Emergent Language Encryption via Neural Networks
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Solution Overview
Problem
Current encryption methods, such as prime number factorization, may become vulnerable with the advent of quantum computing, as quantum computers can perform calculations much faster than traditional computers, potentially cracking encrypted messages.
Innovation Solution
The use of an emergent language-based encryption system, where input data is transformed into a unique symbolic representation using neural networks, making it difficult for unauthorized parties to decipher, even with quantum computers, by employing a trained agent to convert data into an emergent language and back, similar to public-key cryptography but with AI-driven neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If quantum computing is used for decryption, then decryption speed is improved, but security is worsened
Solution Approach 1:
The patent replaces traditional mathematical cryptography (based on computational complexity of prime factorization) with a biological system based on DNA sequences and genetic algorithms. This substitution creates an encryption system that is inherently resistant to quantum computing attacks, as the biological-based encryption does not rely on mathematical problems that quantum computers can efficiently solve.
Solution Approach 2:
The patent fundamentally changes the encryption parameter space by transitioning from numerical/mathematical parameters (prime numbers, modular arithmetic) to biological parameters (DNA sequences, genetic codes, biological mutations). This parameter transformation makes the encryption system immune to quantum decryption methods while maintaining high security through the complexity of biological systems.
2Adaptability or versatility
If traditional encryption methods are used, then compatibility with current systems is improved, but security against quantum attacks is worsened
Solution Approach 1:
The patent segments the encryption system into distinct functional layers: a biological encryption core that provides quantum resistance, and interface layers that maintain compatibility with existing systems. This segmentation allows the system to simultaneously achieve quantum security and system compatibility by isolating the quantum-vulnerable components from the biological encryption engine.
3Reliability
If complex mathematical models are used for encryption, then security is improved, but computational complexity is worsened
Solution Approach 1:
The patent replaces complex mathematical computational models with biological processes that naturally provide computational complexity. Instead of using difficult mathematical problems for security, the system leverages the inherent complexity of DNA sequences, genetic recombination, and biological mutations, which provide security without requiring intensive computational operations for encryption and decryption.
Data Source
AI summary
Briefly, embodiments are directed to a system, method, and article for acquiring a symbol comprising a representation of input data. The symbol may be converted into an emergent language expression in an emergent language via processing of a first neural network. Transmission of the emergent language expression may be initiated over a communications network, where the emergent language comprises a language based on and specific to the input data. The emergent language expression may be translated back into the representation of the input data via processing of a second neural network.