Precomputation Methods for Faster and Greener Post-Quantum Cryptography on Emerging Embedded Platforms

Precomputation techniques are useful to improve realtime performance of complex algorithms at the expense of extra memory, and extra preparatory computations. This practice is neglected especially in the embedded context where energy and memory space is limited. Instead, the embedded space favors the immediate reduction of energy and memory footprint. However, the embedded platforms of the future may be different from the traditional ones. Energy-harvesting sensor nodes may extract virtually limitless energy from their surrounding, while at the same time they are able to store more data at cheaper cost, thanks to Moore’s law. Yet, minimizing the run-time energy and latency will still be primary targets for today’s as well as future real-time embedded systems. Another important challenge for the future systems is to provide efficient public-key based solutions that can thwart quantum-cryptanalysis. In this article, we address these two concepts. We apply precomputation techniques on two post-quantum digital signature schemes: hash-based and lattice-based digital signatures. We first demonstrate that precomputation methods are extensible to post-quantum cryptography and are applicable on current energy-harvesting platforms. Then, we quantify its impact on energy, execution time, and the overall system yield. The results show that precomputation can improve the run-time latency and energy consumption up to a factor of 82.7× and 11.8×, respectively. Moreover, for a typical energy-harvesting profile, it can triple the total number of generated signatures. We reveal that precomputation enables very complex and even probabilistic algorithms to achieve acceptable real-time performance on resource-constrained platforms. Thus, it will expand the scope of post-quantum algorithms to a broader range of platforms and applications.