一个以用户为中心的上下文意识框架,用于实时优化多媒体数据隐私保护,以及在多式联络人工智能系统中的信息保留
1Department of Computer Science, University of Reading, Reading RG6 6AH, UK.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了一个AI隐私框架,它适应用户对个人信息实时保护的需求. 它平衡了隐私与可用性,为各种应用提供了符合GDPR的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 人工智能驱动的识别系统 (面部,物体,动作,场景,情绪) 构成隐私风险,特别是在个人身份信息 (PII) 方面.
- 现有的隐私方法缺乏用户适应性和上下文意识,经常使用统一的模糊化.
- 人工智能视觉系统需要动态,以用户为中心的隐私解决方案.
研究的目的:
- 为人工智能视觉系统提出以用户为中心,上下文意识,本体学驱动的隐私保护框架.
- 根据用户偏好,实体敏感性和上下文动态调整隐私决策.
- 为了提高隐私,同时保持场景的可理解性,并实现实时性能.
主要方法:
- 集成的最先进的识别模型与基于上下文完整性理论的上下文本体学.
- 通过遮蔽实现了自适应性隐私级别,并通过用户定义的"红线"实现了多层次隐私模型.
- 开发了一种使用软生物识别技术的重新识别指数 (RII),以缓解身份泄露和备用保护.
- 利用GPU加速进行实时处理,将时间从1200ms缩短到198ms.
主要成果:
- 用户研究显示,超过85.2%的人认为掩盖非常有效,14.8%的人认为足够有效.
- 71.4%的人认为隐私与实用性的平衡非常令人满意;28%的人认为这是令人满意的.
- 通过GPU加速实现实时性能,显著减少处理时间.
- 该框架证明了GDPR合规性,并通过设计实现了隐私.
结论:
- 拟议的本体学驱动框架为人工智能隐私保护提供了一种动态,以用户为中心和上下文为中心的方法.
- 它有效地平衡了隐私保护与场景可理解性,优于当前匿名化方法.
- 该框架适用于医疗保健,监控和社交媒体等现实世界应用,确保GDPR合规.
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