Lattice-based Cryptography for IoT in A Quantum World: Are We Ready?

Abstract

The impending realization of scalable quantum computers has led to active research in Post Quantum Cryptography (PQC). The challenge is harder for embedded IoT (edge) devices, due to their pervasive diffusion in today's world as well as their stricter resources (tight area and energy budgets). Amongst various classes of quantum-resistant cryptography schemes, Latticebased Cryptography (LBC) is emerging as one of the most viable, almost half of the 'survivors' of second round of the NIST's PQC competition are lattice-based in construction. This paper surveys the practicality of deployment of these schemes. In this context, the state-of-the-art LBC implementations on the constrained devices (including low-power FPGAs and embedded microprocessors), leading in terms of low-power footprint, small area, compact bandwidth requirements and high performance is fairly evaluated and bench-marked. The work concludes by identifying a suite of some favorite LBC schemes in terms of various IoT critical performance benchmarks .

Post-Quantum Cryptosystems for Internet-of-Things: A Survey on Lattice-Based Algorithms

“…The emergence of new edge computing platforms, such as cloud computing, softwaredefined networks, and the Internet-of-Things (IoT), calls for the adoption of an increasing number of security frameworks, which in turn require the introduction of a variety of primitive cryptographic elements, but the security is just one vector in the IoT world [127]. It is also necessary to implement those secure frameworks that consume less on-board processing, memory and power resources [128]. This presents enormous difficulties in the design and execution of new cryptographic principles in a single embodiment, as diverging priorities and restrictions are accurate for the computing platforms.…”

“…Recently, many researchers are investigating Lightweight Lattice-Based Cryptography (LW-LBC) [128,132], where performance evaluation is fairly measured and benchmarked in terms of low-power footprint, narrow area, lightweight bandwidth requirements and good performance. The main characteristics of post-quantum LBC that makes them well suited for IoT world are: (a) these schemes offer security proofs based on NP-hard problems with average-case to worst-case hardness; (b) secondly, the LBC implementations are noteworthy for their efficiency in addition to being quantum-age stable, largely due to their inherent linear algebra-based matrix/vector operations on integers; and, (c) third, for specialized security, LBC buildings offer expanded features, in addition to the simple classical cryptographic primitives (encryption, signatures, key exchange solutions required in a quantum era, services, such as identity-based encryption (IBE) [133], attribute-based encryption (ABE) [11], and fully homomorphic encryption (FHE)) [134].…”

“…Figure 4 depicts the communication bandwidth by calculating the data bytes of various LBC algorithms with sk, pk, and signature variants, as comprehensively analyzed in Reference [128], while the number manifested at the end of each algorithm is the level of security achieved according to the NIST standards. These security levels can be defined as: (a) Level 1: at least as hard to break as AES-128 (exhaustive key search), (b) Level 2: at least as hard to break as SHA-256 (collision search), (c) Level 3: at least as hard to break as AES-192 (exhaustive key search), (d) Level 4: at least as hard to break as SHA-384 (collision search), and (e) Level 5: at least as hard to break as AES-256 (exhaustive key search).…”

“…Figure 5 depicts the communication bandwidths of LBC algorithms implemented with public key encryption (PKE) or with Key encapsulation mechanisms (KEM) schemes [128,135,136]. It can be seen from the results that Saber and ThreeBears variants both consume less bandwidth at diverse NIST security levels and can be considered as the suitable candidates for lightweight implementation of LBC in the IoT networks.…”

“…In this section, we summarize the practical hardware implementation of LBC by comparing the memory usage (bytes), computational time (ms) and clock cycle counts on an ARM CORTEX-M AT 168 MHz platform [128]. Table 3 depicts the hardware complexity of implementing LBC based on KEMs [143].…”