Automated Role Assignment via Probability Optimization
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Solution Overview
Problem
The complexity of role-based access control in computing systems, particularly when migrating from an existing security model to a new environment, leads to labor-intensive and error-prone manual assignment of permissions and roles, resulting in system performance degradation and user experience issues.
Innovation Solution
An automated system that generates roles based on existing user permissions and information, using an objective function to minimize penalties and optimize role assignments, iteratively improving the role assignment matrix through techniques like simulated annealing and genetic algorithms to create a maintainable and efficient role-based access control security model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual assignment of roles, permissions and policies is used, then flexibility and control are maintained, but labor intensity and error rate increase significantly
Solution Approach 1:
The system performs self-service by automatically generating role assignments using an objective function that evaluates existing user permissions and information. The automated role assigner generates role probability matrices and iteratively improves assignments without human intervention, allowing the system to service itself rather than requiring manual human assignment for each user-role pairing.
Solution Approach 2:
The patent replaces the mechanical manual assignment process with an automated computational system. The objective function and iterative optimization algorithms substitute human operators, using mathematical models to evaluate and improve role assignments automatically, thereby eliminating the labor-intensive nature of manual permission management.
2Productivity
If automated role assignment is implemented, then productivity and accuracy improve, but system complexity increases
Solution Approach 1:
The patent segments the role assignment problem into distinct computational components: an objective function that evaluates assignments, a role probability matrix that stores assignment likelihoods, and an iterative optimization process. This segmentation allows each component to be independently developed and optimized, managing overall system complexity through modular architecture.
Solution Approach 2:
The system uses parameter changes by modifying the role probability matrix through iterative optimization. The objective function evaluates different parameter configurations and guides the system toward optimal role assignments by adjusting probability values, allowing the system to adapt and improve assignments dynamically without requiring complete reconfiguration.
3Reliability
If comprehensive permission repositories are maintained, then security and accuracy are improved, but complexity and migration difficulty increase
Solution Approach 1:
The patent creates a new permission repository structure by copying and transforming existing user permission data. The automated role assigner generates role probability matrices that replicate the essential security relationships from the old system while organizing them in a new, more manageable structure, allowing migration without losing security integrity.
Solution Approach 2:
The system performs preliminary action by pre-generating role probability matrices and evaluating multiple assignment configurations before finalizing the role repository structure. This preliminary optimization ensures that the new permission repository is properly configured from the start, avoiding the need for extensive post-migration adjustments and reducing overall complexity.
Data Source
AI summary
A first probability that indicates a probability that a user is associated with a role is determined, with the first probability having a first score. The first probability is perturbed to determine a second probability having a second score. The second score is evaluated against the first score to determine that the second probability indicates a more optimal probability of the user being associated with the role than the first probability. The role is assigned to the user based at least in part on the second score.