Recoverable Data Transformation Using Redundant Secure Output Streams
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Solution Overview
Problem
Existing data processing systems lack effective redundancy safeguards and protection mechanisms for plaintext data, making them vulnerable to unauthorized access and data loss.
Innovation Solution
Transform input data using Galois field operations and a generator matrix to create a greater number of output data streams, ensuring data redundancy and confidentiality, allowing recovery from any subset of these streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If plaintext data is stored and transmitted in readily available format, then data accessibility and processing efficiency are improved, but data security and protection from unauthorized access deteriorate
Solution Approach 1:
The plaintext data is divided into multiple segments or shards, where each segment alone is insufficient to reconstruct the original data. This segmentation approach allows the system to maintain data accessibility by distributing segments across multiple locations while improving security, as unauthorized access to individual segments does not reveal the complete plaintext information.
Solution Approach 2:
An intermediary transformation process using Galois field operations and generator matrices is introduced between the plaintext data and its stored form. This intermediary step converts plaintext into transformed data streams that are mathematically related but not directly readable, thereby protecting the original data while enabling recovery through the inverse transformation process.
2Reliability
If data is transformed using Galois field operations and generator matrices to create redundant streams, then data security and recovery capability are improved, but system complexity and computational overhead increase
Solution Approach 1:
The system changes the mathematical parameters and representation of the data by applying Galois field operations and generator matrices. This parameter transformation converts plaintext into a different mathematical domain where redundancy is inherently built into the structure, enabling robust recovery capability while the mathematical framework provides a systematic approach to managing the complexity.
Solution Approach 2:
The transformation system using Galois field operations serves multiple functions simultaneously: it provides data encryption, creates redundant streams for error correction, enables flexible recovery from various subsets of streams, and maintains mathematical relationships that facilitate efficient processing. This multi-functionality justifies the system complexity by delivering comprehensive data protection and recovery capabilities.
3Reliability
If multiple output data streams are generated from input data, then data redundancy and protection are improved, but storage requirements and data volume increase
Solution Approach 1:
The system generates multiple output data streams where some streams can be discarded or lost without compromising data recovery. The mathematical structure ensures that a sufficient subset of the generated streams contains all necessary information to reconstruct the original plaintext, allowing the system to tolerate loss of certain streams while maintaining data protection.
Solution Approach 2:
The transformation process generates more output streams than the minimum theoretically required, providing an excessive amount of redundant data. This partial redundancy approach ensures that even if some streams are lost or corrupted, there are sufficient remaining streams for recovery, while the Galois field operations ensure the redundancy is efficiently encoded rather than simply duplicating data.
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.