.png){kind=icon}
.png){kind=icon}
Return
APAS: Application-Specific Accelerators for RLWE-Based Homomorphic Linear Transformations
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2021.3114032.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, the application of multi-party secure computing schemes based on homomorphic encryption in the field of machine learning attracts attentions across the research fields. Previous studies have demonstrated that secure protocols adopting packed additive homomorphic encryption (PAHE) schemes based on the ring learning with errors (RLWE) problem exhibit significant practical merits, and are particularly promising in enabling efficient secure inference in machine-learning-as-a-service applications. In this work, we introduce a new technique for performing homomorphic linear transformation (HLT) over PAHE ciphertexts. Using the proposed HLT technique, homomorphic convolutions and inner products can be executed without the use of number theoretic transform and the rotate-and-add algorithms that were proposed in existing works. To maximize the efficiency of the HLT technique, we propose APAS, a hardware-software co-design framework consisting of approximate arithmetic units for the hardware acceleration of HLT. In the experiments, we use actual neural network architectures as benchmarks to show that APAS can improve the computational and communicational efficiency of homomorphic convolution by 8x and 3x, respectively, with an energy reduction of up to 26x as compared to the ASIC implementations of existing methods.
Keywords:
Cryptography
Protocols
Computer architecture
Transforms
Standards
Libraries
Lattices
Homomorphic encryption
ring learning with errors
secure inference
neural networks
homomorphic linear transformation
number theoretic transform
Journal
IF:
8
Papers:
5.2K
Citations:
2.3W