Randomized 3D Spatial Imaging for Data Integrity Verification
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Solution Overview
Problem
Existing data integrity methods are inadequate in detecting data corruption, particularly by insiders, as traditional cryptographic techniques become unreliable with large data sets and dynamic environments, leading to vulnerabilities and system failures.
Innovation Solution
Converting data to a three-dimensional (3D) spatial representation using strategically placed watermarks and random pixel distribution, followed by analysis to determine probability distributions for data verification, ensuring integrity through detection of alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cryptographic authentication methods are used for data verification, then data integrity can be verified, but the method becomes unreliable as data sets grow larger and more modules are incorporated due to propagation delays
Solution Approach 1:
The patent transforms data verification from traditional cryptographic hashing into a spatial domain problem. Data is converted to 3D spatial representations where verification occurs through spatial analysis rather than sequential hash computation, fundamentally changing the dimension of the verification process to eliminate propagation delays associated with large data sets
2Reliability
If companies subject their hardware and software offerings to independent verification testing, then data integrity can be improved, but critical IP is exposed to risk of theft, malware insertion or reverse engineering
Solution Approach 1:
The patent introduces spatial representations as an intermediary layer between the original data and verification processes. This intermediary transformation allows verification without direct access to the original data, protecting IP from theft and reverse engineering while maintaining verification capability
Solution Approach 2:
The patent creates spatial copies of data that preserve verification information but do not reveal the original data structure or content. These spatial copies enable independent verification while preventing reverse engineering of the actual intellectual property
3Measurement precision
If hash functions are used for data validation in large DFoV environments, then data changes can be detected, but the likelihood of changes occurring ahead of validation increases due to propagation delays
Solution Approach 1:
The patent moves data validation from the temporal domain of sequential hash computation to the spatial domain of parallel spatial analysis. This dimensional shift allows simultaneous verification of multiple data points, eliminating the sequential propagation delays that cause validation reliability issues in large environments
4Object-affected harmful factors
If encryption practices are applied to protect data, then data security is improved, but trackability of data or software code corruption by insiders is lost
Solution Approach 1:
The patent applies the concept of color changes by transforming data into spatial representations where different states (intact vs. corrupted) produce distinguishable spatial patterns. This allows trackability of corruption while maintaining security, as the spatial patterns reveal integrity status without exposing the actual data content
Data Source
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AI summary
A method for determining whether data on a device has changed from an original version of the data ("the original data") is provided. The method comprising: converting the data to a first three-dimensional (3D) spatial representation of the data, wherein the first 3D spatial representation represents multiple characteristics of the data using a position of a point and/or vacancy in the first 3D spatial representation; wherein converting the data to the first 3D spatial representation comprises converting the data using a same process used to generate a second 3D spatial representation from the original data; analyzing the first 3D spatial representation using one or more spatial processes; and determining whether the data has changed based on the analysis of the first 3D spatial representation.