Benchmarking Post-Quantum Cryptography in Lightweight Virtualization Environments on Embedded Hardware
Nikolai Puch, Chi Hieu Ta, Moritz Beckel
Abstract
Post-Quantum Cryptography (PQC) is being deployed while embedded systems increasingly adopt lightweight virtualization for workload isolation and security. Both trends change performance characteristics, yet their interaction is not well understood. To address this, we present a measurement study of PQC primitives on embedded-class ARM hardware under three execution environments with a shared software stack: native execution, a Docker container, and a Unikraft unikernel running under QEMU. We benchmark five signature and five key encapsulation mechanism families, and, for comparison, two classical algorithms each. We evaluate them using different parameter sets for a total of around 70 configurations, measuring execution time, memory, and energy per operation. To better gauge the impact on applications, we evaluated TLS 1.3 cipher combinations. We find that container overhead is negligible for primitive computation, whereas unikernel overhead depends on the algorithm. For most PQC families the overhead is negligible. A moderate overhead (1.28-1.53) arises in BIKE, HQC, and MAYO, and, above all, in Falcon signing (17.8-19.2). Per-operation energy closely tracks execution time in all environments. For TLS handshakes, container and unikernel clients need more time and energy per handshake, while all three environments converge once expensive post-quantum algorithms dominate the handshake. In these cases algorithm choice affects per-handshake energy by up to three orders of magnitude, far outweighing the environment. Overall, virtualization cost is inversely related to cryptographic cost: environment choice matters most for computationally cheap, standardized algorithms, while for expensive schemes, algorithm choice alone dominates performance.
Create a lesson
Related papers
MobileCybench: Evaluating Agent Vulnerability Discovery via Executable Probes
Andy K. Zhang, Ava Huang, Joey Ji et al.
Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges
Heewon Baek, Alsharif Abuadbba, Kristen Moore et al.
SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses
Xingyu Li, Juefei Pu, Haonan Li et al.
Pattern-level Differential Privacy for High-utility Complex Event Processing
He Gu, Thomas Plagemann, Vera Goebel et al.
TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary
When the Agent Becomes the Kernel: A Systematization of Security on the Path to AI-Native Operating Systems
Li Zhang, Yang Sun, Jie Shi