Neural Network Synchronization for Quantum-Resistant Key Exchange
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Solution Overview
Problem
Modern encryption systems based on mathematical difficulties are vulnerable to quantum computers, necessitating a new key exchange technology that is quantum-resistant.
Innovation Solution
A method and apparatus for generating a secret key using a synchronized neural network, where initial codewords are generated based on bit strings of weight values, transmitted and combined with partial codewords from another party, with error correction and restoration success information used to generate the secret key.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modern encryption algorithms based on mathematical difficulties are used, then key exchange security is provided, but the system becomes vulnerable to quantum computer attacks
Solution Approach 1:
The patent replaces traditional mathematical difficulty-based encryption mechanisms with a neural network synchronization mechanism. Instead of relying on mathematical problems like factoring or discrete logarithms, the system uses coupled neural networks that synchronize their states through interaction, generating identical secret keys through dynamic synchronization rather than static mathematical computations.
Solution Approach 2:
The patent changes the fundamental parameter of key exchange from mathematical problem hardness to neural network synchronization dynamics. By adjusting coupling parameters, interaction strengths, and neural network architectures, the system generates secure keys through dynamic behavioral synchronization rather than static mathematical properties.
2Reliability
If neural network synchronization is used for key exchange, then quantum resistance is achieved, but system complexity increases
Solution Approach 1:
The patent divides the key exchange process into distinct phases: neural network initialization, coupled synchronization interaction, state extraction, and error correction. Each phase is handled by separate functional modules, making the complex neural network system manageable and implementable through standardized procedures.
Solution Approach 2:
The patent introduces error correction codes and reconciliation protocols as intermediary mechanisms between the neural networks and the final key generation. These intermediaries handle the complexity of synchronization verification and error management, shielding users from the underlying neural network complexity while ensuring reliable key exchange.
Data Source
AI summary
A method and an apparatus for generating a secret key for encrypted communication using a synchronized neural network, which includes: generating initial codewords based on a bit string of weight values of the synchronized neural network and transmitting a first partial codeword of the initial codewords to a device of another party; receiving a second partial codeword generated by the device of the other party and combining final codewords based on the second partial codeword received from the device of the party and the bit string of the weight values; performing an error correction on the combined final codewords and transmitting first restoration success information according to the error correction to the device of the other party; and receiving second restoration success information from the device of the other party and generating the secret key based on the restoration success information of the device of the other party, are provided.