Generator-Matrix Data Recovery from Redundant Confidential Streams
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Solution Overview
Problem
Existing systems for handling and processing plain text data lack effective redundancy mechanisms for data recovery and fail to protect the data from unauthorized access, leading to vulnerabilities in data integrity and security.
Innovation Solution
The method involves transforming input data using Galois field operations and a generator matrix to create 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 the original text in any single stream, thus providing redundancy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing systems store and handle plain text data in a readily accessible manner, then data accessibility is improved, but data security deteriorates as the plain text is exposed to unauthorized viewing
Solution Approach 1:
The system segments the original plain text data into multiple separate data streams through transformation. Each stream contains a portion of the transformed data, but none of the streams individually reveal the original plain text. This segmentation allows the data to be stored and accessed in multiple locations while maintaining security, as unauthorized access to any single stream does not compromise the original data.
Solution Approach 2:
The system introduces transformed data as an intermediary between the original plain text and its storage/transmission. This intermediary form protects the original data by converting it into a secure representation that cannot be easily reverse-engineered, while still allowing authorized recovery of the original text through the use of recovery information.
2Device complexity
If existing systems provide limited data recovery capability, then system simplicity is maintained, but data recovery reliability deteriorates when loss occurs
Solution Approach 1:
The system performs preliminary transformation of the original data into multiple redundant streams before storage or transmission. This preliminary action ensures that even if some streams are lost or corrupted, the original data can be recovered by combining sufficient streams. The transformation is performed in advance, so no complex real-time processing is needed during recovery.
Solution Approach 2:
The system changes the parameters of the data representation by transforming the original plain text into a different mathematical form using transformation information. This parameter change allows the data to be represented in multiple equivalent forms, providing redundancy while maintaining the ability to recover the original data through mathematical operations.
3Quantity of substance
If no redundancy mechanisms are implemented, then storage efficiency is improved, but data integrity deteriorates when streams are lost
Solution Approach 1:
The system uses parameter changes by transforming the original data into a mathematical representation that inherently provides redundancy. The transformation information and recovery information work together to ensure that the original data can be reconstructed even if some transformed streams are lost. This approach provides data integrity protection without requiring additional storage beyond the transformed streams themselves.
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 streams from W data input streams produced from input data. Moreover, the method may include generating the H discrete output data streams 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 streams.