Fault Detection Architectures for Inverted Binary Ring-LWE Construction Benchmarked on FPGA
| Autor: | Reza Azarderakhsh, Mehran Mozaffari Kermani, Ausmita Sarker |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Lattice problem 02 engineering and technology Encryption Fault detection and isolation 020202 computer hardware & architecture Reduction (complexity) Computer Science::Hardware Architecture Computer engineering 0202 electrical engineering electronic engineering information engineering Overhead (computing) 020201 artificial intelligence & image processing Electrical and Electronic Engineering Fault model Field-programmable gate array business Computer Science::Operating Systems Learning with errors |
| Zdroj: | IEEE Transactions on Circuits and Systems II: Express Briefs. 68:1403-1407 |
| ISSN: | 1558-3791 1549-7747 |
| DOI: | 10.1109/tcsii.2020.3025857 |
| Popis: | Ring learning with errors (RLWE) is an efficient lattice-based cryptographic scheme that has worst-case reduction to lattice problem, conjectured to be quantum-hard. Ring-BinLWE is an optimized variant of RLWE problem using binary error distribution, resulting in highly-efficient hardware implementation. Efficient and low-complexity architectures in hardware, thwarting natural and malicious faults, are essential for lattice-based post-quantum cryptography (PQC) algorithms. In this brief, we explore efficient fault detection approaches for implementing the Ring-BinLWE problem. This brief, for the first time, investigates fault detection schemes for all three stages of RLWE encryption. Utilizing the stuck-at fault model, we employ recomputing with encoded operands schemes to achieve high error coverage. We simulate and implement our schemes on a field-programmable gate array (FPGA) platform. Our schemes provide low hardware overhead (area overhead of 15.74%, delay overhead of 7.74%, and power consumption overhead of 4.06%), with high error coverage, which can be suitable for resource-constrained as well as high-performance usage models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|