Assess quantum risks, implement hybrid key exchange, and plan a defensible migration to post-quantum standards
Distinguish which public-key, symmetric, and hashing primitives a cryptographically relevant quantum computer threatens in a concrete system.
Analyze harvest-now-decrypt-later exposure by weighing data lifetime, capture feasibility, and protocol exposure.
Explain how learning with errors, module lattices, noise, and parameter choices shape post-quantum security assumptions.
Compare ML-KEM, ML-DSA, and SLH-DSA by security goals, sizes, verification cost, operational constraints, and use cases.
Implement a classical and ML-KEM key agreement with correct session-key derivation, transcript binding, and downgrade resistance.
Evaluate when to retain hybrid protection, adopt pure post-quantum cryptography, or defer using inventory, agility, rollout, monitoring, and validation criteria.
3 sections · 8 lessons
Define the quantum threat to public-key systems, trace why RSA and ECC fail, and prioritize migration targets exposed to harvest-now-decrypt-later attacks.
3 lessonsBuild intuition for module-lattice assumptions, then compare the mechanics, failure modes, and operational tradeoffs of the principal NIST post-quantum standards.
3 lessonsImplement and review hybrid key exchange with secure session-key derivation, then design migration decisions spanning crypto inventory, agility, rollout, monitoring, and validation.
2 lessonsA cryptographically relevant quantum computer would break widely deployed RSA and elliptic-curve protections while leaving symmetric encryption and hashes largely intact. This course develops an advanced understanding of that threat, from Shor’s algorithm and harvest-now-decrypt-later exposure to lattice-based standards and implementation tradeoffs. You will leave with the technical judgment to evaluate systems, build hybrid key exchange, and shape a practical migration strategy.
The course covers quantum threats to public-key cryptography, Shor’s algorithm, harvest-now-decrypt-later prioritization, lattice assumptions, and the mechanics and tradeoffs of ML-KEM, ML-DSA, and SLH-DSA. It also covers coded hybrid key exchange, session-key derivation, and migration planning.
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Average pass rate · Freeman et al., PNAS 2014
What the science says
Students in lecture-only classes are 1.5× more likely to fail.
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You work through itAI does it for you
Skill retention over time · with vs. without practice
What the science says
Offloading thinking to AI erodes the very skills you’re trying to build.
How Fenzo does it
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Retrieval practiceRe-reading
Long-term recall · retrieval vs. re-reading
What the science says
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How Fenzo does it
Lessons weave in quizzes and recall prompts, so it sticks.