Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractical for multi-user scenarios. Moreover, these proposals all fail to exploit Wi-Fi's multiple-in multiple-out (MIMO) capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi sensing framework to support realistic multi-user scenarios. To thwart unauthorized eavesdropping while retaining the sensing and communication capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to physically encrypt Wi-Fi channels, treating the sensed human activities as physical plaintexts. The encryption scheme is further enhanced via an optimization framework, aiming to strike a balance among i) risk of eavesdropping, ii) sensing accuracy, and iii) communication quality, upon securely conveying decryption keys to legitimate users. We implement a prototype of MIMOCrypt on an SDR platform and perform extensive experiments to evaluate its effectiveness in common application scenarios, especially privacy-sensitive human gesture recognition.

翻译：Wi-Fi信号有助于实现低成本、非侵入式的人体感知，但同时也可能被窃听者利用以捕获隐私信息。迄今为止，鲜有研究着力应对这一隐私问题：它们要么干扰所有感知尝试，要么依赖复杂技术仅支持单一感知用户，因而在多用户场景下缺乏实用性。此外，这些方案均未能利用Wi-Fi的多输入多输出（MIMO）能力。为此，我们提出MIMOCrypt，一个支持现实多用户场景的隐私保护Wi-Fi感知框架。为阻止未授权窃听，同时保留合法用户的感知与通信能力，MIMOCrypt创新性地利用MIMO技术对Wi-Fi信道进行物理加密，将被感知的人体活动视为物理明文。该加密方案进一步通过优化框架增强，旨在安全地向合法用户传递解密密钥的同时，在以下三者间取得平衡：i）窃听风险、ii）感知精度、iii）通信质量。我们在SDR平台上实现了MIMOCrypt原型，并在常见应用场景（特别是隐私敏感的人体手势识别）中开展了大量实验，以评估其有效性。