Year: 2024
Type: conference-paper
Abstract: Homomorphic encryption enables computations on the ciphertext to preserve data privacy. However, its practical deployment has been hindered by the significant computational overhead compared to the plaintext computations. In response to this challenge, we present HERMES, a novel hardware acceleration system designed to explore the computation flow of the CKKS homomorphic encryption bootstrapping process. Among the major contributions of our proposed architecture, we first analyze the properties of the CKKS computation data flow and propose a new scheduling strategy by partitioning the computation modules into general-purpose and special-purpose modular computation modules to allow smaller resource consumption and flexible scheduling. The computation modules are also reconfigurable to reduce the memory access overhead during the intermediate computation. We also optimize the CKKS computation dataflow to improve the regularity with reduced control overhead.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
Awards: CAREER: Protecting Deep Learning Systems against Hardware-Oriented Vulnerabilities, Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption, Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
OpenAlex ID: W4409285502