用压缩编码保护医疗传感器数据隐私，兼顾识别准确率
C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams
- 将匿名自编码器与自适应差分编码结合，双重屏蔽身份信息
- 用户重识别准确率降低10-15个百分点，活动识别精度损失小于5个百分点
- 数据量减少约75%，适合资源受限的医疗连续监测场景
可穿戴加速度计和陀螺仪能记录细微行为特征，易被用于用户重识别，因此在医疗应用中需保障隐私。本文提出C-AAE，一种融合匿名自编码器（AAE）与自适应差分脉冲编码调制（ADPCM）的压缩匿名自编码器。AAE首先将原始传感器窗口映射到保留活动相关特征但抑制身份线索的潜在空间；随后ADPCM对潜在流进行差分编码，进一步隐藏残留身份信息并降低码率。在MotionSense和PAMAP2数据集上的实验表明，相比仅使用AAE，C-AAE使用户重识别F1分数下降10-15个百分点，同时保持活动识别F1分数比未保护基线低不超过5个百分点。此外，ADPCM使数据量减少约75%，显著降低传输与存储开销。结果表明，C-AAE为医疗场景下连续传感器活动识别提供了隐私与效用平衡的实用方案。
原文摘要 · Abstract (English)
Wearable accelerometers and gyroscopes encode fine-grained behavioural signatures that can be exploited to re-identify users, making privacy protection essential for healthcare applications. We introduce C-AAE, a compressive anonymizing autoencoder that marries an Anonymizing AutoEncoder (AAE) with Adaptive Differential Pulse-Code Modulation (ADPCM). The AAE first projects raw sensor windows into a latent space that retains activity-relevant features while suppressing identity cues. ADPCM then differentially encodes this latent stream, further masking residual identity information and shrinking the bitrate. Experiments on the MotionSense and PAMAP2 datasets show that C-AAE cuts user re-identification F1 scores by 10-15 percentage points relative to AAE alone, while keeping activity-recognition F1 within 5 percentage points of the unprotected baseline. ADPCM also reduces data volume by roughly 75 %, easing transmission and storage overheads. These results demonstrate that C-AAE offers a practical route to balancing privacy and utility in continuous, sensor-based activity recognition for healthcare.
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