Efficient AGCD-Based Homomorphic Encryption for Matrix and Vector Arithmetic
| Autor: | Hilder V. L. Pereira |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Sequence Computer science 05 social sciences Multiplicative function Homomorphic encryption 02 engineering and technology Matrix (mathematics) Ciphertext 0202 electrical engineering electronic engineering information engineering Greatest common divisor 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Nondeterministic finite automaton Arithmetic Ciphertext expansion Computer Science::Cryptography and Security |
| Zdroj: | Applied Cryptography and Network Security ISBN: 9783030578077 ACNS (1) Lecture Notes in Computer Science Lecture Notes in Computer Science-Applied Cryptography and Network Security |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | 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 a 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 \(128\times 128\) matrices in less than one second. We show how to use our scheme to homomorphically evaluate nondeterministic finite automata and also a Naive Bayes Classifier. |
| Databáze: | OpenAIRE |
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