Recoverable Data Transformation Using Secure Redundant Streams
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Solution Overview
Problem
Existing systems for handling and subdividing information lack the ability to effectively recover data when loss occurs and do not provide adequate protection for plaintext data from unauthorized access.
Innovation Solution
The method involves transforming input data, including plaintext, into output data streams using Galois field operations and a generator matrix, resulting in redundant data streams that can be used for recovery and protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If plaintext data is stored and handled in a readily accessible manner, then ease of operation is improved, but security against unauthorized access deteriorates
Solution Approach 1:
The plaintext data is divided into multiple segments or shards using Galois field operations. Each segment alone is insufficient to reconstruct the original data, providing security while allowing efficient access when sufficient segments are available. The data is split into W segments and distributed across H storage locations.
Solution Approach 2:
Transformed data segments serve as intermediaries between the original plaintext and its storage locations. These transformed segments obscure the underlying plaintext while maintaining the ability to reconstruct it through mathematical operations, thus protecting data during transmission and storage.
2Ease of operation
If data is subdivided into smaller portions for easier handling and storage, then ease of operation is improved, but the ability to recover data when loss occurs deteriorates
Solution Approach 1:
Redundant data segments are generated in advance using Galois field operations and a generator matrix. This preliminary creation of redundant information ensures that even if some segments are lost during storage or transmission, the original data can be recovered from any W out of H segments.
Solution Approach 2:
The system transforms data using mathematical parameters (Galois field operations, generator matrix) to create multiple valid representations of the same information. By changing the representation parameters rather than the underlying data, the system enables flexible recovery options while maintaining data integrity.
3Reliability
If redundant data streams are created for recovery purposes, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or procedural backup systems with mathematical transformations based on Galois field operations. Instead of physically duplicating and managing multiple complete copies of data, the system uses efficient mathematical operations to generate compact redundant segments that require less storage and management overhead.
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.