Brian Yuan (CS/ICC–CyberS, DataS) is principal investigator of a $500K National Science Foundation grant titled, “CNS Core: Small: Privacy-Preserving On-Device Intelligence in the IoT Era.” Lan Zhang (ECE/ICC–CPS) is co-PI. The project intends to develop privacy-preserving on-device intelligence in the IoT era.
Abstract: On-device artificial intelligence (AI) is playing an increasingly important role in the Internet-of-Things (IoT) era. As a key enabler, model compression can accommodate state-of-the-art AI models within limited IoT resources without significantly compromising prediction performance. Given the connectivity among IoT devices, on-device intelligence is expected to not only serve individual IoT users more quickly and safely, but also support the broader IoT community. However, the growing IoT market makes IoT devices a primary target of adversaries. Meanwhile, recent research has shown severe privacy threats of AI models. When IoT meets AI, on-device intelligence is more likely to be subject to multiple new attack vectors, with significant potential impacts. This project consists of three research thrusts to develop privacy-preserving on-device intelligence in the IoT era: (i) exploring new vulnerabilities and the corresponding countermeasures for private on-device inference with model compression; (ii) establishing a private collaborative inference framework to protect intermediate on-device representations with high prediction performance; and (iii) investigating uncertainty-aware knowledge aggregation with lightweight, on-device obfuscation to protect collaborative training privacy. The proposed research will be rigorously evaluated via simulation tools, a large-scale testbed, and real-world deployment.
The success of this project will bring tangible benefits to a range of domains in the IoT era, including healthcare, cyber-physical systems, transportation, and education. The proposed research will unlock the potential of intelligent IoT applications and services while preserving privacy, democratizing AI for a more diverse community. The programs, models, and testbeds developed in the project will be publicly released to boost community progress in this research area. Further, research findings will be integrated within existing courses, educational programs, and K-12 outreach activities to encourage students, especially underrepresented groups, to pursue STEM education and research.