Paper 2024/1089
Juliet: A Configurable Processor for Computing on Encrypted Data
Abstract
Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet's capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers Simon and Speck, as well as logistic regression (LR) inference and matrix multiplication.
Note: The Juliet framework is open-sourced at https://github.com/TrustworthyComputing/Juliet, and the HEJava compiler is available at https://github.com/TrustworthyComputing/HEJava-compiler. This article is published in IEEE Transactions on Computers; the IEEE version is available at https://doi.org/10.1109/TC.2024.3416752.
Metadata
- Available format(s)
- Category
- Applications
- Publication info
- Published elsewhere. IEEE Transactions on Computers
- DOI
- 10.1109/TC.2024.3416752
- Keywords
- Encrypted ProcessorHardware AccelerationHomomorphic EncryptionPrivacy-Preserving ISAPrivate Computation
- Contact author(s)
-
cgouert @ udel edu
jimouris @ udel edu
tsoutsos @ udel edu - History
- 2024-07-05: approved
- 2024-07-04: received
- See all versions
- Short URL
- https://ia.cr/2024/1089
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/1089, author = {Charles Gouert and Dimitris Mouris and Nektarios Georgios Tsoutsos}, title = {Juliet: A Configurable Processor for Computing on Encrypted Data}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/1089}, year = {2024}, doi = {10.1109/TC.2024.3416752}, url = {https://eprint.iacr.org/2024/1089} }