The quantum shift is on. Security teams, embedded engineers and hardware vendors are now busy migrating systems to PQC, and top of the integration list is likely to be ML-DSA. Standardized by NIST as FIPS 204, ML-DSA is the cornerstone algorithm for quantum-safe digital signatures. From automotive control systems to aerospace electronics, ML-DSA is likely to be found everywhere, helping digital systems authenticate and prove the integrity of communications and files.
However, benchmarking ML-DSA comes with a major catch. Unlike the classical primitives it replaces, ML-DSA uses a technique called ‘rejection-sampling’, which in turn leads to timing variability. As a result, applying old techniques to benchmark the performance of ML-DSA (for example, minimum, average, or maximum execution times) will not produce reliable results. It’s an important consideration, as safety-critical systems and high-network environments rely on accurate measurements. A signing operation that takes 20 times longer than the average, for example, could easily lead to catastrophe.
In a new plain-english white paper (based on joint research by PQShield and the University of Bundeswehr Munich) we break down the core challenges:
Download the white paper
Read the original research
Link to Press Release
However, benchmarking ML-DSA comes with a major catch. Unlike the classical primitives it replaces, ML-DSA uses a technique called ‘rejection-sampling’, which in turn leads to timing variability. As a result, applying old techniques to benchmark the performance of ML-DSA (for example, minimum, average, or maximum execution times) will not produce reliable results. It’s an important consideration, as safety-critical systems and high-network environments rely on accurate measurements. A signing operation that takes 20 times longer than the average, for example, could easily lead to catastrophe.
In a new plain-english white paper (based on joint research by PQShield and the University of Bundeswehr Munich) we break down the core challenges:
- - The 10 Benchmarking Pitfalls – from ambiguous measurement boundaries to misleading derterministic assumptions
- - A standardized methodology – how to use dataset-driven testing to accurately evaluate PQC implementations
- - Real-world hardware results – featuring the first publicly verifiable worst-case execution time benchmarks on real-world embedded processors
Download the white paper
Read the original research
Link to Press Release