ABAC Lab: An Interactive Platform for Attribute-based Access Control Policy Analysis, Tools, and Datasets

• URL: http://arxiv.org/abs/2505.08209v1
• Date: Tue, 13 May 2025 03:53:19 GMT
• Title: ABAC Lab: An Interactive Platform for Attribute-based Access Control Policy Analysis, Tools, and Datasets
• Authors: Thang Bui, Anthony Matricia, Emily Contreras, Ryan Mauvais, Luis Medina, Israel Serrano,
• Abstract summary: Attribute-Based Access Control (ABAC) provides expressiveness and flexibility, making it a compelling model for enforcing fine-grained access control policies.<n>To facilitate the transition to ABAC, extensive research has been conducted to develop methodologies, frameworks, and tools that assist policy administrators in adapting the model.<n>This paper introduces ABAC Lab, an interactive platform that integrates existing ABAC policy datasets with analytical tools for policy evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attribute-Based Access Control (ABAC) provides expressiveness and flexibility, making it a compelling model for enforcing fine-grained access control policies. To facilitate the transition to ABAC, extensive research has been conducted to develop methodologies, frameworks, and tools that assist policy administrators in adapting the model. Despite these efforts, challenges remain in the availability and benchmarking of ABAC datasets. Specifically, there is a lack of clarity on how datasets can be systematically acquired, no standardized benchmarking practices to evaluate existing methodologies and their effectiveness, and limited access to real-world datasets suitable for policy analysis and testing. This paper introduces ABAC Lab, an interactive platform that addresses these challenges by integrating existing ABAC policy datasets with analytical tools for policy evaluation. Additionally, we present two new ABAC datasets derived from real-world case studies. ABAC Lab serves as a valuable resource for both researchers studying ABAC policies and policy administrators seeking to adopt ABAC within their organizations. By offering an environment for dataset exploration and policy analysis, ABAC Lab facilitates research, aids policy administrators in transitioning to ABAC, and promotes a more structured approach to ABAC policy evaluation and development.

Related papers

• Adapting Vision-Language Models Without Labels: A Comprehensive Survey [74.17944178027015]
  Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks.<n>Recent research has increasingly focused on unsupervised adaptation methods that do not rely on labeled data.<n>We propose a taxonomy based on the availability and nature of unlabeled visual data, categorizing existing approaches into four key paradigms.
  arXiv  Detail & Related papers  (2025-08-07T16:27:37Z)
• An Approach for Handling Missing Attribute Values in Attribute-Based Access Control Policy Mining [0.0]
  Attribute-Based Access Control (ABAC) enables highly expressive and flexible access decisions by considering a wide range of contextual attributes.<n>Algorithms that mine ABAC policies from legacy access control systems can significantly reduce the costs associated with migrating to ABAC.<n>This paper introduces an approach that enhances the policy mining process by predicting or inferring missing attribute values.
  arXiv  Detail & Related papers  (2025-05-03T17:34:46Z)
• BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping [64.8477128397529]
  We propose a training-required and training-free test-time adaptation framework. We maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples. We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets.
  arXiv  Detail & Related papers  (2024-10-20T15:58:43Z)
• UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework [59.428668614618914]
  We take a deeper look into the diverse factors that influence the efficacy of modern unsupervised domain adaptation (UDA) methods. To facilitate our analysis, we first develop UDA-Bench, a novel PyTorch framework that standardizes training and evaluation for domain adaptation.
  arXiv  Detail & Related papers  (2024-09-23T17:57:07Z)
• InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation [79.09622602860703]
  We introduce InsightBench, a benchmark dataset with three key features.<n>It consists of 100 datasets representing diverse business use cases such as finance and incident management.<n>Unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics.
  arXiv  Detail & Related papers  (2024-07-08T22:06:09Z)
• DePLOI: Applying NL2SQL to Synthesize and Audit Database Access Control [6.2859996652179]
  This paper introduces a new access control model called Intent-Based Access Control for Databases (IBAC-DB)<n>In IBAC-DB, access control policies are expressed using abstractions that scale to high numbers of database objects, and are traceable with respect to implementations.<n>This paper proposes DePLOI, a system leveraging access control-specific task decompositions to accurately synthesize and audit access control implementation from IBAC-DB abstractions.
  arXiv  Detail & Related papers  (2024-02-11T23:50:12Z)
• When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective [64.73162159837956]
  evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset. Our empirical analysis of DataCOPE in the logged contextual bandit settings using healthcare datasets confirms its ability to evaluate both machine-learning and human expert policies.
  arXiv  Detail & Related papers  (2023-11-23T17:13:37Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis [96.53859361560505]
  Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. There always exists severe domain shift between the pretraining and downstream ABSA datasets. We introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2021-10-26T04:03:45Z)
• Adaptive ABAC Policy Learning: A Reinforcement Learning Approach [2.5997274006052544]
  We propose an adaptive ABAC policy learning approach to automate the authorization management task. In particular, we propose a contextual bandit system, in which an authorization engine adapts an ABAC model through a feedback control loop. We focus on developing an adaptive ABAC policy learning model for a home IoT environment as a running example.
  arXiv  Detail & Related papers  (2021-05-18T15:18:02Z)
• Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values [0.6662800021628273]
  This paper presents the first algorithms for mining ABAC and ReBAC policies from access control lists (ACLs) and incomplete information about entities. We show that the core of this problem can be viewed as learning a concise three-valued logic formula from a set of labeled feature vectors containing unknowns.
  arXiv  Detail & Related papers  (2020-08-19T13:56:29Z)
• An Automatic Attribute Based Access Control Policy Extraction from Access Logs [5.142415132534397]
  An attribute-based access control (ABAC) model provides a more flexible approach for addressing the authorization needs of complex and dynamic systems. We present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process.
  arXiv  Detail & Related papers  (2020-03-16T15:08:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.