Data Stream Encoding for Recoverable and Confidential 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, often failing to safeguard against unauthorized access and data loss.
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 in any single stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If plaintext data is stored and transmitted in a readily accessible manner, then ease of operation is improved, but security and protection from unauthorized access deteriorates
Solution Approach 1:
The patent segments plaintext data into multiple separate data streams using encoding techniques. Each stream contains only a portion of the original data and is meaningless alone, providing security while maintaining operational accessibility through authorized reconstruction of the complete data set from multiple streams.
2Ease of manufacture
If data is divided into smaller portions for transmission and storage, then ease of manufacture and handling is improved, but data recovery capability deteriorates when portions are lost
Solution Approach 1:
The patent applies parameter changes by using error correction coding techniques that modify the data structure to include redundant information. This allows the system to maintain ease of handling divided data portions while improving recovery capability through mathematical reconstruction algorithms that can retrieve original data even when some portions are lost.
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.