Dynamic Data Encryption Using ML-Derived Keys and Hash Verification
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Solution Overview
Problem
Data exchanged between endpoints in a communication network is vulnerable to interception and manipulation by bad actors, leading to compromised communication and security operations, potentially causing large-scale losses and information exposure.
Innovation Solution
A system and method that dynamically encrypts, decrypts, and verifies data using machine learning models and multiple keys, ensuring a constantly-changing encryption process that adapts to immediate modifications, thereby securing data exchanges and preventing unauthorized tampering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static encryption methods are used, then implementation is simple, but security is compromised because encryption keys remain constant and can be intercepted
Solution Approach 1:
The patent applies dynamics by transforming static encryption keys into dynamic, constantly changing keys through machine learning model transformations. The encryption key evolves over time based on ML operations, ensuring that even if one key is compromised, previous and future encrypted communications remain secure. This resolves the contradiction by making the encryption process adaptive and time-dependent.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the plaintext and encryption key. The ML model transforms the original key into a derived encryption key through its internal operations, adding a layer of complexity and security. This intermediary prevents direct access to the original key while maintaining the encryption functionality.
2Reliability
If machine learning models are used for dynamic encryption, then security is improved through constantly-changing keys, but processor and memory usage increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model with encryption-related parameters and key transformation rules before actual encryption operations. This pre-processing allows the model to perform efficient key transformations during runtime without requiring extensive computation for each encryption operation, thereby reducing real-time processor and memory usage.
Solution Approach 2:
The patent changes the parameters of the ML model dynamically based on the encryption requirements and data characteristics. By adjusting model parameters such as learning rates, transformation depths, and key derivation methods, the system optimizes the balance between security strength and computational resource consumption.
3Reliability
If data is encrypted multiple times with different keys, then security against interception is improved, but encryption and decryption time increases
Solution Approach 1:
The patent implements continuous useful action by establishing a chain of encrypted communications where each message is encrypted with a key derived from the previous message's encryption process. This continuous derivation creates a seamless security chain that provides strong anti-interception capability while maintaining efficient encryption and decryption operations through the established ML model framework.
Data Source
AI summary
An apparatus comprises a memory communicatively coupled to a processor. The memory is configured to store one or more machine learning algorithms associated with encrypting data in accordance with one or more machine learning models. The processor is configured to generate a first hash of shareable data and encrypt the shareable data based at least in part upon one or more keys upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models. Further, the processor is configured to generate a second hash of an encrypted shareable data, combine the first hash and the second hash into a combined sender hash, encrypt the combined sender hash, and transmit an encrypted combined sender hash and the encrypted shareable data to a receiver.