  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.
