Parameter Impact Explorer
Explore how changing algorithm parameters affects security level, key and signature sizes, and performance tradeoffs. Adjust the sliders to see real-time impact visualizations for each post-quantum algorithm.
Default Parameter Comparison
| Metric | ML-KEM | ML-DSA | SLH-DSA |
|---|---|---|---|
| Security | ~16 bits | ~13 bits | ~32 bits |
| Public Key | 30 B | 30 B | 32 B |
| Secret Key | 10 B | 20 B | 64 B |
| Output | 15 B | 40 B | 256 B |
ML-KEM
Module-Lattice Key Encapsulation · FIPS 203
Parameters
Size Impact
A matrix + t vector
Secret vector s
Encrypted shared secret
Performance Tradeoffs
How it works
With N=8 (polynomial degree), Q=17 (modulus), K=2 (module dimension): each polynomial has 8 coefficients, each needing 5 bits. The module dimension K=2 determines how many polynomials form the secret/public vectors, directly scaling key sizes and security. Larger Q increases coefficient precision but also sizes.
Understanding Parameter Tradeoffs
Security vs Size
Increasing security parameters (N, K, Q) makes cryptographic attacks harder but produces larger keys and ciphertexts/signatures. Finding the right balance is crucial for practical deployment.
Speed vs Security
Larger parameters require more computation. ML-DSA's rejection sampling with tight bounds (small \u03b3\u2212\u03b2) increases security but may require many signing attempts. SLH-DSA trades speed for minimal trust assumptions.
Lattice vs Hash-Based
ML-KEM and ML-DSA rely on lattice problems (MLWE/MSIS) for compact keys. SLH-DSA relies only on hash function security — tiny keys but larger signatures. Different assumptions hedge against different quantum attack vectors.