解决扬声器匿名化方面的挑战,以保持实用性,同时确保病理性语音的隐私
Soroosh Tayebi Arasteh1,2,3, Tomás Arias-Vergara4, Paula Andrea Pérez-Toro4
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. soroosh.arasteh@fau.de.
Communications medicine
|September 25, 2024
概括
扬声器匿名化增强了病理性言语的隐私,对诊断实用性的影响最小. 针对不同语言障碍的定制策略至关重要,以平衡隐私和数据有用性.
科学领域:
- 语音处理 语音处理
- 生物医学信息学是生物医学信息学.
- 保护隐私的机器学习保护隐私
背景情况:
- 由于生物识别数据,医疗保健语音集成引发了隐私问题.
- 扬声器匿名化保护个人信息,同时保留语言内容.
- 尽管存在关键的隐私需求,病理性言语的匿名化仍未得到充分探索.
研究的目的:
- 调查演讲者匿名化对病态语音的影响.
- 评估隐私增强,诊断效用和人口统计学公平性.
- 探索深度学习和信号处理匿名化方法.
主要方法:
- 分析了来自多个德国机构的2700多名演讲者.
- 基于深度学习和基于信号处理的匿名化技术的评估.
- 评估隐私 (同等错误率增加) 和各种语言障碍的实用性.
主要成果:
- 观察到大量的隐私改善,等错率增加到1933%.
- 对诊断实用性的最小影响,用于诸如脱节障碍,失声症和唇裂等疾病.
- 不同疾病之间不同的匿名化效应需要特定于疾病的策略;在人口统计学中保持一致的公平性.
结论:
- 扬声器匿名化有效地提高了病态言语中的隐私.
- 定制,疾病特定的匿名化方法是必不可少的.
- 策略必须平衡隐私与诊断实用性,并解决潜在的反转攻击.
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