Lightweight Privacy-Preserving Deep Learning and Inference in Internet of Things
Date:
Abstract: With the rapid development of sensing and communication technologies, the Internet of Things (IoT) is becoming a global data generation infrastructure. To utilize the massive data generated by IoT for achieving better system intelligence, machine learning and inference on the IoT data at the edge and core (i.e., cloud) of the IoT are needed. However, the pervasive data collection and processing engender various privacy concerns. While various privacy preservation mechanisms have been proposed in the context of cloud computing, they may be ill suited for IoT due to the resource constraints at the IoT edge. In this talk, I will present four privacy-preserving approaches on the learning and inference phases. These four approaches are computationally lightweight and can be executed by resource-limited edge devices including smartphones and even mote-class sensor nodes. Extensive performance evaluation performed on multiple datasets and real implementations on IoT hardware platforms show the effectiveness and efficiency of these approaches in protecting data privacy while maintaining the learning and inference performance.