Geometric Data Encoding for Quantum-Resistant Security
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Solution Overview
Problem
Current binary-based data representations are inefficient and insecure, leading to computational inefficiencies and vulnerabilities, especially with the advent of advanced computing technologies like quantum computing.
Innovation Solution
Encoding data using geometric shapes and transformations, where images are represented as combinations of geometric primitives like circles, triangles, and squares, with transformations such as scaling, rotation, and translation, to create a more compact and secure form of data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary-based data representation is used, then data can be processed by conventional computing devices, but computational efficiency is low and security is vulnerable
Solution Approach 1:
The patent changes the fundamental parameter of data representation from binary (0s and 1s) to geometric properties (shape, size, position, orientation). This parameter transformation enables both improved security through triple-layer encryption and enhanced computational efficiency by leveraging specialized geometric processing hardware that can handle these transformed parameters more effectively than traditional binary processors.
Solution Approach 2:
The patent substitutes the conventional binary processing mechanism with a geometric processing mechanism. By encoding data as geometric shapes and using geometric transformations (rotation, scaling, translation) for processing, the system replaces traditional binary logic operations with geometric operations that can be performed more efficiently and securely, particularly resistant to quantum computing attacks.
2Reliability
If traditional binary encoding is used, then data processing is straightforward, but the system is vulnerable to quantum computing attacks
Solution Approach 1:
The patent transforms data from binary parameters to geometric parameters (shape, size, position, orientation). This parameter change creates quantum resistance because geometric relationships are inherently more complex to compromise than binary states. The triple-layer encryption built into the geometric encoding structure provides inherent quantum resistance while managing complexity through systematic encoding rules.
Solution Approach 2:
The patent creates a composite encoding structure combining multiple geometric properties (shape type, size, position, orientation) and triple-layer encryption mechanisms. This composite approach to data representation provides robust quantum resistance by layering multiple protective geometric and cryptographic mechanisms, making the system resistant to quantum attacks while maintaining structured complexity.
3Reliability
If geometric encoding is implemented, then security is enhanced and quantum resistance is achieved, but implementation complexity increases
Solution Approach 1:
The patent segments the geometric encoding process into distinct, manageable layers: shape encoding, size encoding, position encoding, orientation encoding, and triple-layer encryption. Each layer handles a specific aspect of the data representation, making the overall complex system easier to implement and maintain by breaking it down into modular components with clear responsibilities.
Solution Approach 2:
The patent manages implementation complexity by establishing clear parameter transformation rules for encoding (converting binary data to geometric parameters) and decoding (converting geometric parameters back to binary data). These standardized parameter change procedures, combined with systematic triple-layer encryption integration, make the geometric encoding system more implementable despite its enhanced security features.
Data Source
AI summary
Disclosed in some examples are methods, systems, devices, and machine-readable mediums which encode data into a geometric representation for more efficient and secure processing. For example, data may be converted from a binary representation to a geometric representation using an encoding dictionary. The encoding dictionary specifies one or more geometric shapes used in the encoding. The geometrically encoded data may comprise one or more identifiers that specify one or more of the shapes of the encoding dictionary that best match one or more detected features in an image corresponding to the data. In some examples, the geometrically encoded data may also comprise one or more transformations of the one or more shapes to reduce error in the geometric encoding.