Multi-Layer Data Compaction with Selective Encryption Policies
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity of physical storage devices, and the limitations imposed by transmission bandwidth are creating a bottleneck in data management, particularly with the increasing use of multimedia data.
Innovation Solution
A system and method for highly efficient data encoding that includes asymmetric encoding/decoding, distributed computing policy enforcement, and multi-layer encoding with selective encryption, allowing for secure, compact, and efficient data processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency is improved, but data loss occurs with lossy compression or compression ratio is limited to 2:1 with lossless compression
Solution Approach 1:
The patent segments data into multiple layers (e.g., metadata layer, content layer, sensitive data layer) and applies different processing techniques to each layer. This allows selective compression and encryption of specific layers while preserving critical data integrity, achieving both high compression ratios and data protection without the limitations of traditional single-layer compression methods.
2Quantity of substance
If physical storage capacity is increased to meet growing data demand, then storage capacity is improved, but the bottleneck remains as storage demand outstrips manufacturing capacity
Solution Approach 1:
The patent creates a composite data structure that combines compressed data, encryption layers, and metadata into a unified format. This composite approach enables efficient storage and retrieval while maximizing the utilization of available physical storage capacity, effectively increasing usable storage without requiring additional physical hardware.
3Speed
If transmission bandwidth is increased to handle large data sets, then data transmission capability is improved, but transmission bandwidth becomes increasingly bottlenecked
Solution Approach 1:
The patent performs preliminary compression and layering of data before transmission. By pre-processing data into a compact multi-layer format at the source, the system reduces the amount of data that needs to be transmitted over the network, thereby improving transmission speed and reducing bandwidth requirements without compromising data integrity or security.
4Reliability
If traditional encoding methods are used for data security, then security is provided, but asymmetric encoding/decoding with data manipulation rules provides double security
Solution Approach 1:
The patent adds an additional dimension to traditional encoding by implementing asymmetric encoding/decoding with data manipulation rules. This creates a two-dimensional security model where data is encoded with one set of rules and requires a different set of rules for decoding and manipulation, providing layered security that protects against both interception and unauthorized modification while maintaining manageable complexity through systematic rule application.
Data Source
AI summary
A system and method for multi-layer data processing with selective encryption and transformation. The system generates a multi-layer data structure comprising reference elements derived from input data separated into multiple layers. Data processors receive and process input data using this structure, correlating input elements with reference elements. The system selectively encrypts one or more layers based on predefined encryption policies and applies transformation rules to processed data elements as specified in the structure. The output is a sequence of transformed, untransformed, and selectively encrypted elements. The system includes an encryption policy subsystem for managing layer-specific policies, a key management subsystem for handling encryption keys, and an enhanced decoder capable of processing both encrypted and non-encrypted data streams. Encryption algorithms and strengths can vary based on data sensitivity, with dynamically adjustable policies responsive to factors such as data content, user permissions, and network conditions.