, Sharma, Aditya N., Fitzsimons, Joseph F., Osellame, Roberto, Walther, Philip   +6 more
openaire   +5 more sources

NTRU-type fully homomorphic encryption scheme without key switching

网络与信息安全学报, 2017
In order to construct a fully homomorphic encryption scheme based on NTRU cryptosystem from ring learning with errors, noise growth and homomorphic property in the NTRU cryptosystem were analyzed. The con-cept of zero homomorphic encryption was introdced
Xin-xia SONG, Zhi-gang CHEN, Guo-min ZHOU   +2 more
doaj   +3 more sources

General Impossibility of Group Homomorphic Encryption in the Quantum World [PDF]

, 2014
Group homomorphic encryption represents one of the most important building blocks in modern cryptography. It forms the basis of widely-used, more sophisticated primitives, such as CCA2-secure encryption or secure multiparty computation.
Armknecht, Frederik, Gagliardoni, Tommaso, Katzenbeisser, Stefan, Peter, Andreas   +3 more
core   +10 more sources

Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network [PDF]

IEEE Access, 2021
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, Hyungchul Kang, Yongwoo Lee, W. Choi, Jieun Eom, M. Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, Jong-Seon No   +10 more
semanticscholar   +1 more source

User privacy prevention model using supervised federated learning‐based block chain approach for internet of Medical Things

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract This research focuses on addressing the privacy issues in healthcare advancement monitoring with the rapid establishment of the decentralised communication system in the Internet of Medical Things (IoMT). An integrated blockchain homomorphic encryption standard with an in‐build supervised learning‐based smart contract is designed to improvise ...
Chandramohan Dhasarathan, Mohammad Kamrul Hasan, Shayla Islam, Salwani Abdullah, Shailesh Khapre, Dalbir Singh, Abdulaziz A. Alsulami, Ali Alqahtani   +7 more
wiley   +1 more source

Research on privacy information retrieval model based on hybrid homomorphic encryption

Cybersecurity, 2023
The computational complexity of privacy information retrieval protocols is often linearly related to database size. When the database size is large, the efficiency of privacy information retrieval protocols is relatively low.
Wei-tao Song, Guang Zeng, Wen-zheng Zhang, Dian-hua Tang   +3 more
doaj   +1 more source

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Entropy, 2022
Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first
Rafik Hamza, Alzubair Hassan, Awad Ali, Mohammed Bakri Bashir, Samar M. Alqhtani, Tawfeeg Mohmmed Tawfeeg, Adil Yousif   +6 more
doaj   +1 more source

Privacy‐preserving federated learning based on multi‐key homomorphic encryption [PDF]

International Journal of Intelligent Systems, 2021
With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data.
Jing Ma, Si-Ahmed Naas, S. Sigg, X. Lyu
semanticscholar   +1 more source

Synonym‐based multi‐keyword ranked search with secure k‐NN in 6G network

IET Networks, EarlyView., 2022
Abstract Sixth Generation (6G) integrates the next generation communication systems such as maritime, terrestrial, and aerial to offer robust network and massive device connectivity with ultra‐low latency requirement. The cutting edge technologies such as artificial intelligence, quantum machine learning, and millimetre enable hyper‐connectivity to ...
Deebak Bakkiam David, Fadi Al‐Turjman
wiley   +1 more source