Edge Computing Data Transformation Rules Architecture
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Solution Overview
Problem
Enterprise organizations face challenges in securely processing large data sets while maintaining the secrecy of proprietary data transformation rules, as existing methods involve substantial manual operations and risk exposure during electronic data transmission.
Innovation Solution
A system comprising a central computing node and edge computing nodes, where the central node determines an edge node for data processing, applies machine learning algorithms to reverse-engineer data transformation rules, and securely stores them at the edge nodes, limiting access and enhancing security through a zero-knowledge proof system and distributed architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data transformation rules are stored and processed at a central computing node, then data processing can be performed, but the risk of exposure during electronic data transmission increases and security is compromised
Solution Approach 1:
The system segments the data processing architecture into a central computing node that holds the data set and multiple edge computing nodes that hold data transformation rules. This segmentation separates sensitive data from transformation logic, allowing rules to be applied at the edge without transmitting the actual data, thereby reducing exposure risk during transmission.
Solution Approach 2:
The patent introduces an intermediary mechanism where data transformation rules are transmitted to edge computing nodes instead of transmitting the actual data set. This intermediary approach allows the data to be processed indirectly through rules, eliminating the need to expose the data during transmission while still achieving the transformation objective.
2Productivity
If proprietary data transformation rules are made accessible for processing, then data processing can be performed, but access control becomes more difficult and secrecy is compromised
Solution Approach 1:
The system segments access rights by separating the data set (held at the central node) from the data transformation rules (held at edge nodes). This segmentation enables controlled access where different entities can have access to different components without needing access to both, thereby maintaining secrecy while enabling processing capability.
Solution Approach 2:
The patent uses an intermediary approach where data transformation rules act as intermediaries that can be applied to data without requiring direct access to the data itself. This allows proprietary rules to be utilized for processing while maintaining strict access control, as the rules can be transmitted and executed at edge nodes without exposing the underlying data or requiring centralized access.
3Ease of manufacture
If manual operations are used for data processing, then data transformation can be performed, but substantial manual oversight is required and efficiency is reduced
Solution Approach 1:
The system implements self-service automation where data transformation rules are automatically applied to the data set at edge computing nodes without requiring manual intervention. The central computing node automatically transmits rules to appropriate edge nodes, and the edge nodes autonomously execute the transformations, eliminating the need for substantial manual oversight and significantly improving processing efficiency.
Data Source
AI summary
A system for data processing using machine learning processing and distributed architecture is described. Specifically, proprietary data transformation rules to be applied for the data processing may be stored at edge computing devices, while the bulk of data processing may be performed at a central computing node that houses the databases. A subset of a data set, in the database, may be sent from the central computing node to the edge computing node. The edge computing node may generate a second data set based on applying data transformation rules to the subset of the data set. The central computing node may determine, using a machine learning (ML) algorithm and based on the subset of the data set and the second data set, the data transformation rules, which may then be applied to the rest of the data set.