Abstract: Reusable Iris Authentication
Biometrics are increasingly the preferred authentication modality on mobile platforms including phones, tablets, and wearables. Biometric authentication has two primary challenges are 1) biometrics exhibit noise between repeated readings, necessitating a matching algorithm that allows for error tolerance and 2) biometrics cannot be regenerated or refreshed. Due to the noise, biometrics are stored in plaintext, so device compromise completely reveals the user’s biometric value. We focus on the iris due to recognition as the best biometric (Prabhakar et al., S\&P 2003).
Benjamin Fuller is an Assistant Professor of Computer Science and Engineering at the University of Connecticut. His research focuses on driving cryptography to use in practice. His primary interests are authentication and searchable encryption. He has worked on a variety of problems from testing broadcast encryption while flying to scanning his iris for cryptographic key derivation. Prior to joining UConn, Ben was a research scientist at MIT Lincoln Laboratory from 2007-2016 working on searchable encryption. He received his PhD and MA from Boston University in 2015 and 2011 respectively.