Encrypted Data Compression with Hardware Resource Adaptation
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Solution Overview
Problem
The rapid growth of data storage demand is outpacing the capacity to store it, with existing data compression methods being inadequate for multi-media data and transmission bandwidth becoming a bottleneck, especially in applications like the 'Internet of Things'.
Innovation Solution
A system and method for encrypted data compression using asymmetric encoding/decoding and genomic encryption, incorporating a hardware management layer to optimize resource utilization and apply data manipulation rules during decoding, along with Burrow's-Wheeler transform (BWT) for enhanced compression and encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency improves, but compression ratio decreases for multi-media data
Solution Approach 1:
The patent segments data into fixed-size blocks and processes them independently through encoding/decoding operations. Each block is treated as a discrete unit that can be transformed and stored efficiently, enabling systematic compression while maintaining the ability to handle diverse data types including multi-media content.
Solution Approach 2:
The patent implements asymmetric encoding/decoding where the encoding process transforms data into a compact form using encoding rules, while the decoding process reconstructs original data using corresponding decoding rules. This asymmetry enables efficient compression ratios while maintaining data integrity across different data types.
2Quantity of substance
If additional physical storage capacity is added to meet demand, then storage availability improves, but manufacturing cost increases
Solution Approach 1:
The patent applies compression algorithms and encoding rules in advance to reduce the volume of data before storage. By pre-processing data to eliminate redundancy and compact representation, the system reduces the physical storage capacity needed to meet future data demands, avoiding the need for additional manufacturing capacity.
3Speed
If data transmission bandwidth is increased to handle large data sets, then transmission speed improves, but network infrastructure cost increases
Solution Approach 1:
The patent applies compression and encoding to data sets before transmission, reducing the volume of data that needs to be transmitted across the network. This pre-processing enables faster transmission speeds by decreasing the total data volume, thereby reducing the bandwidth infrastructure requirements and associated costs.
4Reliability
If lossless compression is applied to retain all original data, then data integrity improves, but storage efficiency decreases
Solution Approach 1:
The patent segments data into blocks and applies encoding/decoding transformations that preserve all original information within each block. This segmentation approach enables lossless compression by maintaining complete data integrity while achieving efficient storage through systematic transformation and compact representation of each data block.
Solution Approach 2:
The patent creates transformed copies of original data through encoding rules that preserve all information. The encoded data serves as a compact representation that can be fully reconstructed into the original data, achieving both storage efficiency and complete data integrity through reversible transformation.
5Quantity of substance
If lossy compression is applied to increase storage efficiency, then storage capacity improves, but data quality deteriorates
Solution Approach 1:
The patent applies encoding transformations in advance that preserve all original data information. By performing lossless encoding before storage, the system achieves storage efficiency through compact representation while maintaining complete data quality, avoiding the need for lossy compression techniques.
Solution Approach 2:
The patent creates exact transformed copies of original data through reversible encoding/decoding processes. These encoded copies maintain full data fidelity while occupying less storage space, eliminating the trade-off between storage efficiency and data quality that characterizes lossy compression methods.
Data Source
AI summary
A system and method for encrypted data compression with hardware management combines data compression techniques with real-time hardware optimization. The system receives an input data stream comprising data blocks, analyzes frequency distributions within the stream, and generates a conditioned data stream by identifying portions that deviate from target frequency distributions and applying conditioning rules. A hardware management layer continuously monitors the system resource utilization including processing latency, memory consumption, and field-programmable gate array (FPGA) resource usage during data processing operations. When resource utilization exceeds performance thresholds, the system generates hardware optimization plans and implements configuration changes including memory reallocation, processor adjustments, and FPGA resource management. The system applies Burrow's-Wheeler transforms (BWT) to improve data compressibility and creates error streams through XOR operations between original and conditioned data. The hardware management layer enables dynamic adaptation to changing processing demands while maintaining optimal system performance for data compression operations.