Recoverable Secure Data Transformation Using Redundant Output Streams
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Solution Overview
Problem
Existing data processing systems lack effective methods for transforming plaintext data into redundant forms that provide protection and recovery capabilities, particularly in scenarios where data loss occurs, and they fail to securely protect data from unauthorized access.
Innovation Solution
The system employs Galois field operations and a generator matrix to transform input data into multiple output data streams, ensuring that the original data can be recovered from any subset of the output streams, while maintaining confidentiality by not storing plaintext directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If plaintext data is stored and handled in a readily accessible manner, then data accessibility and processing efficiency are improved, but data security and protection from unauthorized access deteriorate
Solution Approach 1:
The system segments plaintext data into multiple separate data streams through transformation components. Each stream contains only a portion of the original data, making individual streams useless without the others. This segmentation maintains processing efficiency while improving security, as unauthorized access to any single stream does not expose the complete plaintext data.
Solution Approach 2:
The patent introduces transformation components as intermediaries between the original plaintext data and the stored data streams. These components apply Galois field operations and generator matrices to convert plaintext into transformed streams, acting as a security barrier that prevents direct access to plaintext while allowing efficient processing of the transformed data.
2Device complexity
If data is divided into smaller portions for easier handling and storage, then data management and storage efficiency are improved, but data recovery capability when loss occurs deteriorates
Solution Approach 1:
The system performs preliminary transformation of plaintext data into multiple redundant data streams using Galois field operations and generator matrices before storage. This preliminary action ensures that even if some streams are lost, the original data can be recovered from the remaining streams through inverse transformation, thereby improving reliability while maintaining manageable data portions.
Solution Approach 2:
The patent changes the mathematical parameters of the data through Galois field operations and generator matrix transformations. By operating in Galois fields and using specific matrix properties, the system creates transformed data streams that maintain the information content of the original data in a distributed manner, enabling recovery from partial data loss while keeping each stream manageable in size.
3Reliability
If additional redundant data streams are created to aid in future recovery, then data recovery reliability is improved, but data processing complexity and storage requirements worsen
Solution Approach 1:
The patent replaces complex mechanical or procedural redundancy methods with mathematical transformations based on Galois field operations and generator matrices. This substitution creates redundant data streams through efficient mathematical computations rather than duplicating or mechanically replicating data, reducing processing complexity while maintaining high recovery reliability.
Solution Approach 2:
By changing the mathematical representation of data through Galois field operations and generator matrix transformations, the system creates redundant information in an efficient manner. The parameter changes enable the generation of multiple redundant streams from the original data without requiring simple duplication, thereby improving recovery reliability while controlling processing and storage complexity.
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.