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arxiv: 2605.02276 · v1 · submitted 2026-05-04 · 💻 cs.CR · cs.PF

Recognition: 3 theorem links

· Lean Theorem

Post-Quantum Cryptography Migration in Australian Real-Time Payment Infrastructure: A Monte Carlo Simulation Study of the New Payments Platform

Nazmus Salehin Sammo
Authors on Pith no claims yet

Pith reviewed 2026-05-08 18:14 UTC · model grok-4.3

classification 💻 cs.CR cs.PF
keywords post-quantum cryptographyMonte Carlo simulationreal-time paymentsservice level agreementsHNDLqueueing modelsAustralian NPPquantum migration
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The pith

ML-DSA and Falcon post-quantum signatures meet Australia's 2,000 ms NPP payment SLA with under 2 ms added latency while SPHINCS+ saturates queues and fails entirely.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Australia's New Payments Platform processes millions of real-time transactions daily under a strict 2-second limit. This Monte Carlo study tests NIST post-quantum signature standards by simulating their effects on hardware security module queues, tail latencies, and harvest-now-decrypt-later exposure across 1,000 mixed-season days. ML-DSA and Falcon variants sustain full compliance in every configuration, with maximum p99 overhead of 1.57 ms, while SPHINCS+ overloads the system and produces zero compliance. The work also projects nearly 10 billion records at risk from future quantum computers and estimates peak migration costs of 21.4 million dollars in 2026.

Core claim

Through 80 million events in seasonally adjusted Monte Carlo runs on a multi-cloud testbed, the study shows ML-DSA and Falcon achieve 100 percent SLA compliance with p99 overhead below 2 ms and Crypto Dilution Index values under 0.04, identifies Falcon-512 as the only algorithm fitting the 2,048-byte SWIFT MT limit, demonstrates SPHINCS+ queue saturation at rho of 1.8855 yielding 0 percent compliance, and quantifies 9.56 billion NPP records at HNDL risk under a 2030 CRQC timeline with migration costs peaking at 21.4 million USD.

What carries the argument

The joint Monte Carlo simulation combining M/M/c queue saturation modeling for HSMs, generalized extreme value tail bounds, and an actuarial HNDL exposure model validated on liboqs across Intel, AMD, and ARM platforms.

Load-bearing premise

The M/M/c queue saturation, GEV tail bounds, and 1,000-day seasonally mixed Monte Carlo setup accurately represent real NPP hardware behavior, traffic patterns, and quantum computer timelines.

What would settle it

Direct measurement of HSM queue lengths and end-to-end latencies when running ML-DSA, Falcon, or SPHINCS+ at full NPP daily volume on production hardware, checking whether p99 stays under 2 seconds or queues saturate as modeled.

Figures

Figures reproduced from arXiv: 2605.02276 by Nazmus Salehin Sammo.

Figure 1
Figure 1. Figure 1: Simulated daily transaction volume profile (Gaussian mixture, 6 components) view at source ↗
Figure 2
Figure 2. Figure 2: NPP SLA compliance distribution across 1,000 Monte Carlo days per algorithm view at source ↗
Figure 3
Figure 3. Figure 3: End-to-end NPP p99 latency distribution (violin plot) across 1,000 Monte Carlo view at source ↗
Figure 4
Figure 4. Figure 4: 95% confidence intervals on the mean of daily NPP p99 latency values across view at source ↗
Figure 5
Figure 5. Figure 5: Cohen’s d effect sizes for each PQC algorithm vs ECDSA-P256 baseline. Values view at source ↗
Figure 6
Figure 6. Figure 6: M/M/c queue utilisation (ρ) and mean wait time for each algorithm at Big 4 bank average daily load (13.5 TPS, c = 2 servers). SPHINCS+ bar extends to ρ = 1.8855, off the stable region. All ML-DSA and Falcon variants have utilisation ρ < 0.003, meaning the two-server pool is essentially idle, each algorithm requires less than 0.3% of one server’s capacity at peak NPP load. SPHINCS+ requires c ≥ 8 servers pe… view at source ↗
Figure 7
Figure 7. Figure 7: TPS sweep analysis, queue utilisation ρ across institution TPS load levels for each algorithm (c=2 servers). SPHINCS+ crosses the ρ=1.0 saturation threshold at approximately 7.2 TPS/institution (theoretical threshold λ = c·µ = 2 × 3.58 = 7.16 TPS). All ML-DSA and Falcon variants remain below ρ=0.01 across the full sweep. 4.4.1 SPHINCS+ Queue Saturation as a DoS Amplification Vector The queue saturation dyn… view at source ↗
Figure 8
Figure 8. Figure 8: Key and signature sizes for each algorithm against SWIFT MT 2,048 B limit view at source ↗
Figure 9
Figure 9. Figure 9: NPP SLA compliance across five stress scenarios. All ML-DSA and Falcon view at source ↗
Figure 10
Figure 10. Figure 10: p99 latency under three HSM deployment architectures. Even network-attached view at source ↗
Figure 11
Figure 11. Figure 11: Projected NPP p99 latency at 15.6% YoY volume growth (2026–2029). All view at source ↗
Figure 12
Figure 12. Figure 12: Four-Phase PQC Migration Cost Model (2025–2028 and beyond). Phase 1 view at source ↗
Figure 13
Figure 13. Figure 13: GEV-fitted p99, p99.9, and p99.99 quantiles for non-SPHINCS+ algorithms. view at source ↗
Figure 14
Figure 14. Figure 14: 24-hour M/M/c queue utilisation profile under Christmas scenario (c=2 view at source ↗
Figure 16
Figure 16. Figure 16: Algorithm recommendation matrix scoring each candidate across six operational view at source ↗
Figure 17
Figure 17. Figure 17: Algorithm regulatory compliance heatmap across APRA CPS 234, RBA NPP view at source ↗
read the original abstract

Australia's New Payments Platform (NPP) processes 5.2 million real-time transactions per day under a 2,000 ms SLA. With cryptographically relevant quantum computers projected by 2030-2035 and the Harvest Now, Decrypt Later (HNDL) threat active, this paper presents a Monte Carlo simulation study of NIST FIPS 204/205/206 signature standards (ML-DSA, SLH-DSA/SPHINCS+, Falcon) in Australian payment infrastructure, jointly modelling M/M/c queue saturation, GEV tail bounds, and HNDL actuarial exposure across 1,000 seasonally-mixed simulation days (80 million events). Cross-platform validation used liboqs 0.15.0 on a seven-node multi-cloud testbed spanning four microarchitectures (Intel Xeon Ice Lake/Cascade Lake, AMD EPYC Milan, ARM Graviton3). ML-DSA and Falcon achieve 100% SLA compliance across all configurations; worst-case NPP p99 overhead is 1.57 ms (ML-DSA-87, 0.079% of SLA budget). We introduce the Crypto Dilution Index (CDI = delta-p99/p99_e2e), showing all non-SPHINCS+ algorithms achieve CDI < 0.04. GEV analysis yields p99.9 bounds below 154 ms (95% CI). Falcon-512 is the only NIST PQC signature fitting within the 2,048-byte SWIFT MT field limit (1,563 bytes combined). SPHINCS+ saturates HSM queues at NPP volumes (rho=1.8855, c=2 servers), achieving 0% NPP SLA compliance, characterised as a DoS amplification surface in hybrid deployments (utilisation ratio ~9,428x ECDSA). An HNDL actuarial model estimates 9.56 billion NPP records at risk under CRQC-2030. Migration costs peak at USD 21.4M in 2026, declining to USD 1.5M/year by 2028.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports a Monte Carlo simulation study of migrating Australia's New Payments Platform (NPP, 5.2M daily transactions, 2000 ms SLA) to NIST PQC signatures (ML-DSA, SPHINCS+, Falcon). It jointly models M/M/c queue saturation, GEV tail bounds, and HNDL actuarial exposure over 1000 seasonally-mixed days (80M events), with liboqs cross-validation on a seven-node multi-cloud testbed. Central claims include 100% SLA compliance and p99 overhead of 1.57 ms for ML-DSA/Falcon (CDI < 0.04), 0% compliance for SPHINCS+ due to saturation (rho=1.8855, c=2), Falcon-512 fitting the 2048-byte SWIFT limit, 9.56B records at HNDL risk under CRQC-2030, and migration costs peaking at USD 21.4M in 2026.

Significance. If the distributional assumptions hold, the work supplies quantitative performance and risk metrics for PQC adoption in high-volume real-time payment systems, including a novel Crypto Dilution Index and actuarial HNDL projections. Strengths include the multi-architecture liboqs validation and the scale of the event-driven simulation. The results could inform infrastructure planning for quantum-safe transitions in financial networks.

major comments (2)
  1. [§4 (Queueing Model)] §4 (Queueing Model): The 0% SLA compliance and DoS-amplification characterization for SPHINCS+ rest on the M/M/c derivation yielding rho=1.8855 (c=2 servers) and the associated utilization ratio of ~9428x ECDSA; the model assumes Poisson arrivals and exponential service times, yet the manuscript provides no empirical calibration or sensitivity checks against documented NPP traffic features (diurnal bursts, batching, heavy tails) or measured HSM latency distributions (often closer to deterministic or log-normal).
  2. [§5.1 (GEV Analysis)] §5.1 (GEV Analysis): The p99.9 bounds <154 ms (95% CI), p99 overhead of 1.57 ms, and CDI values are obtained from GEV fitting to the 80M simulated events generated by the seasonally-mixed 1000-day process; the paper does not report the fitting procedure, shape/scale parameter estimates, goodness-of-fit diagnostics, or robustness tests under alternative generators, leaving the headline compliance figures dependent on unvalidated tail assumptions.
minor comments (2)
  1. [Abstract] The abstract introduces the Crypto Dilution Index (CDI) without a one-sentence definition; a brief parenthetical would improve standalone readability.
  2. [Results tables] Table captions and axis labels should explicitly state the number of Monte Carlo replications and the exact liboqs version used for each latency measurement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has helped us identify opportunities to strengthen the transparency of our modeling sections. We address each major comment point by point below and have prepared corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: §4 (Queueing Model): The 0% SLA compliance and DoS-amplification characterization for SPHINCS+ rest on the M/M/c derivation yielding rho=1.8855 (c=2 servers) and the associated utilization ratio of ~9428x ECDSA; the model assumes Poisson arrivals and exponential service times, yet the manuscript provides no empirical calibration or sensitivity checks against documented NPP traffic features (diurnal bursts, batching, heavy tails) or measured HSM latency distributions (often closer to deterministic or log-normal).

    Authors: We acknowledge that the M/M/c analysis in §4 relies on standard Poisson arrival and exponential service assumptions for tractability and does not include explicit sensitivity checks against NPP-specific features such as diurnal patterns or alternative HSM latency distributions. Our liboqs testbed measurements informed the service time parameters, and the seasonally-mixed simulation already incorporates some daily variation, but we did not report robustness tests under log-normal or deterministic alternatives in the submitted version. In the revision we will expand §4 with a new paragraph on model limitations and add sensitivity results using log-normal service times fitted to our testbed data; these confirm that SPHINCS+ queue saturation (rho > 1) and the associated DoS amplification characterization remain robust under heavier-tailed distributions. revision: yes

  2. Referee: §5.1 (GEV Analysis): The p99.9 bounds <154 ms (95% CI), p99 overhead of 1.57 ms, and CDI values are obtained from GEV fitting to the 80M simulated events generated by the seasonally-mixed 1000-day process; the paper does not report the fitting procedure, shape/scale parameter estimates, goodness-of-fit diagnostics, or robustness tests under alternative generators, leaving the headline compliance figures dependent on unvalidated tail assumptions.

    Authors: We agree that §5.1 would be strengthened by explicit reporting of the GEV fitting methodology. The submitted manuscript omitted these details for brevity. We will revise the section to document the maximum-likelihood fitting procedure, the estimated shape (ξ), scale (σ), and location (μ) parameters with standard errors, goodness-of-fit diagnostics (QQ-plots, Kolmogorov-Smirnov and Anderson-Darling statistics), and robustness checks via bootstrap resampling and comparison to Gumbel and log-normal alternatives. These additions will directly support the reported p99.9 bounds, p99 overhead, and CDI values. revision: yes

Circularity Check

0 steps flagged

Monte Carlo simulation driven by external NIST standards, liboqs benchmarks, and published queueing/GEV theory

full rationale

The paper's core results (SLA compliance percentages, p99 overheads, CDI values, queue saturation rho, GEV tail bounds, HNDL exposure estimates, and migration cost projections) are generated by forward simulation of 80 million events over 1,000 days. Inputs are taken from NIST FIPS 204/205/206 specifications, direct liboqs 0.15.0 measurements on external multi-cloud hardware, standard M/M/c formulas, and published GEV theory; no equation or reported figure is shown to equal a parameter fitted from the same run's outputs or to rest on a self-citation chain that itself lacks independent verification. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 2 invented entities

Claims rest on standard M/M/c queueing and GEV distributions plus a newly defined CDI and custom HNDL actuarial model; no independent evidence or machine-checked validation is supplied for the parameter choices or projections.

free parameters (2)
  • simulation_days
    1,000 seasonally-mixed days chosen to generate 80 million events
  • NPP_daily_volume
    5.2 million transactions per day taken as fixed input
axioms (2)
  • domain assumption M/M/c queue model accurately captures HSM saturation at NPP load
    Invoked to derive rho=1.8855 and 0% SLA compliance for SPHINCS+
  • domain assumption GEV distribution provides valid p99.9 tail bounds for latency
    Used to claim bounds below 154 ms with 95% CI
invented entities (2)
  • Crypto Dilution Index (CDI) no independent evidence
    purpose: Quantify p99 overhead relative to end-to-end SLA budget
    Newly introduced metric showing CDI < 0.04 for non-SPHINCS+ algorithms
  • HNDL actuarial exposure model no independent evidence
    purpose: Estimate number of NPP records vulnerable to future CRQC
    Produces 9.56 billion records at risk under 2030 projection

pith-pipeline@v0.9.0 · 5691 in / 1745 out tokens · 33939 ms · 2026-05-08T18:14:39.842969+00:00 · methodology

discussion (0)

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Reference graph

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