Abstract: Consumers today are increasingly concerned about the security and privacy risks of smart home IoT devices. However, few empirical studies have looked at these problems at scale, partly because a large variety and number of smart-home IoT devices are often closed-source and on private home networks, thus making it difficult for researchers to systematically observe the actual security and privacy issues faced by users in the wild.

In this talk, I describe two methods for researchers to empirically understand these risks to real end-users: (i) crowdsourcing network traffic from thousands of real smart home networks [IMWUT '20], and (ii) emulating user-inputs to study how thousands of smart TV apps track viewers [CCS '19]. Both methods have allowed us to conduct the largest security and privacy studies on smart TVs and other IoT devices to date. Our labeled datasets have also created new opportunities for other research areas, such as machine learning, network management, and healthcare.

Short Bio: Danny Y. Huang is an Assistant Professor at New York University. He is broadly interested in the security and privacy of consumer technologies, such as cryptocurrency and IoT. He did a postdoc at Princeton University and obtained his PhD in Computer Science and Engineering from University of California, San Diego. For more information, visit https://mlab.engineering.nyu.edu/.