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Efficient AGCD-Based Homomorphic Encryption for Matrix and Vector Arithmetic
Lima Pereira, Hilder Vitor
2020In Applied Cryptography and Network Security
Peer reviewed
 

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Keywords :
Homomorphic encryption; AGCD; Nondeterministic finite automata; Naïve Bayes Classifier
Abstract :
[en] We propose a leveled homomorphic encryption scheme based on the Approximate Greatest Common Divisor (AGCD) problem that operates natively on vectors and matrices. To overcome the limitation of large ciphertext expansion that is typical in AGCD-based schemes, we randomize the ciphertexts with a hidden matrix, which allows us to choose smaller parameters. To be able to efficiently evaluate circuits with large multiplicative depth, we use a decomposition technique à la GSW. The running times and ciphertext sizes are practical: for instance, for 100 bits of security, we can perform a sequence of 128 homomorphic products between 128-dimensional vectors and 128×128 matrices in less than one second. We show how to use our scheme to homomorphically evaluate nondeterministic finite automata and also a Naïve Bayes Classifier.
Disciplines :
Computer science
Author, co-author :
Lima Pereira, Hilder Vitor ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
no
Language :
English
Title :
Efficient AGCD-Based Homomorphic Encryption for Matrix and Vector Arithmetic
Publication date :
August 2020
Event name :
18th International Conference on Applied Cryptography and Network Security Search within this conference
Event place :
Rome, Italy
Event date :
from 19-10-2020 to 2210-2020
Audience :
International
Main work title :
Applied Cryptography and Network Security
Publisher :
Springer International Publishing
ISBN/EAN :
978-3-030-57808-4
Pages :
110--129
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Available on ORBilu :
since 14 December 2020

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