Project Toward Prediction of A User's Identity from Missing IoT Biometric Data Program DUIRI - Discovery Undergraduate Interdisciplinary Research Internship Term Spring 2024 Status Rejected Research Area Global Security Description With the emergence of the internet of things (IoT), smart sensing devices ranging from smart wearables, such as Fitbits or Apple watches, to smartphones are packed with a range of sensors helping us with a range of services from unlocking cars and homes to validating financial transactions, among several other services. But, often these services are delivered based on a user’s sensitive personal information, including demographic identity, and various biometric data, ranging from heart rate to breathing patterns. Therefore, it is important to understand how missing biometric samples can be fatal to predict a user’s identity and his/her entire cyber-physical space. This project will utilize machine learning and data fusion techniques on wearable and smartphone data to predict a user’s identity to better understand possible risks and foster global security. Supervisor Sudip Vhaduri Mentor Sudip Vhaduri Type of work required Working with an interdisciplinary research team, in this project, students will first process various types of data, e.g., heart rate, gait, and breathing patterns, among several others obtained from smartphones and smart wearables. Then, students will visualize and compute different features. Finally, students will develop machine learning models to authenticate a user. During this project, student researchers will be closely guided in every step, including problem formulation, data processing and characterizations, statistical analysis and data visualization, machine learning model development, and interpretation of findings. Participating in this project, our student researchers will achieve technical expertise to solve real-world problems. The students will also get a clear idea of how their scientific discovery contributes to the entire community in terms of securing their cyber-physical space. Websites https://sudipvhaduri.wordpress.com/nethealth/ Qualifications Students should be in good academic standing. Students are expected to have some experience with machine learning modeling using Scikit learn, Tensorflow, or similar frameworks. Online Citi training/certification will be needed before starting the internship since this work involves human subjects. Credit Hours 0 Weekly Hours 10 (estimated)