Post-Quantum Cryptography

This approach is based on the hardness of solving systems of multivariate quadratic polynomials over finite fields. The secret is a structured, easily invertible multivariate map, while the public key is its disguised, complex form.

• Key Algorithms: CRYSTALS-Dilithium (uses structured lattices, but general multivariate schemes exist).
• Typical Use: Primarily for digital signatures (e.g., Rainbow, though it was broken in later analysis).
• Operation: Signing involves using the secret trapdoor to find a solution to the public system of equations. Verification checks that the solution satisfies the public polynomials.
• Consideration: Many historical multivariate schemes have been broken, highlighting the need for careful parameter selection and design.

A more recent approach that uses the mathematical theory of elliptic curves, but derives security from the difficulty of computing an isogeny (a special kind of map) between two supersingular elliptic curves, rather than from the discrete logarithm problem.

• Key Algorithms: SIKE (Supersingular Isogeny Key Encapsulation), though it was later found to have a potential vulnerability.
• Core Problem: The Supersingular Isogeny Diffie-Hellman (SIDH) problem.
• Advantage: Offers very small key sizes compared to other post-quantum schemes.
• Status: An active area of research; the cryptanalysis of SIKE demonstrated the relative youth and evolving understanding of this mathematical area for cryptography.

A dedicated, tamper-resistant physical device that performs cryptographic operations and manages digital keys. In Edge AI deployments, HSMs provide a Root of Trust for secure key storage, accelerating PQC algorithms, and signing firmware/model updates.

• Function: Offloads and protects encryption/decryption, digital signatures, and key generation.
• Edge Relevance: Essential for implementing Secure Boot, Remote Attestation, and managing PQC key pairs on edge nodes.

The suite of cryptographic techniques that protect the decentralized training process in Federated Learning. This ensures model updates from edge devices can be aggregated without exposing the private local training data.

• Key Techniques: Secure Multi-Party Computation (MPC) for aggregation, Differential Privacy to add statistical noise, and Byzantine-Robust Aggregation to tolerate malicious clients.
• PQC Synergy: Protects the federated learning control channel and update signatures against future quantum decryption.

A form of encryption that allows computations to be performed directly on ciphertext, generating an encrypted result that, when decrypted, matches the result of operations on the plaintext. For Edge AI, this enables privacy-preserving inference where data never needs to be decrypted on the device.

• State: Partially Homomorphic Encryption is practical today; Fully Homomorphic Encryption (FHE) is computationally intensive but emerging.
• Use Case: A sensor sends encrypted data to an edge server; the server runs the model on the encrypted data and returns an encrypted prediction, preserving data confidentiality.

A security protocol where a trusted verifier cryptographically confirms the software integrity and hardware state of a remote edge device. It proves the device is running authentic, unmodified firmware and a known-good AI model.

• Mechanism: The device's Root of Trust generates a signed report of its measured boot sequence and loaded software.
• Critical for PQC: Attestation verifies that the correct, quantum-resistant cryptographic libraries and protocols are actively running before sensitive data is exchanged.

The integration of security practices into the machine learning operations (MLOps) lifecycle. For Edge AI Security, MLSecOps mandates continuous scrutiny of the ML pipeline for vulnerabilities unique to AI, from data collection to model deployment on edge fleets.

• Key Practices: Adversarial Robustness testing, Data Poisoning Defense, Model Watermarking for IP protection, and secure SBOM tracking for all model dependencies.
• PQC Integration: Ensures PQC algorithms are correctly implemented, key lifecycles are managed, and models are signed with quantum-safe signatures before OTA deployment.