Data Pool Management System Rule-Based Authorization
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Solution Overview
Problem
In distributed systems, entities often lack sufficient data for accurate data representation, leading to unreliable computations and wastage of computing resources due to insufficient quantity and quality of data, as well as restrictions on data sharing from originating entities and regulatory constraints.
Innovation Solution
A data pool management system (DPMS) is implemented to manage access to a collection of data from multiple entities, using rule sets for authorization based on entity-level, group-level, and system-level access rules, ensuring that only authorized data sets are used for computations, and previously computed data representations are reused to conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities share more data to improve data representation accuracy, then data quality and quantity improve, but data security and privacy risks increase
Solution Approach 1:
The patent introduces a data pool management system as an intermediary between data providers and data consumers. This mediator collects data from multiple entities, applies rule-based access controls, and distributes authorized data for computations, thereby enabling accurate data representations while maintaining security and privacy through centralized governance.
Solution Approach 2:
The patent segments data access rights into different levels (entity-level, group-level, system-level) and types (read access, write access, delete access). This segmentation allows fine-grained control over data sharing, enabling entities to share data for specific purposes while maintaining security boundaries and protecting sensitive information.
2Object-affected harmful factors
If entities restrict data sharing to maintain security and privacy, then data security improves, but data representation accuracy deteriorates
Solution Approach 1:
The patent creates a universal data pool that serves multiple functions: storing data from various entities, applying different access rules for different users, enabling secure data sharing across organizational boundaries, and supporting diverse computational tasks. This multi-functional system allows restricted yet controlled data sharing that maintains security while improving data representation accuracy.
3Productivity
If computing resources are deployed for computations with insufficient data, then computational work is performed, but resource efficiency decreases due to wasted computations
Solution Approach 1:
The patent performs preliminary actions by collecting and validating data in a centralized pool before computations are initiated. The system checks data availability, quality, and access authorization in advance, ensuring that computational resources are only deployed when sufficient authorized data is available, thereby preventing wasted computations and improving resource efficiency.
4Object-affected harmful factors
If complex rule sets are implemented for data access authorization, then data security control improves, but system complexity increases
Solution Approach 1:
The patent adds a new dimension to data access control by implementing a hierarchical rule system with multiple levels (entity-level, group-level, system-level) and multiple rule types (allow rules, deny rules, default rules). This multi-dimensional approach organizes complex authorization logic in a structured manner, improving security control while making the system more manageable through clear hierarchical boundaries.
Data Source
AI summary
Techniques are described for pooling data originating from different entities into a data pool managed by a data pool management system for performing accurate and resource-efficient statistical and other data operations by entities. Techniques further include maintaining rule sets that govern access to the data sets of the data pool. The DPMS uses the rule sets to determine whether a particular data set, on which a particular operation is requested to be performed, qualifies as authorized data for the requesting entity. In an embodiment, the DPMS determines, based on one rule set, that the particular data set does not qualify as authorized data for the particular operation. The DPMS further determines that based on another rule set the particular data set does qualify as authorized data for the particular operation. Based on determining that authorizing rule set overrides the non-authorizing rule set, DPMS proceeds to performing the particular operation using the particular data set.