SQISign vs ML-DSA: The Post-Quantum Cryptographic Bottleneck in Smart Grid HSMs

By Rizowan Ahmed (@riz1raj)
Senior Technology Analyst | Covering Enterprise IT, Hardware & Emerging Trends

If you believe the transition to post-quantum cryptography (PQC) is as simple as swapping out software libraries, you have clearly never designed firmware for a smart grid utility pole. The NIST standardization of ML-DSA (FIPS 204) was celebrated by cloud architects who have the luxury of gigabytes of RAM and gigabit backplanes. For the embedded engineer working on utility-pole-mounted smart meters, distribution transformers, and remote terminal units (RTUs), FIPS 204 is a looming architectural challenge.

In the smart grid ecosystem, Hardware Security Modules (HSMs) and Secure Elements (SEs)—often running on modest 32-bit microcontrollers like the ARM Cortex-M4 or secure variants like the STMicroelectronics ST33—must sign and verify telemetry data under brutal constraints. We are talking about limited available SRAM, slow clock speeds to conserve power, and communication channels like Narrowband IoT (NB-IoT) or Power Line Communication (PLC) where every byte of overhead translates directly to packet fragmentation, transmission delays, and grid instability.

This technical analysis pits the primary lattice-based signature standard, ML-DSA, against the leading isogeny-based alternative, SQISign (Short Quaternion Isogeny Signature), specifically analyzing how they perform under the constraints of smart grid HSMs.

The Core Conflict: Packet Size vs. Compute Cycles

The fundamental trade-off between ML-DSA and SQISign is a classic engineering dilemma: Do you pay your tax in communication bandwidth or in CPU cycles?

Lattice-based schemes like ML-DSA are fast but require larger key and signature sizes. Isogeny-based schemes like SQISign are compact but computationally intensive. In a modern smart grid, where protocols like DLMS/COSEM (IEC 62056) or DNP3 Secure Authentication dictate strict timing windows and packet sizes, this trade-off determines whether your device can securely boot, authenticate, and report telemetry without causing network latency.

ML-DSA (FIPS 204) at a Glance

ML-DSA relies on the hardness of the Module Learning With Errors (M-LWE) problem. Its mathematical operations rely heavily on polynomial multiplication over a small prime field ($q = 8380417$). On an ARM Cortex-M4, these operations can be optimized using the Number Theoretic Transform (NTT). However, the resulting public keys and signatures are relatively large.

SQISign at a Glance

SQISign leverages the Deuring correspondence between supersingular elliptic curves and maximal orders in quaternion algebras. It is a highly space-efficient post-quantum signature scheme. Its signatures and public keys are comparable in size to classical elliptic curve schemes (ECDSA). However, the mathematical operations—evaluating isogenies between elliptic curves—require complex computations.

Signature Size and Network Fragmentation Benchmarks

To understand the impact of signature size on smart grid communications, we must look at the Maximum Transmission Unit (MTU) of typical grid communication layers. For example, G3-PLC networks operate with constrained MTUs. NB-IoT packets are ideally kept small to prevent IP-level fragmentation, which can increase packet loss rates in noisy electrical environments.

Let us look at the physical byte sizes for security level 1 (equivalent to AES-128 security):

• Classical ECDSA (P-256): Public Key: 64 bytes | Signature: 64 bytes | Total Overhead: 128 bytes
• SQISign (Level 1): Public Key: 64 bytes | Signature: 177 bytes | Total Overhead: 241 bytes
• ML-DSA-44 (Level 1): Public Key: 1,312 bytes | Signature: 2,420 bytes | Total Overhead: 3,732 bytes

The implications of these numbers are stark. An ML-DSA-44 signature and public key combination requires over 3.7 KB of data. Transmitting this over a constrained network requires fragmenting the cryptographic payload across multiple separate physical-layer packets. A single lost fragment triggers a retransmission of the entire sequence, degrading network throughput and introducing latency spikes.

Conversely, SQISign easily fits within a single physical packet. The total cryptographic overhead of 241 bytes leaves plenty of headroom for telemetry payloads within standard MTU limits, minimizing fragmentation overhead.

RAM Benchmarks: The Embedded Battleground

For a smart grid HSM, RAM is often a scarcer resource than flash memory. Many secure elements allocate limited transient SRAM for cryptographic operations to minimize physical die size and power consumption. If a cryptographic algorithm requires more RAM than is physically available, it must swap variables to non-volatile memory, which degrades write-cycle lifespans and introduces performance penalties.

ML-DSA-44 pushes the limits of typical low-power RAM budgets during the signing phase. To minimize the stack footprint, developers must use highly optimized in-place computations, which reuse the same memory buffers. This leaves minimal RAM for the rest of the HSM's operating system or network stack, forcing developers to implement complex memory management schemes.

SQISign is significantly more polite to the RAM footprint. Because the underlying curves require minimal coordinate storage, the stack during verification is particularly small, making SQISign suitable for low-cost smart meters that primarily perform signature verification of firmware updates pushed by the utility head-end.

The Catch: CPU Execution Cycles and Power Consumption

While SQISign wins the battle of transmission sizes and RAM footprints, it pays a significant price in execution speed. Evaluating isogeny paths requires complex modular arithmetic and isogeny evaluations.

On standard embedded microcontrollers, the performance differences are dramatic. ML-DSA-44 operations execute rapidly, whereas SQISign signing can introduce substantial latency. This execution delay can become a bottleneck if a smart meter needs to sign a rapid stream of events or authenticate real-time challenge-response protocols. Furthermore, running a microcontroller at full capacity for extended periods consumes more energy—a critical factor for battery-powered devices.

For more details on the structural tradeoffs of these algorithms, see our comprehensive guide on SQISign vs ML-DSA: Post-Quantum Cryptographic Migration for Resource-Constrained Hardware Security Modules.

Co-Processor Acceleration: The Savior of PQC?

To make either algorithm viable in production smart grid HSMs, hardware acceleration is becoming highly beneficial. However, the type of hardware acceleration required by each algorithm is vastly different.

Accelerating ML-DSA

ML-DSA benefits from hardware-accelerated Keccak engines (for SHAKE-128 and SHAKE-256 hashing, which consume a significant portion of ML-DSA's execution time) and vector-multiplication units. Modern secure elements are beginning to incorporate dedicated accelerators that can drop ML-DSA signing times and reduce RAM requirements by offloading intermediate polynomial states to dedicated hardware buffers.

Accelerating SQISign

SQISign requires a modular arithmetic coprocessor (MAP) capable of handling large-integer arithmetic and fast modular inversion. While traditional RSA/ECC coprocessors can be repurposed for basic arithmetic, the complex tree-traversal algorithms and isogeny evaluations must still be managed by the main CPU, limiting the effectiveness of naive hardware offloading.

The Verdict for Smart Grid Architects

There is no single solution for post-quantum smart grid migration. The choice between SQISign and ML-DSA is determined by your dominant system constraint:

Choose ML-DSA if: Your grid communication infrastructure is robust (e.g., fiber-to-the-substation, high-bandwidth cellular), and your primary requirement is high-throughput, low-latency transaction signing. ML-DSA is the standardized path, provided your HSM has sufficient RAM available and your network can handle larger payloads.

Choose SQISign if: You are operating over legacy, highly constrained communication channels where packet fragmentation is a major failure mode, and your primary security operation is firmware verification (which is computationally cheaper than signing). SQISign is a viable path to avoid complete physical network redesigns, but you must be prepared to accept longer execution times and invest in HSMs with robust modular arithmetic coprocessors.

As the industry transitions, we expect to see secure elements featuring hybrid hardware acceleration for both lattice-based and isogeny-based algorithms. Early adopters in the utility sector must begin prototyping with both families immediately; relying solely on one approach may leave resource-constrained edge devices physically incapable of communicating over the networks they are meant to protect.

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