Cloud-Assisted IoT Systems Privacy--2019Q1
PI(s), Co-PI(s), Researchers: Fengjun Li, Bo Luo
HARD PROBLEM(S) ADDRESSED
The goal of this project is to develop principles and methods to model privacy needs, threats, and protection mechanisms in cloud-assisted IoT systems. The work aims to address the hard problems of resilient architectures, security metrics as well as scalability and composability.
PUBLICATIONS
- N/A
PUBLIC ACCOMPLISHMENT HIGHLIGHTS
- Designed a privacy-preserving protocol for model training, as an extended scheme to support our privacy-preserving classification protocol.
In the cloud-assisted IoT scenario, IoT devices are continuously generating new data. When new data becomes available, the incremental learning scheme can integrate the new data into the quadratic program and modify the kernel and regularization parameters if necessary. This online, active and incremental batch learning capability is critical to cloud-assisted IoT application scenarios.
COMMUNITY ENGAGEMENTS
We continued our bi-weekly Privacy Study Group to explore privacy principles, concept models, and enforcement designs from perspectives from different disciplines. Regular attendees include PIs and graduate students of our lablet, students from computer science and philosophy departments of KU, and researchers from Kansas State University.
EDUCATIONAL ADVANCES
N/A