Machine-Learning Encoder-Decoder Keys for Low-Setup Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing secure communication methods require repeated key reestablishment, which is challenging in non-networked facilities, and one-time pad approaches limit communication frequency and are vulnerable to future key compromise.
Innovation Solution
Implement a machine learning-based encoder-decoder model for automatic key generation, synchronizing a seed key once and generating new keys for each session, ensuring similarity and difference between keys to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repeated key reestablishment is used in existing secure communication methods, then communication security can be maintained, but the complexity and difficulty of setup increases significantly
Solution Approach 1:
The system uses machine learning models to automatically generate and synchronize keys between communicating parties without requiring manual key exchange or repeated setup. The encoder and decoder models autonomously create compatible keys based on input data, eliminating the need for complex manual key reestablishment while maintaining security.
Solution Approach 2:
The patent replaces traditional mechanical key exchange processes with machine learning-based automatic key generation. Instead of manually synchronizing keys through network communication, the system uses trained ML models to generate compatible keys automatically, significantly reducing setup complexity while maintaining security.
2Productivity
If one-time pad encryption is used, then communication frequency can be increased, but the system becomes vulnerable to future key compromise
Solution Approach 1:
The system dynamically generates new keys for each communication session based on the input data and previous key history, rather than using static one-time pads. The machine learning models continuously adapt and generate fresh keys that are both unique to each session and resistant to future compromise, combining the benefits of frequent communication with enhanced security.
Solution Approach 2:
The machine learning models incorporate feedback from previous key generations and communication history to create more secure future keys. This feedback mechanism ensures that each new key is both unique for the current session and designed to resist future compromise, eliminating the vulnerability inherent in traditional one-time pad systems.
3Ease of operation
If machine learning-based automatic key generation is implemented, then key setup and maintenance is simplified, but computational resources and processing time are required
Solution Approach 1:
The machine learning models are pre-trained offline using extensive training data and computational resources. Once trained, the models can generate keys quickly during actual communication without requiring significant real-time computational resources. This preliminary action separates the heavy computational workload from the operational phase, making key generation both easy to use and computationally efficient during actual communication.
Data Source
AI summary
A processing system may obtain a first source data and encoding the first source data in accordance with a first key via an encoder associated with an encoder-decoder model to generate a first source data encoding that is a first output of the encoder and a second key that is a second output of the encoder. The processing system may then transmit the first source data encoding to a recipient system, where the second key is for generating a second source data encoding for transmission by the processing system to the recipient system. A processing system may also obtain a first encoded source data from a sending system, apply the first encoded source data and a first key as inputs to a decoder associated with an encoder-decoder model to obtain a first decoded source data and a second key as outputs, and present the first decoded source data.