Generator-Matrix Data Transformation for Recoverable Secure Storage
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Solution Overview
Problem
Existing systems for handling and processing plaintext data lack effective redundancy mechanisms for data recovery and protection against unauthorized access, failing to provide sufficient safeguards against data loss and exposure.
Innovation Solution
The method involves transforming input data using Galois field operations and a generator matrix to create multiple output data streams, ensuring redundancy for recovery and obfuscating the original data, allowing recovery from any subset of the output streams while maintaining confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If plaintext data is stored and processed in readily accessible formats, then ease of operation and processing speed are improved, but security and protection against unauthorized access deteriorate
Solution Approach 1:
The patent segments plaintext data into multiple separate data streams through transformation operations. Each stream contains only a portion of the original data and is meaningless alone, providing both accessibility (data can be retrieved from any stream) and security (unauthorized access to individual streams reveals no useful information).
Solution Approach 2:
The patent introduces transformation components and recovery components as intermediaries between the stored data streams and the original plaintext. These intermediaries perform mathematical transformations (such as Galois field operations) that obscure the original data while enabling recovery when sufficient streams are combined, thus protecting data during storage and transmission.
2Loss of information
If data is divided into smaller portions for storage and transmission, then loss of information is reduced through redundancy, but device complexity increases
Solution Approach 1:
The transformation components perform multiple functions: they divide data into streams, embed redundancy information, and enable recovery operations. The same mathematical framework (Galois field operations with generator matrices) handles both the segmentation and the recovery processes, reducing overall system complexity despite the multiple functions required.
Solution Approach 2:
The patent changes the mathematical parameters of the data representation by applying transformation matrices and Galois field operations. This allows the system to work with transformed data streams that contain embedded redundancy, enabling recovery from partial data loss while maintaining manageable complexity through systematic parameter transformation rather than complex structural changes.
3Reliability
If redundant data streams are created for recovery purposes, then reliability is improved, but quantity of data stored increases
Solution Approach 1:
The patent creates H data streams from W original data streams, where H > W. This excessive action provides redundancy for recovery (improving reliability) but increases storage volume. The system deliberately creates more streams than the minimum required, allowing flexible recovery scenarios where any W streams can reconstruct the original data, enhancing reliability at the cost of increased storage.
Data Source
AI summary
Systems and methods are disclosed for processing data. In one exemplary implementation, there is provided a method of generating H output data from W data input streams produced from input data. Moreover, the method may include generating the H discrete output data components via application of the W data inputs to one or more transforming components or processes having specified mathematic operations and/or a generator matrix functionality, wherein the W data inputs are recoverable via a recovery process capable of reproducing the W data inputs from a subset (any W members) of the H output data streams. Further exemplary implementations may comprise a transformation process that includes producing an H-sized intermediary for each of the W inputs, combining the H-sized intermediaries into an H-sized result, and processing the H-sized result into the H output data structures, groups or streams.