Spring 2018 Big Data Seminar Series

Speaker: Prof. Yingying (Jennifer) Chen
Location: Galanti Lounge (third floor of the University Library)
Date/Time: Apr. 26 (Thursday), at 11:00 a.m.
Title: “ Toward Secure Mobile Health: Unobtrusive Wellbeing Monitoring and its Vulnerabilities”

Abstract

The widespread deployment of wireless communication systems and wearable technologies cre-ates unprecedented opportunities to change the paradigm of mobile healthcare in pervasive wireless environ-ments. We envision that people’s health could be monitored in an unobtrusive and continuous way through-out their daily lives. The large-scale heterogeneous sensing data can be mined and analyzed not only for un-derstanding of human behaviors or daily activities but also for facilitating disease diagnosis and prevention. We show that the signals collected from off-the-shelf WiFi can be exploited to track vital signs such as breathing rate and heart rate during sleep. Tracking human vital signs during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinical usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors. Our system re-uses existing WiFi networks and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. While pervasive wireless networks and wearable technologies facilitate the feasibility of mobile healthcare, they also present security risks with such devices. As an example we demonstrate a security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people access key-based security systems such as ATM machines, key-pad based door locks, etc. We show that a wearable device can be exploited to discriminate mm-level distances and directions of the user’s fine-grained hand movements, which enable attackers to reproduce the trajectories of the user’s hand and further to recover the secret key entries (e.g., ATM PIN sequences). Therefore, secure mobile health is a pressing need for realizing large-scale low-cost continuous health monitoring in our daily lives.

Biography

Yingying (Jennifer) Chen is a Professor of Electrical and Computer Engineering at Rutgers University and a member of the Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. Her research interests include smart healthcare, cyber security and privacy, Internet of Things, connected vehicles and mobile computing and sensing. She has published 2 books and over 110 journals and referred conference papers in these areas. Her background is a combination of Physics, Computer Science and Computer Engineering. Prior to joining Rutgers, she was a tenured profes-sor at Stevens Institute of Technology and had extensive industry experiences at Alcatel-Lucent (now as Nokia). She is the recipient of the NSF CAREER Award and Google Faculty Research Award. She also re-ceived NJ Inventors Hall of Fame Innovator Award. She is the recipient of the Best Paper Awards from IEEE SECON 2017, ACM AsiaCCCS 2016, IEEE CNS 2014 and ACM MobiCom 2011. She is the recipient of IEEE Region 1 Technological Innovation in Academic Award 2017; she also received the IEEE Outstanding Contribution Award from IEEE New Jersey Coast Section each year 2005 – 2009. Her research has been re-ported in numerous media outlets including MIT Technology Review, CNN, Fox News Channel, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving on the editorial boards of IEEE Transactions on Mobile Computing (IEEE TMC) and IEEE Transactions on Wireless Communications (IEEE TWireless).