Security and Privacy of AI-enabled IoT Eco-Systems



With the ubiquitous adoption of Internet-of-Things (IoTs) and wearable devices, an increasing amount of sensitive user information is being collected, analyzed, and relayed to remote locations for training machine learning and artificial intelligence (ML/AI) models. Some modern IoTs and edge devices (e.g., GPU-enabled embedded devices) have significant on-device computing that allows them to employ ML/AI models. Due to their high level of connectivity, maintaining the privacy and assuring security is challenging for wearable/IoT devices. In this new eco-system, IoTs and edge computing devices that are heterogeneous in processing/storage capabilities are leveraged to perform various tasks, from pure sensing and data aggregation to analysis, inferencing, and training deep neural network (DNN) models or running real-time DNN-based inferences. Some of these devices only have a local view, while others leverage information collected from other clusters of IoTs or central servers. This results in new attack surfaces that compromise security and user privacy across layers of the network protocol stack, hardware/software, and system level. Our team proposes a holistic, cross-layer approach to designing robust ML/AI systems for enhancing the security and privacy of wearable/IoTs. The aimed project, if successful, will have a societal impact by providing the much-needed security and privacy assurance to enable AIoT-based applications (such as smart health services) on edge devices.

Scientific Challenge

In the first phase of the project, we will address the following two challenges:

Faculty Participants

Noyce Fellows

Noyce Fellow Alumni

Progress Highlights

Publications, Patents, Other Reports


Project funded under UC Noyce Institute: Center for CyberSecurity and CyberIntegrity (C-CUBE)