Program Data Encoding in Trusted Execution Environments
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Solution Overview
Problem
Current cybersecurity solutions are inadequate in addressing the hyper-scalability dilemma, failing to effectively distinguish between noble and nefarious activities in digital ecosystems, and are vulnerable to quantum computer-assisted cryptanalysis and AI-informed subversive algorithms, leading to catastrophic cyber-attacks and privacy assaults.
Innovation Solution
Cyphergenics (CG) technology employs computationally complex genomic constructions to enable hyper-scalability, allowing for virtual unboundedness and strategic regulation of digital ecosystems, using information theory-constructed genomic constructions that preserve computational integrity and enable virtual affiliation, authentication, agility, and trusted execution domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cryptographic solutions are used, then basic security is provided, but hyper-scalability is compromised and the system becomes vulnerable to quantum attacks
Solution Approach 1:
The patent transforms cryptographic security from a static parameter to a dynamic, scalable parameter by introducing genomic constructions that can be instantiated in unlimited quantities with consistent security properties. Each genomic construction instance provides identical security guarantees while enabling hyper-scalable deployment across digital ecosystems.
Solution Approach 2:
The patent segments cryptographic functionality into modular genomic constructions (CNA, LNA, PNA, XNA, ZNA objects) that can be independently deployed and composed. This segmentation enables hyper-scalability by allowing selective instantiation of security constructs where needed, rather than requiring monolithic cryptographic infrastructure.
2Reliability
If traditional security technologies are deployed, then perimeter protection is achieved, but they fail to distinguish between noble and nefarious activities effectively
Solution Approach 1:
The patent applies local quality by enabling differentiated security treatment for different digital activities and entities. Genomic constructions can be selectively applied to specific transactions, users, or data flows, providing tailored security and detection capabilities that adapt to the local context of each interaction rather than applying uniform perimeter security.
Solution Approach 2:
The patent introduces a metaphorical 'color coding' system where genomic constructions can mark and differentiate between legitimate (noble) and malicious (nefarious) activities. This enables precise detection and classification of digital activities based on their genomic signatures and behavioral patterns.
3Strength
If computational complexity is increased to resist quantum attacks, then security strength improves, but system overhead and bandwidth requirements increase significantly
Solution Approach 1:
The patent uses copying by creating multiple instances of standardized genomic construction templates rather than computing complex cryptographic primitives from scratch each time. This allows rapid instantiation of security constructs with consistent strength guarantees while minimizing computational overhead through template-based replication.
Solution Approach 2:
The patent applies preliminary action by pre-computing and storing genomic construction templates (CNA, LNA, PNA, XNA, ZNA objects) that encapsulate complex cryptographic logic. These pre-prepared templates can be instantly deployed without repeated complex computations, reducing real-time overhead while maintaining quantum-resistant security strength.
Data Source
AI summary
A method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genomic differentiation object corresponding a computer program; modifying the genomic differentiation object based on genomic regulation instructions (GRI) to obtain a modified genomic differentiation object; and executing a first executable instruction of the computer program. Executing the first executable instruction includes: retrieving first encoded data that is input to the first executable instruction; extracting a sequence from metadata associated with the encoded data; generating a first genomic engagement factor (GEF) based on the first sequence, the GRI and, and the modified genomic differentiation object; decoding the first encoded data based on the first GEF to obtain first decoded data; and executing the first executable instruction using the first decoded data.