Inferensys

Glossary

Post-Quantum Cryptography (PQC)

Cryptographic algorithms designed to be secure against cryptanalytic attacks by both classical and large-scale quantum computers, ensuring long-term protection for encrypted data.
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QUANTUM-RESISTANT SECURITY

What is Post-Quantum Cryptography (PQC)?

Post-quantum cryptography refers to cryptographic algorithms designed to secure data against attacks from both classical and large-scale quantum computers, ensuring long-term confidentiality.

Post-Quantum Cryptography (PQC) is the development of cryptographic algorithms—typically based on lattice-based cryptography, hash-based signatures, or code-based systems—that are believed to be secure against cryptanalytic attacks by a cryptographically relevant quantum computer. Unlike quantum key distribution, PQC runs on standard classical hardware and replaces vulnerable RSA and Elliptic Curve Cryptography (ECC) algorithms with mathematical problems resistant to Shor's algorithm.

The primary goal is long-term data protection against 'harvest now, decrypt later' threats, where adversaries store encrypted healthcare data today to decrypt it once fault-tolerant quantum computers become available. Standardization is led by NIST, which has selected algorithms like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, ensuring cryptographic agility for regulated environments.

CRYPTOGRAPHIC RESILIENCE

Key Features of PQC

Post-Quantum Cryptography (PQC) standardizes algorithms designed to resist attacks from both classical and large-scale quantum computers, ensuring the long-term confidentiality and integrity of sensitive healthcare data.

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Hybrid Migration Strategies

To mitigate transition risk, security architects are deploying hybrid cryptographic schemes that combine classical and PQC algorithms. This ensures security even if one layer is broken.

  • Dual Signing: A document is signed with both ECDSA and CRYSTALS-Dilithium; both must validate.
  • Composite Encryption: Data is encrypted using a classical key encapsulation mechanism (KEM) and a PQC KEM, with the session key derived from both shared secrets.
  • This approach maintains backward compatibility while introducing quantum resistance.
04

Harvest Now, Decrypt Later (HNDL) Defense

A primary driver for immediate PQC adoption in healthcare is the HNDL threat. Adversaries are currently intercepting and storing encrypted long-lived patient records (e.g., genomic sequences) with the expectation of decrypting them once a cryptographically relevant quantum computer (CRQC) becomes available.

  • Data Longevity: Protected Health Information (PHI) often requires confidentiality for decades.
  • Forward Secrecy: PQC ensures that sessions encrypted today cannot be retroactively broken in the future.
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Performance and Bandwidth Trade-offs

PQC algorithms generally involve larger key sizes and ciphertexts compared to classical ECC, impacting network overhead and storage.

  • CRYSTALS-Kyber-768: Public key size is ~1,184 bytes vs. 32 bytes for ECC Curve25519.
  • Signature Size: CRYSTALS-Dilithium signatures are ~2,420 bytes, significantly larger than ECDSA.
  • Optimization: Hardware acceleration and instruction set extensions (e.g., AVX2) are critical for maintaining low latency in federated learning aggregation rounds.
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Stateful vs. Stateless Signatures

PQC signature schemes are categorized by their state management requirements, which is critical for fault-tolerant distributed systems.

  • Stateless (SPHINCS+, Dilithium): No secret state needs to be maintained between signatures, making them robust against system crashes and cloning—ideal for virtualized healthcare cloud environments.
  • Stateful (XMSS, LMS): Require tracking a one-time signature index; reusing a state value catastrophically breaks security. These are better suited for firmware signing in controlled hardware security modules (HSMs).
POST-QUANTUM CRYPTOGRAPHY

Frequently Asked Questions

Clear, technically precise answers to the most common questions about cryptographic algorithms designed to resist attacks from both classical and large-scale quantum computers.

Post-Quantum Cryptography (PQC) refers to cryptographic algorithms designed to be secure against cryptanalytic attacks by both classical and large-scale quantum computers. Unlike current public-key cryptosystems such as RSA and Elliptic Curve Cryptography (ECC)—which rely on the hardness of integer factorization and discrete logarithm problems that Shor's algorithm can efficiently solve on a sufficiently powerful quantum computer—PQC schemes are built upon mathematical problems believed to be intractable even for quantum adversaries. The primary families of PQC include:

  • Lattice-based cryptography: Relies on the hardness of problems like Learning With Errors (LWE) and Ring-LWE on high-dimensional lattices.
  • Code-based cryptography: Based on the difficulty of decoding random linear codes, exemplified by the Classic McEliece scheme.
  • Multivariate cryptography: Uses the hardness of solving systems of multivariate polynomial equations over finite fields.
  • Hash-based signatures: Constructs digital signatures solely from the security of cryptographic hash functions, such as the SPHINCS+ scheme.
  • Isogeny-based cryptography: Leverages the computational difficulty of finding isogenies between supersingular elliptic curves.

PQC does not require quantum computers to operate; these are purely classical algorithms that run on conventional hardware, making them a drop-in replacement for vulnerable classical cryptosystems.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.