ML Tagging for Secure Data Synchronization
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Solution Overview
Problem
Current systems for collecting and distributing large datasets in the pharmaceutical industry face challenges in ensuring accurate and secure data segregation across multiple databases, leading to potential data processing errors and unauthorized access.
Innovation Solution
A computer-implemented method using machine learning tagging algorithms to assign tags to data in a central database, ensuring accurate segregation and secure synchronization of data to relevant individual databases based on biomedical named entity tags, utilizing a Biomed-AI framework with cloud-based storage and microservices for automated and intelligent data synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is segregated across multiple individual databases for security and privacy, then data security and privacy are improved, but data processing accuracy and synchronization reliability deteriorate due to increased risk of copying errors to wrong databases
Solution Approach 1:
The patent introduces a central database as an intermediary between data sources and individual databases. This central database acts as a controlled distribution point where data is standardized, tagged, and verified before being copied to individual databases, thereby reducing copying errors while maintaining security through controlled access.
Solution Approach 2:
The patent applies preliminary tagging and classification to data before it is distributed to individual databases. By pre-marking data with metadata indicating its intended destination and authorization level, the system ensures accurate routing and prevents accidental copying to wrong databases, thus improving data copying accuracy while maintaining segregation.
2Reliability
If manual tracking of data locations is used to ensure security, then data security can be monitored, but time consumption and operational efficiency deteriorate significantly
Solution Approach 1:
The patent replaces manual tracking mechanisms with automated electronic tagging and metadata systems. Data is automatically marked with destination identifiers and authorization tags during the copying process, enabling systematic tracking without human intervention. This substitution dramatically reduces time consumption while maintaining comprehensive security monitoring.
3Reliability
If one-way data flow from central database to individual databases is implemented, then data security against malicious access is improved, but system complexity increases due to need for automated tracking and verification
Solution Approach 1:
The patent implements self-service mechanisms where data automatically carries its own routing information and authorization tags. The tagging system embedded in the data structure enables the data to essentially guide its own distribution process, reducing the need for complex external tracking systems while maintaining security through automated verification of authorization tags.
Data Source
AI summary
A computer implemented method of synchronizing data between a central database and at least one individual database including the steps of: acquiring data from at least one external source; forming an intelligence dataset, wherein the intelligence dataset includes multiple intelligence datum; storing the intelligence dataset in a central database; applying a machine learning ML tagging algorithm to the stored dataset to assign at least one tag to each intelligence datum in the stored dataset, forming a tagged intelligence dataset including multiple tagged intelligence datum; and copying each tagged intelligence datum from the central database to at least one individual database based on the assigned tag.