Neural Network Custom Language for Quantum-Resistant Communications
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Solution Overview
Problem
Traditional data protection technologies are vulnerable to quantum computing, necessitating new methods to secure sensitive information as quantum computers can potentially crack encryption.
Innovation Solution
Utilizing deep learning models to create a custom language for neural networks that obfuscate and optimize communications by translating requests and responses between devices, preventing unauthorized access and interception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption methods are used to protect sensitive information, then data security is improved, but the security becomes vulnerable to quantum computing attacks
Solution Approach 1:
The patent changes the fundamental parameter of data protection from mathematical encryption to biological language interpretation. Neural networks are trained to create and speak custom languages that evolve over time, transforming the protection mechanism from static cryptographic algorithms to dynamic, adaptive communication protocols that quantum computers cannot crack.
Solution Approach 2:
The patent replaces the mechanical/mathematical system of traditional encryption with a biological-inspired system using neural networks. Instead of relying on mathematical complexity, the system uses trained neural networks to generate and interpret custom languages, substituting computational mathematics with bio-mimetic pattern recognition and generation.
2Reliability
If custom languages are created by neural networks to obfuscate communications, then security against quantum computing is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service by allowing neural networks to autonomously create and evolve their own custom languages without continuous human intervention. The networks train themselves on sample data and automatically generate the communication protocols, reducing the need for complex manual configuration and maintenance despite the underlying system complexity.
Solution Approach 2:
The patent applies preliminary action by training neural networks in advance to create custom languages before deployment. The networks are pre-trained on sample request-response data and initialization vectors, establishing the obfuscation protocol beforehand so that the complex training process occurs once during setup rather than continuously during operation.
3Reliability
If neural networks translate requests and responses into custom languages, then unauthorized access is prevented, but communication overhead increases
Solution Approach 1:
The patent ensures continuity of useful action by making the translation process seamless and integrated into the communication flow. The neural networks continuously translate requests and responses in real-time without interrupting the overall communication process, maintaining the speed and efficiency of data exchange while adding the protective layer of custom language obfuscation.
Data Source
AI summary
Concepts and technologies are disclosed herein for using deep learning models to obfuscate and optimize communications. A request can be received in a first language, from a user device, and at a first computing device storing a first neural network. The request can be translated using the first neural network into a modified request in a custom language. The modified request can be sent to a second computing device hosting an application. The first computing device can receive a modified response that is in the custom language, where the modified response can be created at the second computing device using the second neural network and based on a response from the application. The modified response can be translated into a response in the first language and sent to the user device.