Cryptology ePrint Archive: Report 2020/491

Efficient AGCD-based homomorphic encryption for matrix and vector arithmetic

Hilder Vitor Lima Pereira

Abstract: 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 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 Nave Bayes Classifier. We also present a generalization of the GCD attacks against the some variants of the AGCD problem.

Category / Keywords: Homomorphic Encryption; AGCD; Nave Bayes Classifier; Nondeterministic finite automata.

Original Publication (with major differences): 18th International Conference on Applied Cryptography and Network Security (ACNS 2020)

Date: received 27 Apr 2020

Contact author: hilder vitor at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20200428:121733 (All versions of this report)

Short URL: ia.cr/2020/491


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