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

TL;DR

This paper introduces a scalable, policy-driven system architecture that automates detection and encryption of sensitive visual data regions, enabling secure, fine-grained access control in large data repositories.

Contribution

It presents a novel hybrid encryption scheme combined with automated sensitive region detection and key management for scalable visual data sharing.

Findings

Effective detection of privacy-sensitive objects with improved F1 score and mAP

Maintains decryption time under 1 second per image

Demonstrates scalability and security in visual data sharing systems

Abstract

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.