基于特征解缠的保护隐私的运动意图分类

Jiahao Fan, Xiaogang Hu
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摘要

近年来的研究表明,在基于表面肌电信号的模式识别应用中,敏感属性和隐私属性可以被解码,这可能会给用户的隐私带来威胁。到目前为止,大多数研究都集中在提高表面肌电信号分类器的准确性和可靠性上,而对表面肌电信号分类器的隐私性关注较少。为了填补这一空白,本研究实施并评估了一个框架来优化基于表面肌电信号的数据共享机制。我们的主要目标是在与主要模式识别任务共享表面肌电信号特征之前,删除它们中的敏感属性(即身份相关信息)。我们从原始的表面肌电信号特征中分离出身份不敏感的、与任务相关的表征。我们将其与下游模式识别任务共享,以减少潜在攻击者推断敏感属性的机会。该方法采用20名受试者的数据进行评价,训练和测试数据间隔3-25天。我们的研究结果表明,与最先进的特征投影方法生成的原始特征及其稀疏表示相比,解纠缠表示显着降低了身份推理攻击的成功率。然后在手势识别任务中评估解纠缠表示。我们的研究结果表明,与其他特征实现相比,解纠缠表示在分类器上具有更高的分类精度。这项工作表明,表面肌电信号的解纠缠表示是一种很有前途的解决方案,可以用于保护隐私的运动意图识别应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy-Preserving Motor Intent Classification via Feature Disentanglement
Recent studies have revealed that sensitive and private attributes could be decoded from surface electromyogram (sEMG) signals, which can incur privacy threat to the users of sEMG-based pattern recognition applications. Most studies so far focus on improving the accuracy and reliability of sEMG classifiers, but much less attention has been paid to their privacy. To fill this gap, this study implemented and evaluated a framework to optimize sEMG-based data-sharing mechanism. Our primary goal was to remove sensitive attributes (i.e., identity-relevant information) in the sEMG features before sharing them with primary pattern recognition tasks. We disentangled the identity-insensitive, task-relevant representations from original sEMG features. We shared it with the downstream pattern recognition tasks to reduce the chance of sensitive attributes being inferred by potential attackers. The proposed method was evaluated on data from twenty subjects, with training and testing data acquired 3–25 days apart. Our results showed that the disentangled representations significantly reduced the success rate of identity inference attacks compared to the original feature and its sparse representations generated by the state-of-the-art feature projection methods. The disentangled representation was then evaluated in hand gesture recognition tasks. Our results revealed that the disentangled representations led to higher classification accuracy across classifiers compared with other feature implementations. This work shows that disentangled representations of sEMG signals are a promising solution for privacy-preserving motor intent recognition applications.
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