Neural Network Encryption for Quantum-Resistant Zero-Trust Security

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Solution Overview

Problem

Existing public key encryption models, such as RSA and elliptic curve encryption, are vulnerable to quantum computing and require significant computing resources to protect sensitive data, posing a risk to zero-trust architectures.

Innovation Solution

Implement a crypto system that generates neural network encryption models based on dataset descriptors and obfuscation features, using intelligent decryption models to enhance zero-trust security by verifying target environments and preventing decryption in invalid environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional public key encryption models (RSA, elliptic curve) are used, then encryption functionality is provided, but security is compromised against quantum computing attacks

Engineering Contradiction:

ImprovesecurityVSAvoidquantum attack vulnerability

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent changes the fundamental parameters of encryption by transitioning from traditional mathematical functions (RSA, elliptic curve) to neural network-based encryption. This involves changing the encryption algorithm type, key generation method, and decryption approach to create quantum-resistant security while maintaining encryption functionality.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces traditional cryptographic mechanical systems (mathematical functions) with a neural network-based system. The neural network learns encryption patterns and can perform decryption without knowing the traditional private key, substituting the mathematical mechanism with an intelligent system that is resistant to quantum attacks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional encryption models are used, then encryption is provided, but significant computing resources are consumed

Engineering Contradiction:

Improvedata protectionVSAvoidcomputing resources

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent performs preliminary action by training the neural network encryption model in advance. Once trained, the model can perform encryption and decryption operations efficiently without requiring significant computing resources during actual data protection operations. The heavy computational work is done beforehand during the training phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent substitutes traditional computationally intensive cryptographic operations with a neural network-based system that, after training, can perform encryption and decryption with reduced computational overhead, thereby conserving computing resources during data protection operations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If public key specifications are made available, then encryption standards are established, but zero trust architectures are exposed to failures and threats

Engineering Contradiction:

Improveencryption standard availabilityVSAvoidzero trust security

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent extracts the security-critical components (encryption logic, key management) from traditional transparent systems and embeds them within a neural network. The neural network's internal structure and learned parameters are not easily extractable or analyzable, providing inherent security while maintaining encryption functionality.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The neural network acts as an intermediary between encryption and decryption operations. It mediates the cryptographic process by learning encryption patterns and performing decryption without exposing traditional private keys or vulnerability points, thereby protecting zero trust architectures while maintaining adaptability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250300818A1Systems and methods for providing enhanced multi-layered security with encryption models that improve zero-trust architectures

Publication Date: 2025.09.25 VERIZON PATENT & LICENSING INC

AI summary

A device may generate neural network encryption models based on a dataset descriptor, a dataset geometry, and selected neural network types, and may generate obfuscation features based on the dataset descriptor, a noise type, an obfuscation model type, and noise and model parameters. The device may train the neural network encryption models, with a dataset and the obfuscation features, to generate model weights, a latent space, and noising and denoising models, and may generate an intelligent decryption model based on the model weights, the latent space, and the noising and denoising models. The device may receive an encrypted dataset associated with a target environment, and may determine whether the target environment is valid according to immune rules. The device may process, based on determining that the target environment is valid, the encrypted dataset, with the intelligent decryption model, to generate a decrypted dataset.