CRCVLGOct 22, 2025

From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data

arXiv:2510.19418v11 citationsh-index: 10
Originality Incremental advance
AI Analysis

This work addresses the problem of secure data sharing with role-based permissions for organizations handling visual data, though it is incremental as it builds on existing encryption and detection methods.

The paper tackles the challenge of protecting sensitive information in large visual data repositories by proposing a system architecture for fine-grained access control, which integrates automated detection, encryption, and policy enforcement, resulting in improved detection metrics (5% F1 score and 10% mAP increase) and efficient decryption times under 1 second per image.

As the volume of stored data continues to grow, identifying and protecting sensitive information within large repositories becomes increasingly challenging, especially when shared with multiple users with different roles and permissions. This work presents a system architecture for trusted data sharing with policy-driven access control, enabling selective protection of sensitive regions while maintaining scalability. The proposed architecture integrates four core modules that combine automated detection of sensitive regions, post-correction, key management, and access control. Sensitive regions are secured using a hybrid scheme that employs symmetric encryption for efficiency and Attribute-Based Encryption for policy enforcement. The system supports efficient key distribution and isolates key storage to strengthen overall security. To demonstrate its applicability, we evaluate the system on visual datasets, where Privacy-Sensitive Objects in images are automatically detected, reassessed, and selectively encrypted prior to sharing in a data repository. Experimental results show that our system provides effective PSO detection, increases macro-averaged F1 score (5%) and mean Average Precision (10%), and maintains an average policy-enforced decryption time of less than 1 second per image. These results demonstrate the effectiveness, efficiency and scalability of our proposed solution for fine-grained access control.

Foundations

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