Personal Health Data Compaction Using Multiple Encoding Libraries
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Solution Overview
Problem
Current data encoding methods for personal health information use a single encoding algorithm, which compromises security and does not allow for maximum encoding compaction, creating vulnerabilities and potential privacy breaches.
Innovation Solution
A system and method that utilizes multiple encoding libraries to encode different portions of personal health data, selecting the most compacting library for each portion, and optionally rotates or shuffles codebooks for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single encoding algorithm is used for all data, then the decoding process is simple and requires only one algorithm, but the data compaction is not maximized and security is compromised
Solution Approach 1:
The patent divides the data into multiple portions and applies different encoding algorithms to each portion. Each encoded portion is associated with an identifier indicating which algorithm was used. This segmentation allows the system to achieve both security (through multiple algorithms) and decoding simplicity (by using identifiers to guide the decoding process)
Solution Approach 2:
Different portions of the data are encoded with different algorithms based on their specific characteristics. The system selects the most appropriate algorithm for each local portion, optimizing compaction for that specific data while maintaining overall security through the diversity of algorithms used across the entire dataset
2Ease of manufacture
If a single encoding algorithm is used for all data, then the encoding process is simple and consistent, but maximum encoding compaction cannot be achieved
Solution Approach 1:
The system changes the encoding parameter (algorithm selection) based on the characteristics of different data portions. By evaluating multiple algorithms and selecting the one that provides the greatest compaction for each portion, the system achieves maximum overall compaction while maintaining encoding consistency through systematic selection criteria
Solution Approach 2:
The encoding approach is made dynamic by selecting different algorithms for different data portions rather than using a static single algorithm for all data. This dynamic selection allows the system to adapt to the specific characteristics of each data portion, achieving optimal compaction ratios
3Device complexity
If the same encoding algorithm is used across large sets of files, then the encoding process is uniform and manageable, but security vulnerabilities arise because all data can be decoded using a single algorithm
Solution Approach 1:
The patent segments the encoding process by applying different algorithms to different portions of data. Each segment is encoded independently with a specific algorithm, and identifiers are attached to track which algorithm was used. This segmentation eliminates the security vulnerability of using a single algorithm across all files while keeping management straightforward through the identifier system
Solution Approach 2:
The system uses a composite encoding approach, combining multiple encoding algorithms into a unified encoding scheme. Rather than relying on a single algorithm, the system creates a composite encoding structure where different algorithms work together, providing both security through diversity and manageability through the identifier-based organization
Data Source
AI summary
Health monitor data is encoded using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but also provides substantial additional security in that multiple decoding libraries must be used to decode the data. Optionally, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data.