Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme

Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme
Title:
Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme
Other Titles:
IEEE Transactions on Emerging Topics in Computing
Publication Date:
04 March 2019
Citation:
A. Qaisar Ahmad Al Badawi, Y. Polyakov, K. M. M. Aung, B. Veeravalli and K. Rohloff, "Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme," in IEEE Transactions on Emerging Topics in Computing. doi: 10.1109/TETC.2019.2902799
Abstract:
Homomorphic encryption is an emerging form of encryption that provides the ability to compute on encrypted data without ever decrypting them. Potential applications include aggregating sensitive encrypted data on a cloud environment and computing on the data in the cloud without compromising data privacy. There have been several recent advances resulting in new homomorphic encryption schemes and optimized variants. We implement and evaluate the performance of two optimized variants, namely Bajard-Eynard-Hasan-Zucca (BEHZ) and Halevi-Polyakov-Shoup (HPS), of the most promising homomorphic encryption scheme in CPU and GPU. The most interesting (and also unexpected) result of our performance evaluation is that the HPS variant in practice scales significantly better (typically by 15%-30%) with increase in multiplicative depth of the computation circuit than BEHZ, implying that the HPS variant will always outperform BEHZ for most practical applications. For the multiplicative depth of 98, our fastest GPU implementation performs homomorphic multiplication in 51 ms for 128-bit security settings, which is faster by two orders of magnitude than prior results and already practical for cloud environments supporting GPU computations. Large multiplicative depths supported by our implementations are required for applications involving deep neural networks, logistic regression learning, and other important machine learning problems.
License type:
PublisherCopyrights
Funding Info:
Description:
(C) 2019 IEEE.
ISSN:
2168-6750
2376-4562
Files uploaded:
File Size Format Action
There are no attached files.