CAMEL: Cybersecurity, Autonomous system, and Machine learning Engineering Lab

Back to Projects

I/UCRC FRP: Collaborative Research: Enabling Biometrics Research in the Cloud--Design and Demonstration (NSF)

In this Fundamental Research proposal the PIs will explore the migration of biometric applications into a Cloud Computing environment. Using existing cloud hardware and software infrastructure at participating Universities, the PIs will design and demonstrate a multi-university, multi-domain, multi-dataset virtual biometric cloud - BioCloud. The PIs plan to analyze access control, privacy protection and data sharing policies and techniques. Selected solutions will then be used to develop the BioCloud prototype that will support Biometric Software (Identification) as a Service (B-SaaS), Biometric Data as a Service (B-DaaS), and Biometric Platform as a Service (B-PaaS).

In this proposed work, the PIs will develop the catalog of requirements, futures and constraints that must be addressed in identity management applications. Verifiable protection policies and techniques that guard personally identifiable information in pervasive next generation government and commercial identity management cloud-based applications are the prerequisite for the improved public trust, which in turn allows for sustainable protection of security and civil liberties. The project will further improve the industry/ university collaboration in the field of biometrics, thus increasing the chances of speedy transition of best research ideas into practice. Undergraduate and graduate students will be included in the research and prototyping. BioCloud will create a platform for innovative undergraduate and graduate school projects at the three participating universities. Special attention will be given to the recruitment of minorities and students from traditionally underrepresented groups in STEM fields.

Related Publications

An Auto-tuning Assisted Power-Aware Study of Iris Matching Algorithm on Intel's SCC (springer)
Gildo Torres, Chen Liu, Jed Kao-Tung Chang, Fang Hua, and Stephanie Schuckers
The Journal of Signal Processing Systems (JSPS), May 2014
DOI:10.1007/s11265-014-0901-4