Under Development
Please note: This section is a work in progress. Information and features are subject to change and may be incomplete.

This research presents a quantum-resistant encryption algorithm that leverages a high-dimensional lattice framework (n=1000) based on the Learning With Errors (LWE) problem[cite: 290, 302]. [cite_start]The scheme is designed to counter threats from quantum algorithms like Shor's and Grover's, which undermine classical cryptosystems such as RSA and AES[cite: 288, 296]. [cite_start]The parameters are carefully chosen to align with NIST's post-quantum security standards (Category 5, 256-bit quantum security)[cite: 292, 302].
This work details a post-quantum cryptographic (PQC) algorithm founded on the hardness of the Learning With Errors (LWE) problem[cite: 290, 301]. [cite_start]It operates in a 1000-dimensional space with a modulus of q ≈ 2^32 and a discrete Gaussian error distribution, providing robust quantum resistance[cite: 291, 329, 330, 331]. [cite_start]The paper specifies the mathematical operations for key generation, encryption, and decryption, with security guarantees based on worst-case hardness assumptions[cite: 292, 301].
The scheme's security is based on the LWE problem[cite: 322]. [cite_start]Key generation involves creating a public key (A, b) where b = As + e (mod q) and a secret key s[cite: 333, 337]. [cite_start]For a message m, encryption produces a ciphertext (u, v) by computing u = A^T * r (mod q) and v = b^T * r + floor(q/2)m (mod q), using a random vector r[cite: 341, 343, 344]. [cite_start]Decryption recovers the message m by calculating m' = round(2/q * (v - s^T * u)) (mod 2)[cite: 348, 351]. [cite_start]The paper also discusses optimizations using Ring-LWE and enhanced error reconciliation techniques[cite: 388, 392].
Shifted focus to more practical AI applications and generative models.
The research identified several avenues for future work, including algorithmic optimizations via parallel processing and hardware acceleration (GPUs/FPGAs)[cite: 448]. [cite_start]It also proposed investigating advanced error reconciliation techniques and conducting field testing in real-world environments like secure cloud storage and IoT systems to evaluate practical performance[cite: 450, 452].
