Role-Based Access Control (RBAC) is foundational to enterprise security, yet manual role engineering remains error-prone and unscalable. Although automated role mining addresses this, existing methods face a critical trade-off: exact approaches guarantee minimal roles but fail on real-world scales, while heuristics scale but lack formal guarantees. This inconsistency forces enterprises into suboptimal, insecure configurations—increasing vulnerability risks and compliance costs. We resolve this instability
through a four-level resource-aware framework that dynamically adapts: (1) a memory-light heuristic, (2) optimality-preserving reductions, (3) a greedy heuristic with logarithmic approximation bounds, and (4) an ILP-based exact solver. Notably, our approach eliminates more than 99% of edges in 26 out of 31 real-world systems, enabling globally optimal role configurations and achieving an average 53% simplification of existing RBAC systems. Our heuristics achieve near-optimal solutions, while providing significant speedups over prior heuristics. Beyond individual components, the unified, adaptive framework minimizes suboptimal decisions at any scale. We open-source this framework to enable minimal RBAC deployment at any scale.

In this paper, we tackle the problem of creating decision trees that are both optimal and individually fair. While decision trees are popular due to their interpretability, achieving optimality can be difficult. Existing approaches either lack scalability or fail to consider individual fairness. To address this, we define individual fairness as a separable optimization task by analyzing the fairness gained and lost within a sub-tree. Using the Streed framework, we implement an algorithm that constructs optimal decision trees with the lowest misclassification score and individual fairness value above a certain threshold. Our algorithm has been tested on various datasets, demonstrating its effectiveness and scalability. This research is a significant step towards creating fair decision trees that are optimal, fair, and scalable.