Feature-Level Access Control for Distributed Data Visualization
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Solution Overview
Problem
Current data visualization and analysis platforms lack granular control over feature-level access, making it difficult to restrict user access to specific applications and data sets, often requiring multiple instances of the platform to manage discrete data sets securely.
Innovation Solution
The system provides a method for configuring feature-level access on a per-role basis, allowing administrators to assign different access levels (no access, read-only, or full access) to features within a user interface, and automatically controlling access based on user roles, enabling secure and organized data visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If granular feature-level access control is implemented, then data security and organization are improved, but system complexity and configuration difficulty increase
Solution Approach 1:
The system segments access control into feature-level granularity, allowing individual features to be controlled independently through feature controls objects. Each feature control object contains specific attributes (read, write, delete, execute) that can be independently configured, enabling precise security control without managing multiple platform instances. This segmentation resolves the contradiction by providing fine-grained security (improving reliability) while maintaining a unified system architecture (avoiding excessive complexity).
Solution Approach 2:
The patent introduces a new dimension of control by adding feature-level access control attributes to the existing role-based access control framework. Instead of only controlling access at the platform or dataset level, the system now controls access at the individual feature level within each dataset. This dimensional expansion enables sophisticated security policies without requiring multiple system instances, thus improving data security while managing complexity through a structured extension of the existing framework.
2Reliability
If multiple platform instances are used to manage discrete data sets securely, then data security is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent merges multiple security control functions into a single platform instance by implementing feature-level access control within the existing role-based framework. Instead of requiring separate platform instances for different data sets and security requirements, the system combines multiple datasets with different security policies into one unified platform. The feature controls objects enable each dataset to have its own security configuration, achieving the security isolation of multiple instances while maintaining the resource efficiency of a single instance.
3Measurement precision
If feature-level access control is configured manually for each feature, then access precision is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system implements preliminary action by providing default feature control configurations that are automatically applied when datasets are created or imported. These default configurations establish baseline security policies for common scenarios, eliminating the need for manual configuration of every feature. Administrators can then selectively modify only those features that require customized security policies, significantly reducing configuration time while maintaining precise access control where needed.
Solution Approach 2:
The patent enables efficient configuration through parameter changes by allowing administrators to modify access control attributes (read, write, delete, execute) for features in bulk or through automated policies. The system supports dynamic updates to feature controls objects, enabling security policies to be adjusted without reconfiguring the entire system. This parameter-based approach allows precise access control to be implemented and modified efficiently, reducing the time investment required compared to manual feature-by-feature configuration.
Data Source
AI summary
Methods and systems for providing configurable feature level controls for data. The data can be associated with data visualization and analysis in a distributed search engine environment. An example method comprises providing a user interface for enabling a selection of a type of access to grant for each feature of a plurality of features, the selection being on a feature-by-feature basis and the selection being assigned to selected roles; and in response to the selection of the type of access, automatically controlling the type of access to each of the features including determining whether a user has any role to which a particular feature has been assigned; and based on the determining, for users having any of the selected roles, permitting the type of access selected for the particular feature assigned to the selected roles. The types of access may comprise read-only, full, no access, or differing levels of access.