Hashing Medical Binary Decision Diagrams for Data Integrity
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Solution Overview
Problem
Current sensor networks and binary decision diagrams face challenges in efficiently monitoring and analyzing health data, particularly in detecting sensor malfunctions and data corruption, and optimizing data storage and processing for healthcare applications.
Innovation Solution
The integration of sensor networks with binary decision diagrams (BDDs) for data analysis, including methods for combining, partitioning, and compressing BDDs to optimize analysis efficiency and storage, as well as using hashing techniques for data integrity checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor networks are used to monitor health data, then monitoring capability is improved, but data corruption and sensor malfunction detection becomes difficult
Solution Approach 1:
The patent applies preliminary action by computing hash values of BDD representations of sensor data immediately when data is collected and stored. This proactive approach ensures data integrity is verified before potential corruption occurs, allowing the system to detect anomalies and malfunctions early without compromising the automated monitoring capability.
2Loss of substance
If binary decision diagrams are used to represent sensor data, then data compression is improved, but detection of data corruption becomes more complex
Solution Approach 1:
The patent introduces hash values as an intermediary mechanism that works alongside BDD representations. The hash values serve as a simple verification layer that does not complicate the BDD structure but enables efficient corruption detection by providing a quick integrity check that operates independently of the complex BDD format.
3Reliability
If hashing techniques are applied to BDDs, then data integrity is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by computing hash values only for critical portions of sensor data that are most susceptible to corruption or most important for health monitoring decisions. This selective hashing approach maintains data integrity for key parameters while avoiding the computational overhead of hashing entire datasets, thus minimizing processing time impact.
Data Source
AI summary
In particular embodiments, a method includes receiving from a remote system a binary decision diagram (BDD) representing data streams from sensors, an input, and a first hash code, transforming the received BDD to a second arithmetic function by performing the arithmetic transformation on the received BDD, calculating a second hash code from the second arithmetic function and the input, and if the first hash code equals the second hash code, then indicating that the received BDD is uncorrupted data, else indicating that the received BDD is corrupted data.