Decrypted Data Hash Validation for Dynamic Encryption Security
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Solution Overview
Problem
Data exchanged between endpoints in a communication network is vulnerable to interception and manipulation by bad actors, compromising communication and security operations, particularly in cyber-attacks that can cause large-scale losses and information exposure.
Innovation Solution
A system and method utilizing machine learning models to dynamically encrypt, decrypt, and verify data through the use of multiple keys, including public, semi-private, and private keys, by calculating and combining hash values to ensure data authenticity, with a processor configured to compare hash combinations for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static encryption methods are used, then implementation is simple, but data security is compromised because encryption keys remain constant and vulnerable to interception
Solution Approach 1:
The patent implements dynamic encryption where the encryption key changes continuously based on machine learning model predictions. The ML model analyzes communication patterns and generates varying encryption keys for each data transmission, making the encryption process adaptive and resistant to interception while maintaining security reliability
Solution Approach 2:
The system incorporates feedback loops where the machine learning model continuously monitors communication data and adjusts encryption keys based on detected patterns and potential threats. This feedback mechanism allows the encryption process to adapt in real-time, improving security without requiring complete redesign of the system architecture
2Reliability
If machine learning models are used for dynamic encryption, then data authenticity is improved, but processor and memory usage increase
Solution Approach 1:
The patent applies machine learning models selectively rather than to all data transmissions. The ML model is used to generate encryption keys and verify authenticity only when necessary based on communication patterns and risk assessment, reducing overall processor and memory usage while maintaining data authenticity where critical
3Reliability
If verification operations are performed on all decrypted data, then data integrity is ensured, but processing time increases
Solution Approach 1:
The patent performs preliminary verification by embedding authentication codes and hash values within the encrypted data structure before transmission. The receiving end can perform quick verification of these pre-computed values without requiring extensive processing time, ensuring data integrity efficiently
Solution Approach 2:
The verification process is segmented into multiple stages: initial quick verification of authentication codes, followed by more thorough verification only when anomalies are detected. This segmentation reduces overall verification time while maintaining data integrity through layered validation
Data Source
AI summary
An apparatus comprises a memory communicatively coupled to a processor. The processor is configured to obtain a combined sender hash from a decrypted combined sender hash upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models and obtain a combined receiver hash from a decrypted shareable data 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 compare the combined sender hash to the combined receiver hash, determine whether the combined sender hash matches the combined receiver hash, determine that the shareable data is authentic in response to determining that the combined sender hash matches the combined receiver hash, generate a report indicating that the shareable data is authentic, and transmit the report to a sender of the combined sender hash.