用AI精准识别图像敏感区域并分级加密，保障数据共享安全
From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data
- 自动检测图像中的敏感对象，分区域加密保护
- F1分数提升5%，平均精度提高10%，单图解密耗时<1秒
- 适合需要精细权限管理的医疗、金融等敏感数据场景
随着存储数据量持续增长，从大型数据仓库中识别并保护敏感信息变得愈发困难，尤其是在多用户角色和权限共享场景下。本文提出一种基于策略驱动访问控制的可信数据共享系统架构，支持对敏感区域的细粒度保护并保持可扩展性。该架构集成四个核心模块：敏感区域自动化检测、后处理修正、密钥管理与访问控制。敏感区域采用对称加密与属性基加密相结合的混合方案，兼顾效率与策略执行能力。系统支持高效密钥分发，并隔离密钥存储以增强整体安全性。通过在视觉数据集上的评估验证，系统能自动检测图像中的隐私敏感对象（Privacy-Sensitive Objects, PSO），进行重新评估并选择性加密后再存入数据仓库。实验结果表明，本系统实现了有效的PSO检测，宏平均F1分数提升5%，平均精度（mAP）提升10%，且平均每张图像策略强制解密时间低于1秒。这些结果证明了该方案在细粒度访问控制中的有效性、高效性与可扩展性。
原文摘要 · Abstract (English)
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.
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