Generator-Matrix Data Coding for Recoverable Secure Streams
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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 that the original data can be recovered from any subset of the streams, while maintaining confidentiality by not storing plaintext directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If plaintext data is stored and handled in a readily available manner, then ease of operation is improved, but security and protection against unauthorized access deteriorates
Solution Approach 1:
The patent segments plaintext data into multiple separate data streams through transformation processes. The original data is divided into W data inputs that are then transformed into H output data streams, where no single stream contains the complete plaintext. This segmentation ensures that even if some streams are accessed unauthorizedly, the complete plaintext remains protected.
Solution Approach 2:
The patent introduces transformed data streams as intermediaries between the original plaintext and storage/transmission media. These intermediate streams serve as mediators that protect the plaintext while still enabling data access and recovery functions. The transformation process creates these intermediary representations that maintain utility while enhancing security.
2Ease of operation
If data is divided into smaller portions for transmission and storage, then ease of operation is improved, but ability to recover complete data deteriorates when loss occurs
Solution Approach 1:
The patent performs preliminary transformation actions on the data before division and distribution. By applying transformation processes that create redundant and interrelated data streams in advance, the system ensures that recovery capability is built into the structure before any data loss occurs. This preliminary action of transformation creates the mathematical relationships necessary for later recovery.
Solution Approach 2:
The patent changes the parameters of the data representation through mathematical transformation. The original data parameters are transformed into new parameters that exist in multiple streams with specific mathematical relationships. This parameter transformation enables the system to maintain recovery capability while distributing data across multiple portions, as the transformed parameters contain embedded redundancy and interrelationships.
3Reliability
If additional streams with redundant data are created to aid recovery, then reliability is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent creates data streams that serve multiple functions simultaneously. The transformed streams provide both data transmission/c storage functionality and data recovery functionality. The same H output streams that are used for normal data operations also serve as the recovery mechanism, eliminating the need for separate dedicated recovery structures and reducing overall system complexity.
Solution Approach 2:
The patent uses parameter changes through mathematical transformation to create redundancy efficiently. By transforming the original data parameters into new parameters that exist in H streams, the system creates redundant information without simply duplicating data. The mathematical relationships between transformed parameters provide the redundancy needed for recovery while maintaining compact and efficient data structures.
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.