Dyadic Data Compression and Encryption for Drift-Adaptive Storage
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity of physical storage solutions and transmission bandwidth, coupled with security concerns as quantum computing approaches, necessitates a new approach for efficient data storage and transmission that supports automated system efficacy monitoring and model training.
Innovation Solution
A system and method utilizing statistical analyses to determine the probability distribution of datasets, retraining encoding and decoding algorithms, and integrating a dyadic distribution-based algorithm for simultaneous compression and encryption, enabling efficient data storage, transmission, and security on existing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional physical storage capacity is added, then storage demand can be met, but manufacturing capacity is insufficient and costs increase
Solution Approach 1:
The patent transforms data from its original form into a different parameter representation through mathematical encoding and compression algorithms, achieving dramatically higher storage density without requiring proportional increases in physical manufacturing capacity
Solution Approach 2:
The system creates highly compressed digital representations of data that can be stored and replicated efficiently, using mathematical models to capture essential information in minimal space, thereby overcoming physical storage limitations
2Quantity of substance
If data compression is applied, then storage capacity is doubled, but data degradation occurs or compression ratios are insufficient for multi-media data
Solution Approach 1:
The patent applies advanced parameter transformation through mathematical encoding that preserves data integrity while achieving superior compression ratios, using probabilistic models to maintain information fidelity across different data types including multi-media
Solution Approach 2:
The compression system is designed to handle multiple data types (text, audio, video, images) with a unified algorithmic approach that adapts to different data characteristics, achieving consistent high compression ratios without degradation across diverse multi-media formats
3Productivity
If transmission bandwidth is increased, then data transmission capacity improves, but bandwidth remains a bottleneck for large datasets
Solution Approach 1:
The patent transforms large volumes of data into highly compressed parameter representations before transmission, dramatically reducing the actual data volume that must traverse the network while preserving the ability to reconstruct the original information at the destination
4Reliability
If existing encryption technologies are used, then data security is provided, but security is compromised as quantum computing approaches
Solution Approach 1:
The patent employs mathematical encoding and compression algorithms that create inherently secure data representations, where the transformed parameter space provides natural resistance to decryption attempts and is adaptable to post-quantum cryptographic requirements
Data Source
AI summary
A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.