Foster Multidisciplinary Approach

This project establishes a multimedia-based virtual classroom with a virtual lab teaching assistant for the education of cyber physical system (CPS) security. Such a virtual classroom helps college students in resource-limited rural areas to learn the latest CPS security knowledge via an on-line peer-to-peer learning environment with other students from larger schools.

Cloud computing allows users to delegate data and computation to cloud providers, at the cost of giving up physical control of their computing infrastructure. An attacker with physical access to the computing platform can perform various physical attacks, referred to as digital insertion and observation attacks, which include probing memory buses, tampering with memory, and cold-boot style attacks. While memory encryption can prevent direct leakage of data under digital observation, memory access patterns to even encrypted data may leak sensitive information.

The goal of our work is to (a) capture people's expectations and surprises in using mobile apps in a scalable manner, and to (b) summarize these perceptions in a simple format to help people make better trust decisions. Our main idea is analyzing privacy in the form of people's expectations about what an app will and won't do, focusing on where an app breaks people's expectations. We are building an App Scanner that combines automated scanning techniques with crowdsourcing.

Silicon physically unclonable function (PUF) is a supplemental circuit embedded in an IC which generates signatures unique to its native IC. This signature could be used for authentication, protection of data and secure communication. PUFs rely on the presence of uncontrollable variations in the fabrication process causing the circuit parameters to exhibit randomness. Current approaches for PUF design have mostly investigated circuit and architectural aspects. PUF quality is severely marred by a lack of understanding of exactly how fabrication process variations impact the PUF responses.

Many compelling applications involve computations that require sensitive data from two or more individuals. For example, as the cost of personal genome sequencing rapidly plummets many genetics applications will soon be within reach of individuals such as comparing one?s genome with the genomes of different groups of participants in a study to determine which treatment is likely to be most effective. Such comparisons could have tremendous value, but are currently infeasible because of the privacy concerns both for the individual and study participants.