With the increasing popularity of Internet of Things (IoT) devices, security concerns have become a major challenge: confidential information is constantly being transmitted (sometimes inadvertently) from user devices to untrusted cloud services. This work proposes a design to enhance security and privacy in IoT based systems by isolating hardware peripheral drivers in a trusted execution environment (TEE), and leveraging secure machine learning classification techniques to filter out sensitive data, e.g., speech, images, etc. from the associated peripheral devices before it makes its way to an untrusted party in the cloud.

翻译：随着物联网（IoT）设备的日益普及，安全问题已成为一项重大挑战：机密信息不断从用户设备传输至（有时是无意中）不可信的云服务。本研究提出一种设计，通过将硬件外设驱动隔离在可信执行环境中，并利用安全的机器学习分类技术，在敏感数据（例如语音、图像等）从相关外设传输至云端不可信方之前对其进行过滤，从而增强基于物联网的系统的安全与隐私保护。