Neural Network Data Encoding for Secure, Disturbance-Tolerant Encryption
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Solution Overview
Problem
Existing encryption methods for audio, images, or video are insecure due to insufficient encryption of user data, leading to inefficiency and limited reproducibility, especially when faced with transmission disturbances.
Innovation Solution
A method using a neural network with a key, such as an image, to encrypt user data by training the network on the key, generating cipher information from node weights, and transmitting only these weights, ensuring high security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sufficiently high level of encryption is chosen for the user data, then security is improved, but encryption speed decreases and efficiency becomes insufficient
Solution Approach 1:
The encryption method segments the data processing into two distinct phases: training phase (performed once offline) and encoding phase (performed rapidly for each encryption task). The neural network is trained offline on the encryption key to learn the mapping, and then this pre-trained network is used for rapid encoding of ciphertext. This segmentation allows computationally intensive operations to be performed once, while subsequent encryptions benefit from the pre-learned mappings, resolving the contradiction between security and speed.
Solution Approach 2:
The neural network performs preliminary learning of the encryption mapping during an offline training phase using the encryption key. This preliminary action pre-computes the complex transformations needed for secure encryption, so that during actual data encryption, only a forward pass through the trained network is required. This preliminary preparation enables high-speed encryption while maintaining strong security based on the learned key mappings.
2Productivity
If traditional encryption methods are used, then encryption speed is maintained, but the reproducibility of user data is limited and disturbance tolerance is poor
Solution Approach 1:
The neural network encoding process incorporates feedback mechanisms through its architecture, where the network's internal states and weight adjustments during training enable it to learn robust mappings that are tolerant to disturbances. The network adjusts its weights based on the training data and key, creating a system that can reproduce the encryption consistently even when subjected to transmission disturbances, unlike traditional deterministic encryption methods.
Solution Approach 2:
The invention changes the parameter space by using continuous weight adjustments in the neural network during training, rather than fixed discrete transformations. The network weights are optimized to capture the essential mapping between plaintext and ciphertext, creating a system that is inherently more robust to disturbances. This parameter-based approach with learned transformations provides both speed and reproducibility that traditional methods lack.
3Reliability
If the transmitted cipher information is disrupted, then transmission errors occur, but the user data cannot be reconstructed or decrypted
Solution Approach 1:
The neural network is trained beforehand on the encryption key to learn robust mappings that inherently cushion against transmission disturbances. During the training phase, the network adjusts its weights to capture the essential transformations, creating a system that can tolerate certain levels of noise or disruption in the transmitted ciphertext. This prior preparation enables the decryption process to successfully reconstruct the original data even when the transmitted cipher information is partially disrupted.
4Reliability
If a large network with more nodes is used, then error reduction and disturbance tolerance are improved, but device complexity increases
Solution Approach 1:
The neural network is segmented into distinct layers (input layer, hidden layers, output layer) with specific functions. This segmentation allows the complexity to be organized and managed systematically, where each layer processes specific aspects of the encryption mapping. The modular structure enables the network to achieve high error tolerance through its distributed representation while maintaining manageable complexity through organized functional divisions.
Data Source
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AI summary
A method for encoding and decoding user data, the user data particularly being images. The method for encoding user data comprises the steps of: providing a key, wherein the key is particularly an image, providing a network having N nodes, providing user data, inputting the key and training the network to the user data and producing cipher information from the weightings of the network's nodes.