在早期痴呆症识别中进行步态分析的混合保护方案
Francesco Castro1, Donato Impedovo1, Giuseppe Pirlo1
1Department of Computer Science, University of Bari Aldo Moro, 70125 Bari, Italy.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究引入了一种混合隐私保护方法,用于步态分析,以早期检测痴呆症. 该系统结合了加密和可取消的生物识别,保持高准确性,同时保护患者数据.
科学领域:
- 生物识别和人类活动识别
- 神经退行性疾病的诊断 神经退行性疾病的诊断
- 数据安全与隐私数据安全与隐私
背景情况:
- 步行异常是早期发现神经退行性疾病 (如痴呆症) 的关键指标.
- 生物识别数据,包括步行模式,在处理过程中需要强有力的隐私和安全措施.
- 现有的步态分析方法可能无法充分解决隐私问题.
研究的目的:
- 建议和评估一种创新的混合保护方案,用于步行特征.
- 确保早期痴呆症诊断的人类活动识别 (HAR) 系统中的患者隐私.
- 将保护方案与长期短期记忆 (LSTM) 神经网络集成为安全的诊断系统.
主要方法:
- 开发了一种混合保护方案,通过随机投影结合了部分同型加密和可取消生物识别.
- 实施了长期短期记忆 (LSTM) 神经网络的方案,以确保早期痴呆症诊断.
- 确保遵守诸如ISO/IEC 24745.5之类的隐私标准.
主要成果:
- 拟议的方案实现了数据安全和系统性能之间的高权衡.
- 与未受保护的系统相比,精度下降是最小的,最多为1.20%.
- 该保护方案展示了可扩展性和独立于特定神经网络架构的独立性.
结论:
- 混合保护方案有效地保护了步行特征,同时可以准确地早期发现痴呆症.
- 该系统为医疗保健中的HAR提供了一个可扩展和保护隐私的解决方案.
- 该方法为在敏感应用中安全处理生物识别数据提供了坚实的基础.
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