Multi-Algorithm Health Data Encoding for Secure Compaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data encoding methods for personal health information in wearable devices use a single encoding algorithm, which compromises security and compaction efficiency, as all data can be decoded using a single algorithm, leading to potential privacy breaches and health risks.
Innovation Solution
A system and method that employs multiple encoding libraries to encode personal health monitor data, where different portions of the data are encoded using various libraries based on compaction efficiency, and sourceblock sizes, and codebooks are randomly or pseudo-randomly rotated for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single encoding algorithm is used for all data, then decoding is simpler and requires only one algorithm, but data compaction efficiency is suboptimal and security is compromised
Solution Approach 1:
The patent divides the data into multiple segments or blocks, and applies different encoding algorithms to different segments. This segmentation allows the system to achieve both security (through algorithm diversity) and operational efficiency (by selecting algorithms based on data characteristics), directly resolving the contradiction between decoding simplicity and data security.
2Device complexity
If a single encoding algorithm is used for all data, then the encoding process is simpler, but data compaction efficiency is not maximized
Solution Approach 1:
The patent introduces dynamic algorithm selection where the encoding algorithm is chosen based on the characteristics of each data segment rather than using a static single algorithm for all data. This dynamic approach maximizes compaction efficiency while keeping the overall system manageable through automated selection processes.
Solution Approach 2:
The system changes the parameter of encoding algorithm selection based on data characteristics, transitioning from a fixed single-algorithm approach to a variable multi-algorithm approach. This allows optimization of compaction efficiency for different data types while maintaining reasonable system complexity through parameter-based adaptation.
3Stability of the object's composition
If the same encoding algorithm is used across large sets of files, then consistency is maintained, but security vulnerabilities arise as all data can be decoded with a single algorithm
Solution Approach 1:
The patent applies different encoding algorithms to different local segments of data rather than using a uniform algorithm across all data. This local differentiation maintains security by preventing single-algorithm decryption while still providing consistency within each encoded segment, effectively resolving the contradiction between encoding consistency and security vulnerability prevention.
Data Source
AI summary
A system and method for encoding personal health monitor data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction or on some other criteria for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, 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. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.