Encrypting Data in Machine Learning Models via Decryption Code

The patent replaces conventional cryptographic encryption mechanisms with a machine learning-based solution. Instead of using mathematical encryption algorithms that can be computationally broken, the system uses a trained neural network to encrypt data. The neural network learns to transform plaintext into ciphertext through training, and the decryption process involves feeding the ciphertext back through the same network with additional processing. This substitution of mechanical/cryptographic systems with AI-based systems resolves the contradiction by providing security that is computationally infeasible to break.

The patent changes the fundamental parameters of the encryption system by moving from static cryptographic keys to dynamic neural network models. The encryption process involves transforming data through multiple layers of neural network processing, where the network's weights and activations serve as the encryption key. This parameter change enables the system to achieve security levels that conventional encryption cannot reach, as the neural network's complexity and the computational resources required to reverse the transformation are prohibitively high.

The patent segments the encryption functionality into distinct components: a neural network model for encryption, a separate decryption process that uses the same model in reverse, and a control mechanism that manages when and how data is accessed. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high security. The model itself is segmented into training phases where it learns encryption patterns separately from decryption operations.

The neural network model serves multiple functions: it acts as both the encryption mechanism and the decryption mechanism, and it simultaneously performs data transformation, security protection, and access control. By making the model universal, the system reduces overall complexity compared to having separate dedicated components for each function. The same network architecture handles encryption, decryption, and security validation, thereby improving the security-complexity tradeoff.

The patent implements a disposable model approach where the neural network is trained, used for a single encryption operation, and then deleted. This short-living model ensures that even if the model structure itself were compromised, the data cannot be retrieved from the model. The model's temporary existence and subsequent destruction provide a security feature where the computational cost of creating and destroying the model is acceptable compared to the security benefit. This resolves the contradiction by accepting limited operational duration in exchange for enhanced data protection.

The system prepares for future security needs by deleting the model after use, creating a cushion against potential future attacks. The deletion action itself serves as a security measure that prevents any future retrieval of data from the model. This beforehand cushioning ensures that even if computational resources or attack methods advance, the data remains protected because the model that could potentially expose it no longer exists. The operational duration is limited only by the time needed for the single encryption operation, which is acceptable given the security benefit.